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A composite network of conserved and tissue specific gene interactions reveals possible genetic interactions in glioma

  • André Voigt,

    Roles Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Software, Validation, Visualization, Writing – original draft, Writing – review & editing

    Affiliation Network Systems Biology Group, Department of Biotechnology, NTNU - Norwegian University of Science and Technology, Trondheim, Norway

  • Katja Nowick ,

    Roles Conceptualization, Formal analysis, Funding acquisition, Investigation, Methodology, Writing – original draft, Writing – review & editing (EA); (KN)

    Affiliations Bioinformatics Group, Department of Computer Science, and Interdisciplinary Center for Bioinformatics, University of Leipzig, Leipzig, Germany, Bioinformatics, Institute of Animal Science, University of Hohenheim, Stuttgart, Germany, Human Biology, Institute for Biology, Free University Berlin, Berlin, Germany

  • Eivind Almaas

    Roles Conceptualization, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Supervision, Writing – original draft, Writing – review & editing (EA); (KN)

    Affiliations Network Systems Biology Group, Department of Biotechnology, NTNU - Norwegian University of Science and Technology, Trondheim, Norway, K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and General Practice, NTNU - Norwegian University of Science and Technology, Trondheim, Norway

A composite network of conserved and tissue specific gene interactions reveals possible genetic interactions in glioma

  • André Voigt, 
  • Katja Nowick, 
  • Eivind Almaas


Differential co-expression network analyses have recently become an important step in the investigation of cellular differentiation and dysfunctional gene-regulation in cell and tissue disease-states. The resulting networks have been analyzed to identify and understand pathways associated with disorders, or to infer molecular interactions. However, existing methods for differential co-expression network analysis are unable to distinguish between various forms of differential co-expression. To close this gap, here we define the three different kinds (conserved, specific, and differentiated) of differential co-expression and present a systematic framework, CSD, for differential co-expression network analysis that incorporates these interactions on an equal footing. In addition, our method includes a subsampling strategy to estimate the variance of co-expressions. Our framework is applicable to a wide variety of cases, such as the study of differential co-expression networks between healthy and disease states, before and after treatments, or between species. Applying the CSD approach to a published gene-expression data set of cerebral cortex and basal ganglia samples from healthy individuals, we find that the resulting CSD network is enriched in genes associated with cognitive function, signaling pathways involving compounds with well-known roles in the central nervous system, as well as certain neurological diseases. From the CSD analysis, we identify a set of prominent hubs of differential co-expression, whose neighborhood contains a substantial number of genes associated with glioblastoma. The resulting gene-sets identified by our CSD analysis also contain many genes that so far have not been recognized as having a role in glioblastoma, but are good candidates for further studies. CSD may thus aid in hypothesis-generation for functional disease-associations.

Author summary

With the ever increasing availability of large sets of gene expression data, much effort has been directed towards studying shared expression patterns between different genes. We have developed a general method for studying the variation of gene co-expression between two different conditions, which allows for a more detailed description and classification of interactions than previous methods. Applying our method to compare data from two different parts of the brain (the cortex and the basal ganglia), we find that it identifies genes known to be involved in key brain functions. Our analysis also identifies connections between a variety of genes previously known to be involved in the progression of glioma. Our method can also be applied in studies comparing between healthy and disease states, treatment and controls, among others.


How can genomic information that is the same in each cell of an individual be translated into a variety of cell and tissue types? It is clear that gene-regulatory mechanisms must play a leading role in differentiation processes. Transcription factors (TF) belong to the class of proteins that are able to regulate the expression of other genes. However, it is the combinatorial interactions of TFs at the promoter of a gene that determine if that gene is activated, repressed, or not regulated at all [1]. For instance during development, a tightly coordinated cascade of TFs is responsible for the activation and repression of genes that determine cell fate. The same is true for tissue specificity.

The ever-increasing availability of genome-scale microarray and sequencing data has led to the development of an array of methods to investigate cells and tissues at the systems level. One class of such methods, gene co-expression analyses, has found wide use by combining microarray studies with network theory [29]. Investigations using a variety of network methods have found that co-expression patterns often are correlated with biologically relevant processes, such as protein-protein interactions, regulatory cascades, and biological pathways [1016]. Because of the frequently observed relationship between co-expression and function, co-expression analyses have been used in a variety of applications. Examples include functional annotation of genes [17], identification of pathways associated with diseases, such as Alzheimer’s [18] and autism spectrum disorder [19], as well as inference of molecular interactions [20]. It should be noted, however, that co-expressed gene pairs do not necessarily reflect direct biological interactions: Even direct transcriptional relationships may simply be the result of accidentally matched DNA motifs without any particular function [5]. In order to facilitate the study of gene co-expression, a variety of computational tools, notably WGCNA ([21]), have been made publicly available for general use.

A more recent development is the study of differential co-expression networks, which seeks to identify condition-specific co-expression patterns often associated with dysfunctional regulation [22, 23]. Many methods have been developed to generate such networks based on the implementation of different principles. In broad terms, the differential co-expression network methods can be divided into two groups. In the first group, the approaches typically generate co-expression networks that are specific to each condition studied [2426]. Here, genes are connected by links if their co-expression score fulfills a set of statistical criteria for significance. It is a fairly straightforward matter to compare the resulting networks and subsequently, to extract interactions that are present in only one of the conditions or to identify genes subject to extensive rewiring.

The second group of methods is instead focused on assigning a score for each possible gene pair, after which the score is used as input in a process to determine whether there is a significant change in co-expression between the (possibly multiple) conditions. These scores may be as mathematically simple as the difference between a gene-pair’s Pearson or Spearman correlation over the conditions [27], or it may include additional steps to normalize the data [2830]. Some of these methods determine group-wise co-expression by use of e.g. hierarchical clustering on correlation matrices [31] or decomposition of dependencies into global and group-specific components [32].

As the term “differential” suggests, the aim of both groups of methods is to identify differences in collective co-expression patterns in order to elucidate processes specifically relevant to a given condition. One example application of differential co-expression analysis is to identify target genes for treatment of a particular disorder (e.g. a specific type of cancer) by identifying genetic interactions potentially linked to harmful outcomes [29]. These interactions, or some of the interacting genes, may be disabled through appropriate means. However, should any of those genes also be involved in processes that are important under normal conditions, the suggested approach might run the risk of incidentally harming healthy cells and tissues. Consequently, the identification of genes that are potential disease-targets should be refined in such a way that it is possible to determine both conserved and differential co-expression in order to get a more comprehensive understanding of involved mechanisms.

While the various methods differ in the measures by which they identify differential co-expression, there is also considerable variety in what sort of data they produce. Some methods only seek to identify prominent differentially co-expressed genes, without considering the genes with which they are connected [26, 33]. Of the methods that seek to identify inter-gene relationships, some primarily focus on identifying communities or modules of genes that are collectively closely connected [28, 30, 34], while others provide a more general network of differentially co-expressed genes. The latter methods can be divided into unweighted networks [25] (also known as hard thresholding), in which all links are considered equal as long as they fulfill given criteria, or weighted [33] (also known as soft thresholding), where links are given a numerical score quantifying their prominence. These edge weights typically represent the magnitude of change in correlation between conditions. Unweighted networks can readily be converted into weighted networks by determining a specific cut-off value for the edge weight, and uniformly setting the weight of all edges above this cut-off to 1, while the remaining edge weights are set to 0. It has been proposed that gene co-expression networks should follow scale-free topologies [35], but this requirement is not universally imposed.

Just as the various approaches to differential gene co-expression differ in how co-expression is determined, there are fundamentally distinct types of differential co-expression to consider. While some methods characterize differential co-expression by correlations exclusive to a given condition and others by net changes in pairwise correlations, it is important to acknowledge that these two scenarios are not entirely interchangeable. To illustrate this point, we consider the following example: Assume that a pair of genes exhibit positively correlated expression under condition α and uncorrelated expression under condition β. In this case, both classes of approaches should, in principle, be able to identify a differential co-expression.

If we instead consider a pair of genes whose expression is positively correlated under condition α and negatively correlated under condition β, the two classes of methods will return different results: Methods based on net change in pairwise correlations should readily identify this gene pair as differentially co-expressed, and find the pair to be even more strongly linked than in the first example since the net change now may be larger. In contrast, methods based on determining differential co-expression by comparing unsigned co-expression networks from individual conditions [25, 26] would not find the pair of genes to be differentially co-expressed, provided that the absolute values of the correlations are not too dissimilar. However, current “net change” methods will not be able to qualitatively distinguish between the case of positive correlation under condition α and no correlation under condition β, and the case of positive correlation under condition α and negative correlation under condition β, even though they are fundamentally different in the type of genetic correlation. In the first case, differential co-expression might suggest concerted action between the genes under condition α and independent operation under condition β. In the second case, differential co-expression suggests interactions under both conditions, but with possibly different mechanisms in play.

Here, we will make a distinction between the two forms of differential co-expression in order to clearly distinguish between these two qualitatively different cases: Specific co-expression, which we will denote S, in which a gene pair is correlated under only one condition. This corresponds to the first example. Differentiated co-expression, denoted D, in which a gene pair is correlated in both tissues, but with opposing signs: In one condition, the correlation is positive and in the other condition it is negative. This corresponds to the second example. We propose a differential co-expression framework that allows the simultaneous determination and quantification of both conserved co-expressions, C, and the S- and D-types of differential co-expression patterns. Thus, a gene pair that is significantly co-expressed in one tissue may either be similarly co-expressed (C), co-expressed but with an opposite sign (D), or not show any significant co-expression when studied in another tissue (S).

In order to provide a more complete framework for differential co-expression analysis, we have developed an approach, called CSD (“Conserved, Specific, Differentiated”), to categorize gene pairs according to mathematically defined scores which will allow us to construct a unified differential co-expression network from experimental data. A systematic comparison between this new method and pre-existing methods is presented under Materials and Methods. We apply the method using two types of tissues: cerebral cortex and basal ganglia from a published data set containing samples from a large number of human individuals [36]. From this network, we identify potential key sets of interactions and groups of genes which may help explain functional differences across these two tissues.

Materials and methods

Gene expression data set

We have used the differential co-expression profiles in the GTEx V4 data set [36], focusing on tissue groups that (1) exhibit a high degree of similarity (as established by Pierson et al. [37]), and (2) for which the GTEx data set contains a sufficiently large number of samples.

Our data set 1 named cortex, consists of the GTEx groups: Brain—Cortex, Brain—Anterior cingulate cortex (BA24), and Brain—Frontal cortex (BA9). Our data set 2 named basal ganglia consists of the GTEx tissue types: Brain—Caudate (basal ganglia), Brain—Nucleus accumbens (basal ganglia), and Brain—Putamen (basal ganglia). These groupings add up to 73 data points for cortex, and 92 data points for the basal ganglia.

The GTEx V4 data set contains expression data for a total 55,993 loci. We have restricted our analysis to protein-coding open reading frames (as annotated in Ensembl), leaving a set of 18,453 genes (see S4 Text).

Estimated variance in gene co-expression

We calculate the pairwise gene co-expression scores ρij,k as Spearman rank-correlations for the pair of genes i and j in tissue k over all the gene expression data points N. As our analysis bases itself on identifying changes in ρij,k between conditions, we need to determine the extent of the variability of ρij,k within a condition due to confounding factors. While methods exist to evaluate the variance of computed Spearman correlations, we need to account for the fact that specific confounding factors may change the “actual” correlation within specific subpopulations of samples in a given condition.

To illustrate the problem, consider a hypothetical gene-pair shown to exhibit moderate co-expression in a specific type of tissue across a whole population. Upon detailed review of the data, it turns out that this gene-pair displays very high co-expression for some particular subgroups of the population (for instance, certain age groups, or in individuals suffering from certain diseases). At the same time, this pair is not showing a strong co-expression outside of these subgroups. In this hypothetical case, it would be difficult to tell if an observed difference in co-expression in another tissue and another population relates to differences between the tissues, or is due to confounding factors.

On the other hand, if the genes are consistently (but not particularly strongly) co-expressed across all possible groupings of individuals, we can say with greater confidence that the correlation reflects genuine control related to the condition (in this case, a tissue type). Consequently, we may attribute any difference of co-expression (if similarly consistent) in another population and another condition to the differences between the conditions.

In order to determine the variance in co-expression within each given tissue, we compute the Spearman rank-correlation for each independent sub-sample l of size n drawn from the N data points. We use the standard error of the mean, σij,k calculated from the set of values, as a measure of intra-tissue co-expression variation. In order to achieve as many sub-samples as possible, increasing the chance of matching with particular confounding conditions, and accurately determine σij,k while ensuring independence between the different sub-samples, we implement the following approach for selecting sub-samples:

  1. The N data points (per gene) for the full sample are ordered and sequentially numbered.
  2. The N data points are divided into sub-samples of size n. For instance, if N = 100 data points with a chosen sub-sample size n = 8, we initially create 12 sub-samples of size n, consisting of the data points 1-8, 9-16, 17-24 etc.
  3. Beginning with data point N = 1 as initiating data point n*, we sequentially iterate through the data points, adding to the current sub-sample any data point that has not previously co-occurred in a sub-sample with any of the points already in the current sub-sample.
  4. When the size of the current sub-sample reaches n, we re-initiate a new sub-sample with initiating data point n* and repeat step 3.
  5. When no valid sub-sample of size n can be drawn with n* as the initiating data point, we choose n* = n* + 1 as the next initiating data point and repeat from step 3.
  6. The approach is completed when n* = N and no more allowed sub-samples of size n can be constructed.

An example of the application of this algorithm is presented in S1 Text. When implementing this algorithm, we are ensured that two data points only co-occur once in a sub-sample. Note that, it is quite beneficial to select a sub-sample size n such that n2 = N, as this greatly increases the amount of possible sub-samples that will be generated. On the other hand, a small sub-sample size n makes for a coarse Spearman correlations, i.e. to achieve three-digit accuracy, a sub-sample size of 7 is recommended. Consequently, N = 49 data points is a recommended minimum in order to determine the standard error of the correlation ρij,k within a given tissue (or more generally, a given condition) within reasonable accuracy. We recognize that for certain conditions (such as those involving rare diseases or large animals which may be difficult to acquire and maintain) this requirement might not be realistically fulfilled. In this case, subsampling may be omitted, and σij,k set to 0 for that condition (or 1, if subsampling isn’t possible for either condition).

Additionally, while sub-sampling may be omitted in the event of few available data points, there would still need to be enough data points available to accurately determine the base correlation for the set. In order to determine a reasonable minimum, one should keep in mind that for two factually uncorrelated random sequences of N data points, there is still a likelihood 1/N! that the computed Spearman correlation is 1. As real-life gene expression data frequently involve thousands of genes, and thus millions of gene pairs, a small N can thus lead to a substantial number of false positives, no matter how stringent the required Spearman correlation. For instance, if we have a data set containing 1000 genes, and 8 expression values for each, we would expect at least 103(103 − 1)/(2 ⋅ 8!) ≈ 12 perfectly correlated gene pairs by pure chance in addition to any factually correlated gene pairs. While the confidence with which any perfect (or near perfect) correlation can be said to be biologically relevant depends on the total number of observed correlations, users should be aware of these caveats when trying to draw conclusions based on small sample sizes. Additionally, as gene expression is an inherently stochastic process, studies involving small sample sizes are susceptible to a variety of concerns with regards to noise and the resulting uncertainty in observations. Several of these are discussed in further depth in S2 Text. Based on our analysis presented in S2 Text, we hold that for comparisons on the order of thousands of genes, N = 49 data points remains a reasonably safe minimum requirement.

Next, we remark upon the fact that ρij,k and its sub-sample variation σij,k are dependent: As a general rule, gene pairs with absolute correlations close to unity tend to exhibit smaller variation in co-expression between sub-samples. We present two possible explanations for this, one biological and one mathematical: If a large correlation for a pair of genes is important to cellular function, both gene products should be consistently present in nearly the same ratio in all of the sub-samples, e.g. through a process of tight gene regulation. Consequently, the observed variation in the co-expression pattern will be small. Strong correlations should therefore be more frequently associated with low variation between samples.

The mathematical explanation is based on the following observation: A large sub-sample variation σij,k means that the sub-sample averages must follow a broad distribution. However, it is impossible for sub-sample averages to be larger than unity. Thus, when ρij,k is near unity, variation is limited in the sub-sample averages .

Finally, an important thing to keep in mind is that as the Spearman rank correlation is less accurate for small sample sizes, a choice of larger subsample size will generally bring the subsample correlations closer to the full-sample correlation. Consequently, σij,k will generally be lower for higher subsample sizes. Because of this, it is important that the chosen sub-sample size is the same for both conditions studied in order to avoid an unbalanced contribution from one of the conditions to the denominator of Eqs 13.

Gene relationship scores

In order to enable a systematic comparison of co-expressions, we introduce three pair-wise comparative gene co-expression scores which are computed for each pair of genes i and j in two different tissues. In general, these expressions may be applied to sets of data points from two different tissues, conditions or organisms: (1) (2) (3) Here, Cij quantifies the extent to which co-expressions for genes i and j are conserved, i.e. similar in both tissues. Sij quantifies specific correlations: gene pairs which are strongly (positively or negatively) correlated in one tissue while showing no noticeable correlation in the other. Finally, Dij describes the extent to which co-expressions are differentiated: Dij is large for pairs of genes showing strong absolute correlations in both tissues or conditions, but where the nature of this correlation (positive or negative) changes between the two tissues. As the numerators for each of the expressions Cij, Sij and Dij are necessarily positive (being absolute values), and the denominator being a positive number potentially arbitrarily close to 0, Cij, Sij and Dij may assume any value from 0 (included) to infinity. However, as they follow widely different distributions, the three scores are not directly comparable within each other. In order to integrate the three types of co-expression into a common network, further steps are necessary in order to determine appropriate cut-off thresholds. We describe these in detail in the next section.

Fig 1 provides a schematic visual representation of the three co-expression patterns detected by our method. Our scores are designed in such a way that they assume large values within their respective areas (for instance, C-scores are large within the blue areas), while remaining small outside. Increasing the cut-off value for a given score is equivalent to shrinking the corresponding area of interest (restricting it near the corners for C and D, and along the middle of the edges for S). Since these areas converge on different points as the cut-off increases, a given gene pair may not exhibit large values for more than one score. Consequently, we can choose cut-off values for each score in order to uniquely classify relevant gene pairs according to the appropriate categories.

Fig 1. Gene co-expression score surfaces.

General representation of the regions of interest for differential co-expression relationship scores C, S, and D. Here, ρ1 and ρ2 denote the Spearman rank-correlation of the expression of a given gene pair under condition 1 and condition 2, respectively. Colored regions correspond the three kinds of co-expression: blue is conserved C (strong co-expression in both conditions, no sign change), green is specific S (strong co-expression under only one condition), red is differentiated D (strong co-expression in both conditions, but with opposite signs). The colored letters indicate the scores associated with each colored region.

Consolidated comparative gene co-expression network

In order to generate a network for the combined gene co-expression categories, the different ranges of the scores Cij, Sij and Dij necessitate a systematic approach for combining these interaction measures. For each of the three co-expression score types, we wish to determine suitable threshold values in such a way that these values correspond to the same importance level p. Thus, we will keep all gene co-expression scores , and discard those below this threshold. Similarly for Sij and Dij with their respective cut-offs. However, the three different interaction scores show distributions that have noticeably different means, medians, variances and general shapes (see S1 Fig). Consequently, determining whether pairs exhibit significant change or conservation based on either a common fixed-value cut-off or a given distance from the three means is incompatible with a meaningful comparison of significance across categories.

Instead, we determined the importance value of a random variable X based on the likelihood of obtaining said value from the underlying distribution: If the distribution is based on M data points (in our case, the number of different gene-pairs), we draw m samples si from the data set, and each si has the size LM. We determine the threshold value Xp as the average of the maximal values per sample: (4) The associated importance level is determined as p = 1/L. Thus, by choosing a common p for the gene relationship scores, we obtain a set of consistent cut-off values which we use to extract separate C-, S- and D-links that are combined in a final network. It should be noted that this p is not a significance threshold, as it is determined by the distribution of the scores for a given data set, not by testing the data against a null hypothesis. Instead, its purpose is to map the scores Cij, Sij and Dij on to a common scale, to allow for meaningful comparison between them.

Measurement of node homogeneity

In the consolidated network, nodes may connect to their neighbors by either C, S or D link-type. In order to distinguish between nodes predominantly involved in one type of interaction and those with multiple different types of connections, we introduce the concept of node homogeneity H: (5) using an expression introduced in a different context [38]. Here, kC,i, kS,i and kD,i denote node i’s number of C, S and D-type interactions, respectively, and ki is the nodes degree (total number of connections). We note that in the extreme cases, H = 1 indicates a node with only one type of connections, while H = 1/3 (the lowest possible value) indicates a node with an even distribution of C, S and D-type connections.

Disease association and KEGG pathway enrichment analysis

In order to determine enrichment of specific OMIM disease terms and KEGG pathways in our networks, we used Enrichr [39, 40] ( to obtain associated terms for each gene in the GTEx data set. This was a necessary step to establish accurate enrichment values, as Enrichr in itself does not provide for user-specified background gene lists. We then performed (Bonferroni-corrected) hypergeometric tests using NumPy (Python) for each of the 90 disease terms and 293 pathways listed in the Enrichr library to determine the significance of the number of associated genes in a given network.


In order to perform our analysis, we developed a set of software in-house, which has been made publicly available for download ( The code for computing Spearman correlations and variance was written in C++, with the remainder (computation of C, S, and D, estimation of cut-off, and network generation) implemented in Python. We used Cytoscape [41] to visualize the network, and NetworkX [42] to perform network analyses. External software used for the remaining analysis is listed in S3 Text.

Runtime, from expression data to the finished network may take anywhere from a few minutes to several hours for realistic data sets (scaling approximately quadratically with both the number of genes and the number of data points.). As an example, for a data set consisting of 1550 genes and 100 sample points per gene, complete run time is approximately 45 minutes on an Intel Xenon X5690 CPU.


Comparison with existing differential co-expression methods

Table 1 provides a qualitative comparison between CSD and nine previously published methods for differential co-expression analysis that span a variety of method implementations. The defining characteristic of CSD is the classification of two types of differential co-expression (S-type; the loss of co-expression in one condition, and D-type; sign change) as well as the integration of conserved co-expression links in a composite network. Of the other listed methods, only the DCe method [33] recognizes S-type and D-type links as distinct forms of differential co-expression: it uses two different measures to determine the significance of co-expression change, one for same sign in both conditions and another when the sign changes between conditions. However, the DCe method does not distinguish between the two link-types in the final network analysis.

Table 1. Summary overview and characterization of differential co-expression methods.

We characterize the presented methods by 5 tests: 1. Detects loss of co-expression. 2. Detects sign change. 3. Differentiates loss of co-expression from sign change. 4. Differentiates sign change and conservation. 5. Integrates conserved co-expression.

The other listed methods either do not recognize the difference between D- and S-type links, or altogether omit D-type links from their differential co-expression analysis. Note that the CSD method explicitly includes conserved interactions in its resulting network, whereas C-type links are not included in the DCe method [33].

Validation on simulated gene expression data

In order to critically validate our method’s ability to detect regulatory changes, we applied it to synthetic gene-expression data-sets with known, pre-defined regulatory interactions. As an initial test, we used a published synthetic gene data set from Zhang et. al. [43]. This has previously been used as a benchmark for relevant methods [27, 34]. This data set consists of 20 genes, and the two conditions are defined by changing 10 of the interactions, i.e. specifying 10 differentially expressed interactions. In particular, 5 of the interactions are present only in condition 1, and 5 (other) links are present only in condition 2. We recognize all of these 10 links as S-type links within our framework, and the specified network contains no D-type links. In S1 Table, we provide detailed results of our analysis, listing the 10 top-scoring gene pairs for C-type and S-type links, as well as the top 10 S-equivalent links found by DCGL along with a classification scheme for each pair. The relevant categories in this classification, based on their connections in the reference regulatory network, are as follows: direct C (immediate neighbors in both conditions), direct S (immediate neighbors in one condition only), indirect C (connected through one intermediary link in both condition), indirect S (connected through intermediary links specific to one condition). While other interaction schemes are possible, they do not occur among the top 10 links in either test.

Using our CSD-method to compute S-scores for all gene pairs, we find that the 10 direct differential interactions (DDIs) are assigned to 9 of the 10 top scores, with an indirect link [SWI4_SWI6, CLB6] incorrectly assigned the 8th place. The only DDI not making it into the top 10 is the [CLB6, MBP1_SWI6], scoring at 11th place. In contrast, the DCe method identifies 6 of the 10 DDIs amongst its top 10 scoring S-equivalent links, and with one non-differentially co-expressed link at the 10th place. DCe does identify 2 of the 10 DDIs ([MPB1_SWI5, CLB6] and [MBP1_SWI6, CLB6]) as “switched opposites” (equivalent to D-type in our terminology), but the last two selected links ([PHO2, CLB5] and [PHO2, CLB6]) are not identified as differentially co-expressed under standard parameters.

Similar testing of our method focusing on the conserved links identifies 4 of the direct conserved links among the top 10 C-scores, with the remainder consisting of genes separated by a single intermediary gene. However, we note that since correlations are transitive, it is entirely within reason that gene pairs indirectly linked in the regulatory network (but with strong co-expression along the intermediate steps) also show strong co-expression.

As this data set is unsuitable for thorough vetting of the CSD method due to the lack of D-type differential interactions, we used GeneNetWeaver [44, 45] to generate synthetic gene-expression data from networks containing both conserved, specific and differentiated links. Starting with a general regulatory reconstruction of E. coli (containing 1565 genes and 3758 edges) [44, 45], we randomly modified 10% of the interactions (5% removed, 5% switched from activator to repressor, or vice versa). We generated 200 synthetic gene-expression data-samples for both the original and the modified network. This process was repeated 20 times (generating new randomized networks and new synthetic expression data with 200 samples for both conditions each time), yielding 20 distinct sets of C, S and D-scores.

To assess the quality of our method’s ranking of links, we tested true-positive and false-positive rates for C, S and D-type interactions by comparing the ranked lists of C, S, and D-type scores for the links in the known test networks. We quantify the quality by receiver operating characteristics (ROC) curves. We also calculated ROC curves for the DCe method on the same networks and with the same synthetic gene-expression data as input. We used the DCGL package as a benchmark, as it is, to our knowledge, the only published method that is able to distinguish between S- and D-type links (and also demonstrates good performance in comparison to many existing methods [27]).

Fig 2 shows the comparative ROC curves for the CSD and DCe methods. We find that on these data sets, CSD is substantially better at detecting D-type co-expression. However, despite the S-score’s success in identifying differentially expressed genes in the Zhang data set [43], it shows weaker performance than DCe on data generated in GeneNetWeaver [44, 45]. While we do not have any relevant method for which we can compare the performance of the C-score, we note that it’s general predictive power is higher than that of any of the other metrics.

Fig 2. Receiver operating characteristic (ROC) curves for differential gene co-expression scores.

ROC curves for the C, S and D-scores, and equivalent scores in the DCe method averaged over 20 independent simulations. The dashed black diagonal corresponds to Sensitivity = 1 − Specificity. Note that the DCe curves do not extend across the whole range, since DCe classifies genes detected as differentially co-expressed into either S-equivalent or D-equivalent, depending on sign change in the underlying correlation. Since a gene pair may only belong to one category in DCe, it is not possible to relax test requirements in such a way that one category contains all gene pairs. Notably, even under the most inclusive test requirements, the D-equivalent category can only contain on average ≈ 20% of gene pairs that show differently signed correlations between the two conditions.

The DCe method classifies genes detected as differentially co-expressed as either S-equivalent or D-equivalent, depending on sign change in the underlying correlation, with the consequence that DCe curves do not extend across the whole range in Fig 2: Since a gene pair may only belong to one category in DCe, it is not possible to relax test requirements in such a way that one category contains all gene pairs. Notably, even under the most inclusive test requirements, the D-equivalent category can only contain on average ≈ 20% of gene pairs that show differently signed correlations between the two conditions.

We observe that in general, the tested differential co-expression analysis methods do have substantial difficulties in accurately detecting individual regulatory perturbations for these types of network, and that even the best-performing measures in Fig 2 have a great deal of theoretical room for improvement in performance. This is generally the case for the existing methods [27]. A natural explanation for the difficulties in detecting individual changes lies in the fact that, empirical regulatory systems form complex networks, in which a given gene may be subjected to a multitude of regulatory impulses. Many of these input signals are shared with other genes, and regulatory cascades are common. Consequently, the loss of a regulatory interaction may not lead to a discernible change in co-expression correlations if these genes remain connected to shared regulators. On the other hand, an observed change in correlation between two genes may be the result of changes in regulatory mechanisms between intermediary genes in the regulatory network.

Differential gene co-expression network in brain

We selected the expression data from cortex and basal ganglia from the GTEx dataset to generate a CSD network. Using an importance level of p = 10−5 on the 18453 expressed genes, we obtained a network consisting of 1814 nodes (genes) and 2351 edges (Fig 3). Here, transcription-factor genes are indicated by triangle-node symbol. The network contains an even mix of edge types (767 are C-type, 806 are S-type, and 778 are D-type). A link to detailed data files describing the network can be found in S4 Text.

Fig 3. Overview of the CSD network.

Visualization of the aggregate CSD-type network generated using a sample size of L = 105. Triangular nodes indicate transcription factors. Prominent hubs (nodes with more than 40 neighbors) are colored black, enlarged and labeled for emphasis. Edges are colored by type: blue is C-type, green is S-type, red is D-type.

Fig 3 shows that the majority of the network is interconnected, forming a giant component consisting of 1333 (73.8%) nodes and 2024 (86.1%) edges. In addition to the giant component, we find 3 intermediately-sized connected components (respectively containing: 38 nodes and 47 edges, 30 nodes and 41 edges, 13 nodes and 13 edges). The remaining nodes form smaller connected components: 2 components of 5 nodes and 4 edges, 8 of 4 nodes and 3 edges, 28 triplets (all with 2 edges), and 137 isolated pairs. In Fig 3, we have highlighted the names and positions of the six genes with most connections in the networks. The top-3 list of most connected nodes consists of FOXO1 (k = 240 connections), ATP11C (k = 130 connections), and CARHSP1 (k = 120 connections), with a significant drop in connectivity to the fourth-most connected nodes (PBX3, k = 48).

A quick look at Fig 3 provides important insight concerning a key aspect of the network. It could be argued that, if conserved and differentiated interactions were functionally decoupled, and thus belonged to entirely separate parts of the genetic network, an integrated approach might not be particularly necessary, or even useful. In contrast, we find a highly interconnected network, with core regions densely interconnected by all three types of interactions. However, while the different interaction classes do not form separate networks, there is a distinct tendency for links with the same score type (either being C, S or D) to group together.

We investigated the propensity of nodes to be connected with links of different types by calculating the homogeneity-score H (Eq (5)) for each node. Fig 4(a) shows a box-plot of H as function of degree. Of the 1333 nodes in the giant component, 333 (just short of half of the 701 nodes with at least 2 neighbors) have interactions of at least two of the three different types, and 56 (approximately 1 in 8 of the 404 nodes with at least 3 neighbors) have interactions of all three types (Fig 4(c)). Interestingly, Fig 4(a) suggests that highly connected genes are dominated by specific types of interactions, as shown in Table 2. Here, of the top 5 hubs for each category, all but one of the C-hubs and one of the D-hubs have homogeneity scores over 0.9. On the other end of the degree distribution, we note that nodes with very few (less than 4) neighbors also tend to have somewhat more homogeneous neighborhoods than nodes with intermediate connectivity.

Fig 4. Node homogeneity and mixing of interactions.

a) Box plot of gene homogeneity scores H according to node degree. Red bars denote the median H for nodes of the specified degree, and red squares denote the mean. Bottom and top ends of the boxes represent the first and third quartiles, respectively. The end of the whiskers correspond to min/max values of H at that degree. b) Ternary heatmap, detailing the fractions of specified interactions kj,i/ki with j ∈ {C, S, D} per gene: Corners correspond to homogeneous nodes, i.e. nodes with only one type of interaction. The sides correspond to nodes with two types of interactions (scale is fraction × 10), e.g. kC = 0 along the side marked D. The blue cross is an aid, with coordinates (C ∼ 60%, S ∼ 30%, D ∼ 10%. c) Venn diagram showing the relative quantities of genes involved in each type of interaction.

Table 2. Network hubs for each type of interaction.

k denotes node degree (total number of connections), while kC, kS and kD denote the number of connections of each type (kC + kS + kD = k). H denotes node homogeneity, as defined in Eq 5.

Obviously, for nodes with only one neighbor, H = 1, while for nodes with only two neighbors, H must be equal to either 1 or 0.5 (whereas the lower bound on H for k ≥ 3 is 1/3).

If we classify any gene with 3 ≤ k ≤ 10 as an intermediate gene, and genes with k > 10 as a hub, we find that intermediate genes are significantly less homogeneous than hubs (t-test, p = 3.8 ⋅ 10−5). Looking over the combined set of intermediate and hub genes, higher degree nodes are positively correlated with increased homogeneity (Spearman ρ = 0.082, p = 0.085; for all genes with k ≥ 4, ρ = 0.2, p = 8.2 ⋅ 10−4).

How are the different link-types spread among the nodes? Fig 4(b) is a ternary heatmap showing a histogram of the three fractions kj,i/ki, with j ∈ {C, S, D} for each node. Consequently, entries at the corners account for all nodes with H = 1 (see panel (a)), whereas entries at either of the sides correspond to the nodes of Fig 3 connected with only two kinds of links. For entries in the interior, the corresponding nodes are connected to all three kinds of links. For a given point on the triangle, the corresponding proportion of interactions of type X is determined by following the line parallel to the base X = 0 until reaching the base labeled X. To illustrate this, we have provided an example by marking the tile corresponding to a mix of approximately 60% C, 30% S and 10% D (density of 19 nodes) by a blue cross. As the densities at the corners (representing nodes containing only one type of interaction) are far higher than anywhere else, the color scale has been truncated at the highest non-corner value (0% C, 50% D, 50% S, 64 nodes). Whereas the Venn-diagram (Fig 4(c)) details the number of nodes with a given mixture of links, the ternary heatmap shows how the links are mixed at the nodes. Panel (b) shows that the majority of the nodes connected to the three link types are dominated by C-specific (fractions above 0.6), and some S-specific (near 0.3), but only with a few D-specific interactions (fraction near 0.1).

Robustness and features of interaction network to choice of kp

We evaluated the consequence of different cut-off values kp for the structure of interaction specific networks by generating separate C-, S- and D-networks for a range of importance values p ∈ [10−6, 10−4]. For each interaction type network, we calculated their degree distribution, degree assortativity and max k-core number, and identified the top-10 most connected genes in each network. Fig 5 shows that while the C-networks exhibit greater positive degree assortativity than randomized networks with the same degree distribution, S and D networks are disassortative with respect to degree. We also find that the C-type network exhibits a higher maximum k-core value than randomized networks at the same degree distribution, while S- and D generally exhibit lower maximum k-cores (S6 Fig). Both of these traits indicate that C-networks are dominated by reasonably densely interconnected sets of genes of the same type (with highly connected genes generally connecting to other highly connecting genes, and sparsely connected genes generally connecting to sparsely connected genes), while the S- and D-networks follow a hub-and-spoke topology, where certain prominent genes connect to a large number of neighbors, which themselves connect to one or a few prominent nodes.

Fig 5. Robustness of network topology.

a) Degree assortativity for the consolidated comparative gene co-expression network, generated for different importance levels. Lines denote the difference between maximum k-core in the empirical network at the selected threshold (arrow tip) and mean degree assortativity across 100 random networks with degree distributions similar to the empirical network (arrow tail). b) Similar to a), but for networks consisting of interactions of each individual type (C, S, and D). Empirical C-networks show positive assortativity as well as higher than random k-core, indicating an affinity between tightly connected nodes and a “rich club”-structure, while the empirical S- and D-networks show negative assortativity and lower than random k-core, indicating a hub-and-spoke type network structure.

We have verified that the selection of a specific cutoff in the indicated range has little effect on the topology of the resulting network, and in the case of S and D, the relative ranking of prominent hubs remains relatively constant (S2 Table). Due to the comparatively small differences in connectivity of main hubs in the C-network, the changes in node rank are more pronounced, although the relative connectivity (node degree relative to the most-connected hub) remains stable. This allows us to select cutoffs for C, S and D to yield reasonably tractable networks, while ensuring that the specific cutoff chosen does not have a dramatic effect on key aspects of the resulting network and our presented analysis.

We note that the topological differences between the C, S, and D-networks revolve around three main characteristics: degree distribution, assortativity and clustering (defined as the proportion of common neighbors between two directly linked genes). In general terms, the C-network is tightly knit, with high clustering and a rather narrow degree distribution (with less prominent hubs). The D-network is opposite, where most D-links connect high-degree (k > 20) genes with otherwise isolated or near-isolated genes k ≤ 3. Additionally, the clustering coefficient in the D-network is zero, and no genes that are directly connected to each other by a D-link are also connect to a common neighbor through other D-links. The S-network lies somewhere in between these two characteristics: its hubs are more prominent than those of the D-network, but less than those of the C-network, and its clustering coefficient is also somewhere in between.

The differences in clustering are the result of mathematical factors—specifically, the transitivity of strong correlations. Considering three genes i, j and k, it follows from the nature of correlations that if |ρij| ≈ |ρik| ≈ 1, then ρjkρijρik. Because of this, if ρij and ρik remain strong and constant between conditions, then so must ρij, naturally creating “triangles” in the C-network. Assortativity is also a natural consequence of this—if gene i is strongly correlated with gene j, then j will generally tend to be correlated with i’s neighbors—therefore, if i has many neighbors, j is likely to have many neighbors as well.

Similarily, should ρij and ρik simultaneously switch signs, it is mathematically not possible that ρjk also switches signs, as three genes may not all be strongly negatively correlated with each other. In fact, the D-network can be approximately characterized as a so-called bipartite network (ignoring any potential weakening of the transitive effect over longer distances and weaker links). A bipartite network is defined as a network in which each node can be categorized into either of two groups, and where there are no direct links between two nodes belonging to the same group. As a direct result, a bipartite network cannot contain closed triads, and therefore has a clustering coefficient of 0.

In the case of the D-network, however, transitivity of correlations does not, by itself, adequately explain the extreme disassortativity we observe. In as much as the D-network necessarily forms a bipartite network, we noticed that the two characteristic groups roughly correspond to hubs and non-hubs. This is not a mathematical necessity—one can readily find bipartite networks in which the majority of direct connections are between hubs (or between non-hubs), or with a very narrow degree distribution. We could, for instance, create a co-expression network consisting of a giant component divided into two groups of equal size, and each node connects to each of the nodes in the other groups; we could then add any number of isolated connected gene pairs. This would constitute a bipartite network consistent with a correlation network, but highly assortative.

A possible explanation for the disassortativity of the D-network could reside in an argument from parsimony—that the underlying regulatory switches would happen at the individual gene level, that these are reasonably rare, and that changes to one or a few genes in a cluster would not substantially affect the relationship between the other genes in that cluster. In this case, the few perturbed genes would show D-type connections to the majority of the genes that remained constant, while the unperturbed genes would connect only to the few perturbed genes.

Functional enrichment

In order to establish whether the observed network relates to possible functional aspects of the invetigated tissues, we performed GO biological process enrichment analysis using GOrilla [46, 47] ( on 4 networks: separately for the C, S and D-type networks generated with a draw size of 105 pairs, as well as the combined network obtained by merging the individual C, S, and D-networks.

For each of the 4 networks, we found significant enrichment for a variety of biological processes (S2, S3 and S4 Figs). In all cases processes related to nervous functions are enriched, ranging from specific concepts (e.g. regulation of neuron projection development) to general ones (cognition, behavior). Of these, GO categories for ‘anterograde trans-synaptic signaling’ is particularly prominent, showing highly significant enrichment in each of the 4 networks. It is reassuring for our method to find these GO categories as being over-represented, since we analyzed data from brain tissues.

Among the remaining enriched terms, we mainly find processes related to cellular differentiation and localization, metabolism, transport and signaling. While these processes are not important for brain functions only, their enrichment in the network seems far from surprising in a co-expression network of brain tissues, given the exceptional energy requirements of the brain.

PBX3: A variable-connection hub

While most prominent hubs in our network tend to connect to their neighbors through only one type of edge, a few genes exhibit a substantial number of connections of different types (see Fig 4). The most prominent of these is the transcription factor PBX3. In developing macaque brains, PBX3 expression is upregulated in the basal ganglia and the cerebral cortex, suggesting a possible role in brain development [48]. However, PBX3 is mostly known as an oncogene involved in a variety of cancer types. One of these is pilocytic astrocytoma [49] (PA)—a form of glioma most commonly occurring in the cerebellum or areas near the brainstem (which include the basal ganglia), but not in the cerebral cortex [50], and more frequent among children and young adults [49]. The fact that our network analysis points out PBX3 as a hub with connections of different types, might hint at a molecular explanation for the differential occurrence of PA in these two tissues we analyzed.

Looking at PBX3’s neighborhood, we find several other genes with similar characteristics. First, all connections this network represents are strongly positive correlations in basal ganglia. Accordingly, S-type connections correspond to weak absolute correlations in the cortex, while D-type connections correspond to strong negative correlations in the cortex. Out of 48 neighbors, 8 are suspected of influencing the development of glioma. Of these 8 genes, 6 (SULT4A1 [51], NDRG4 [52, 53], GAP43 [54, 55], BEX1 [56], HINT1 [57], LZTS1 [58]) are believed to act as tumor suppressants, while the remaining two, PKM [59] and VIPR1 [6062], have been found to be overexpressed in glioma. Several of these genes also appear to play an important role in mammalian brain development and cell differentiation, where the genes VIPR1 [63], NDRG4 [64, 65], BEX1 [66] and GAP43 [67] have been found to exhibit increased expression in the brain of young rats or monkeys.

KEGG pathway enrichment

Using the 2016 KEGG Pathway database through Enrichr [39, 40] (, we searched our network for overrepresented terms. Detailed results are provided in S3 Table. The whole network shows significant enrichment for categories including dopaminergic synapse (S, D), oxytocin signaling (S, D), adrenergic signaling (D), glutamatergic synapse (D), endocannabinoid signaling (D) and GABAergic synapse (D). The C-network shows fewer significantly enriched pathways—the most prominent being the synaptic vesicle cycle pathway.

We note that the most enriched pathways revolve around chemical compounds well known for their role on the nervous system. This is not unexpected, as our data come from two types of brain tissue. Interestingly, these pathways are not particularly well-represented in the C-network, but are ubiquitous in both the S- and D-networks. This might indicate that while these compounds play important roles across the nervous system, there might be significant regulatory differences between different types of brain tissue.

Relation to protein interaction networks

In an effort to find possible causal links behind observed CSD-links, we searched the human protein interaction network (PIN) for connections between nodes in our network. Since CSD-links are based on co-expression analyses, it is not a given that these (often) indirect relationships should be reflected in direct interactions in the PIN. However, as protein-protein interactions are functionally dependent on both proteins being expressed simultaneously, we would expect these to be a potential source of C-type interactions. The PIN used for the search was compiled from three sources: the Center for Cancer System Biology’s human interactome project (HI-II-14) [68], CCSB’s literature data set (Lit-BM-13) [68], and BioGRID [69]. As the BioGRID data set is not particularly stringent when including an interaction, we decided to only include BioGRID interactions backed by at least two sources. The resulting combined PIN contains 49972 interactions for 10349 genes. 9417 of these genes are also present in the original GTEx expression data—approximately 51% of the total number of genes in the GTEx data set. Of the 1798 genes present in the combined CSD network, 1063 (59%) are connected to at least one other gene in the combined PIN. This shows a moderate over-representation (factor 1.16, p < 10−6) of PIN genes in the CSD-network.

Interestingly, 7 gene pairs are directly linked by edges in both the PIN and the CSD network (see Table 3). While this is a small section of either network, it is still a substantially larger overlap than would be expected by random chance: comparing 105 randomized versions of the CSD network (each made by random selection of 1798 genes and 2351 gene pairs from the 18453 genes in the GTEx data) with the PIN as the null case, we find an expectancy of edge overlap on average to be ≈ 0.4, with a single case of 6 overlapping edges as the maximum observed overlap. Accordingly, the observed overlap between the actual CSD network and the PIN is approximately 18.7 times greater than the null hypothesis (p < 10−5). Further, we note that of these overlapping pairs, 6 are C-type edges in the CSD network (the last pair being D-type).

Table 3. Gene pairs directly connected in both the PPI and CSD networks.

In order to investigate more indirect links, we also computed shortest paths across the PIN for each pair of nodes directly connected in the CSD-network, in order to establish whether other CSD-type connections could relate to protein interactions. We found that the genes in the differential co-expression network are more closely connected to each other than average in the PIN, with an average path distance of 3.95 (against 4.04 for the whole network). While the magnitude of this effect is small, it is highly significant with p ≪ 10−3 and z = 6.29 (based on the standard deviation of the mean distance of similarly sized random samples of the whole PIN). This suggests that the protein-protein interactions may explain certain connections in the CSD, although they are most likely not the main factor.

In order to find possibly relevant mediating genes, we sorted the nodes in our PIN according to the number of shortest paths (between genes directly connected in the CSD network) they appeared in, with the added caveat that those paths consisted of at most 3 steps (meaning there could be at most 2 intermediate genes in the PIN). The purpose of the 3-step limit being to eliminate highly indirect connections in the PIN, which are less likely to reflect an actual functional relationship.

The most prominent intermediate genes in the PIN network include ESR1, AKT1, MDM2, TRAF1, UBE2I, SIRT1 and PPP1CA. Most of these genes are known to be involved in processes which should be relevant to the differentiation and function of neural tissue, such as regulation of gene expression (ESR1, AKT1, UBE2I, SIRT1, PPP1CA) and metabolic/catabolic processes (AKT1, MDM2, UBE2I, SIRT1, SKP2, PPP1CA). In more specific detail, ESR1 and TRAF1 are both involved in regulation of NF-kB signaling—ESR1 as an inhibitor and TRAF1 as an activator. NF-kB is known to be involved in synaptic plasticity, learning, and memory, and may be activated by synaptic transmission. Promoter hypermethylation at ESR1 [70], expression of TRAF1 [71] and mutations in NF-kB [72] are all known to be associated with the emergence of glioma. AKT1 is known to interact with forkhead box transcription factors [73] (which include FOXO1, the most highly connected node in the differential co-expression network) in order to regulate cell growth and apoptosis. As FOXO1 connects to several of the cancer-associated genes adjacent to PBX3 (though not to PBX3 directly), the relative prominence of both FOXO1 and AKT1 might reflect a potential tumor-inhibiting effect in combination with PBX3 and its neighbors.

Disease association

As we were able to identify a number of key neurological process amongst the genes present in our networks, we sought to investigate if there could be any links between differential co-expression and inheritable disease. Using the extended OMIM disease association data set, we found no significant enrichment for disease-associated genes in general in the combined network (p = 0.417) or in the C-only or D-only networks (p = 0.175 and p = 0.649, respectively). However, we did find an over-representation (by a factor of 1.35) of disease-associated genes amongst genes in the S-network (non-corrected p = 0.00458). Using Enrichr [39, 40] ( to search the C, S, D- and combined networks for specific OMIM disease associations, we find substantial enrichment for one of two disease families, depending on the kind of network. The S-network shows an over-representation of genes associated to epilepsy, with 8 genes in the network, while only 1.7 genes would be expected by chance (4.65-fold enrichment, p < 0.4 ⋅ 10−3). The D-network shows enrichment for ataxia, with 6 genes (expected number 1.1, 5.4-fold enrichment, p < 8 ⋅ 10−3), and more specifically, spinocerebellar ataxia, with 5 genes (expected number, 0.62, 8-fold enrichment, p < 2 ⋅ 103). While both terms are rather broad and may refer to any of a variety of diseases with different underlying mechanisms, they both involve defects in motor functions, which are controlled by basal ganglia and cerebellum.

Noting that three of the four dominant hubs exhibit protein interactions with a number of glioma-related genes, as well as the presence of several glioma-related genes in PBX3’s neighborhood, we mapped out their immediate network (see Fig 6). Furthermore, we performed an exhaustive literature search to identify whether any of FOXO1 or CARHSP1’s immediate neighbors also exhibited particular expression patterns related to glioma. In fact, in the combined neighborhoods of FOXO1, CARHSP1 and PBX3, we find 104 such genes (out of a total of 340 in said neighborhoods) (see Table 4 for a detailed listing). For most of these genes (59), increased expression is associated with beneficial outcomes, while 45 genes have their activity linked to increased proliferation, invasiveness and general mortality. The hubs themselves are all associated with aggressive forms of glioma. As previously mentioned, PBX3 is known to be upregulated in PA [49], while increased CARHSP1 expression is linked to necrosis and microvascular proliferation (MVP) [74]. On the other hand, FOXO1 is known to prevent cell proliferation in glioblastoma [75].

Fig 6. Network hubs and glioma associations.

Neighborhood of the glioma-associated hubs FOXO1, CARHSP1 and PBX3. Every represented gene connects to at least one of the hubs. Non-hub genes are grouped according to the hubs they connect to (and by interaction type), as well as regulation in glioma. Transcription factors are denoted by triangular labels, other genes with circles. Purple nodes represent genes whose activity is positively linked to harmful outcomes in glioma, while the activity of yellow node is linked to more benign outcomes. White nodes represent genes without established links between activity and glioma. Red links are D-type connections, while green ones are S-type. There are no D-type connections linking two non-hub genes to each other.

Table 4. Glioma-associated nodes in the neighborhood of FOXO1, CARHSP and PBX3.

Associations between gene activity and glioma (as found in literature) are divided into two general groups: Positive associations denote any gene where increased expression is generally linked to harmful outcomes for the patient. This includes genes which are overexpressed in glioma as opposed to healthy tissue, where increased expression in glioma is correlated with higher mortality, or where the gene is more highly expressed in higher grade gliomas. Negative associations denote genes where higher expression is generally linked to beneficial outcomes for the patient.

We also note that the gene-glioma associations presented come from a variety of previously performed studies, and that there is no guarantee that the literature contains an exhaustive list of genes involved in glioma. It is therefore quite possible that there are genes important to glioma development whose role has not yet been discovered, and consequently, would not have been identified here. The substantial presence of known glioma-associated genes in the neighborhood of FOXO1, PBX3 and CARHSP1 may indicate the additional presence of genes with currently unidentified roles in glioma. We therefore present the exhaustive neighborhoods of FOXO1, PBX3 and CARHSP in S4 Table Text as candidate genes for further study.


In this paper, we describe a new method for identifying differential co-expression relationships between genes when comparing two tissues. In contrast to previous methods, our method allows the detection of genes that play critical roles in context-specific function, based on similarities and differences in co-expression patterns. We demonstrate the power of our new method by analyzing the network of cortex and basal ganglia tissues, which is revealed to be associated with a variety of important aspects of brain function. In particular, we find substantial enrichment of (1) GO terms such as anterograde synaptic signaling, cognition, and neural development, (2) hereditary links to the neurological diseases ataxia and epilepsy, and (3) genes associated with pathways involving compounds important to brain function, such as adrenaline, dopamine and oxytocin.

Furthermore, we find indications for the hub PBX3 to be involved in the occurrence of PA—which occasionally occurs in basal ganglia but not in the cortex. In addition, we suggest that the general preponderance of GO terms with clear relevance to brain development and function indicates that the resulting network represents genuine and meaningful relationships between the genes present in the network.

While the gene expression data used in this study came from the same source, this is not a requirement for the method to be viable. Since the networks are based on the non-parametric Spearman rank-correlation (which relies only on the relative rank of each data point within its set) calculated within each of the compared data sets, it is not necessary for the expression values in the different sets to be normalized against each other. In fact, one could compare a tissue with log-scale expression values (e.g. coming from microarrays) against one where the expression values follow a linear scale (e.g. RNA-Seq data), without any impact on the resulting network.

It should be noted, however, that the networks obtained by this method do not correspond to protein-protein interaction networks or even gene regulatory networks, and that the presence of a link between two genes in the differential co-expression network does not necessarily reflect any direct biological interaction between the two. In fact, a link is only evidence of a coinciding pattern: two co-expressed genes may both be regulated by a common transcription factor, or may be similarly affected by outside factors (for instance, nutrient availability).

We note that triple-type nodes (involved in all three types of interactions) are dominated by C-type interactions and a very small share of D-type interactions. We also note that the leading D-type hubs have far more connections than those of the other types. This may suggest that the D-type regulatory change between tissues demonstrates a much more concentrated effect: even if the underlying changes are focused near only a few key genes, a disproportionately large amount of interacting genes could be affected.

The key topological difference between the C-type network on one hand (highly assortative and with a substantial densely connected core) and the S- and D-type networks on the other (with a few dominant hubs, especially in the case of D) also indicate a possible difference regarding the regulatory mechanisms involved. Hence, we speculate that a tightly co-regulated cluster of genes might involve more redundant (and thereby robust) regulatory mechanisms and therefore be less likely to change. In contrast, genes with more centralized neighborhoods may be more likely to see large changes in co-expression patterns due to perturbations at the individual gene levels. An alternative hypothesis is that the strong prominence of hubs in the D-network comes as a result of regulatory changes mostly involving a few genes within large co-expressed clusters, whereby the few perturbed genes would form D-type links with the remainder of said clusters.

We take particular note of a set of gene clusters, centered around the transcription factors FOXO1, CARHSP1 and PBX3. These consist of multiple genes believed to be of major importance to both neural development and the emergence of glioma. While it is known that defects in genes controlling growth and differentiation is a common factor in cancers, to the best of the authors’ knowledge, no association between these specific genes has previously been determined. However, it is difficult to present conclusions about the underlying cause of the observed co-expression patterns as certain. While the FOXO1/PBX3/CARHSP1-centered gene clusters suggest a functional link between several glioma-associated genes, it does not, for instance, automatically follow that misregulation of (or by) PBX3 is the key driver in glioma development.

While it is hard to determine a definite cause behind these connections at the gene level, a comparative study between the CSD network and the PIN offers one possible explanation. We find that in the PIN, both PBX3 and CARHSP1 are indirectly connected to each other (as well as several of their other neighbors in the CSD-network) through the intermediary of TRAF1, whose overexpression is also associated with the emergence of glioma [71]. We also find similar intermediary protein interactions through ESR1, AKT1 and SIRT1, whose activity are also associated with glioma [70, 168, 169]. Additionally, AKT1 is known to interact with FOXO1 (the most prominent hub in the CSD network) to inhibit apoptosis [73]. FOXO1 and AKT1, along with MDM2 (another common intermediary gene in the PIN) have previously been identified in differential co-expression studies of glioblastoma [170].

The prominence of glioma-related cells in these clusters is somewhat unexpected, as our comparison is not between cancerous and non-cancerous data sets, but rather of two (nominally healthy) different parts of the brain. However, we note a substantial overlap between glioma-associated genes and genes particularly expressed in developing (embryonic and juvenile) brains. Additionally, GO enrichment tests using the Gene Ontology Consortium database ( [171] return an 1.8-fold enrichment (Bonferroni-corrected p = 2.1 ⋅ 10−2) for the term “nervous system development” amongst the 340 genes in the neighborhood of FOXO1/CARHSP1/PBX3, and a 2.7-fold enrichment (corrected p = 1.2 ⋅ 10−3) for the same term among the 104 genes for which we found associations with glioma. The latter 104-gene set also shows significant enrichment for more specific subterms of “nervous system development”, including “neuron differentiation” (3.5-fold, p = 4.9 ⋅ 10−3), “neuron projection morphogenesis” (5.4-fold, p = 2.3 ⋅ 10−2) and “axonogenesis” (6.26-fold, p = 1.45 ⋅ 10−2). The observed connection between these genes may therefore reflect a role in the differentiation of stem cells into specific types of brain tissue. Hence, it is plausible that perturbations in these differentiating mechanisms result in differentiation of brain cells into cancerous tissue, which would explain why so many of these genes emerge in studies involving gene expression in glioma.

The scope of the method for differential co-expression network analysis presented in this paper is not restricted to only comparing two different tissues within a given organism. In fact, it may be used to compare any two sets of gene expression data for which a comparison might be reasonable: the only criterion is the existence of a viable one-to-one match between the genes in each data set. Possible applications of our method include comparing gene expressions between healthy and sick individuals, comparing samples from experiments with before/after treatments, comparing organisms subjected to different external environments and comparing closely related species with known orthologs.

Supporting information

S1 Text. Example run of the subsampling algorithm.


S2 Text. Effect of sample size on accuracy of estimates.


S3 Text. List of software used in our reconstruction and analysis.


S5 Text. Comparison between the CSD network and a metabolic gene network built from the Human Recon 2 genome-scale reconstruction.


S1 Table. Top 10 DCe links in the Zhang et al [43] regulatory network: C-type, S-type and S-equivalent type.


S2 Table. An overview of hub nodes for CSD networks generated at different thresholds.


S3 Table. An overview of KEGG pathways enriched in the CSD network.


S4 Table. Candidate genes for further glioma association studies.


S1 Fig. Distributions of C, S and D scores for our data sets.


S2 Fig. Tree diagram of enriched GO biological processes in the C-only network generated using a threshold sample size of 105.


S3 Fig. Tree diagram of enriched GO biological processes in the S-only network generated using a threshold sample size of 105.


S4 Fig. Tree diagram of enriched GO biological processes in the D-only network generated using a threshold sample size of 105.


S5 Fig. Tree diagram of enriched GO biological processes in the combined CSD network generated using a threshold sample size of 105.


S6 Fig. Maximum k-cores for various importance value thresholds.



  1. 1. Ravasi T, Suzuki H, Cannistraci CV, Katayama S, Bajic VB, Tan K, et al. An Atlas of Combinatorial Transcriptional Regulation in Mouse and Man. Cell. 2010;140(5):744–752. Available from: pmid:20211142
  2. 2. Butte AJ, Tamayo P, Slonim D, Golub TR, Kohane IS. Discovering functional relationships between RNA expression and chemotherapeutic susceptibility using relevance networks. Proceedings of the National Academy of Sciences. 2000;97(22):12182–12186. Available from:
  3. 3. Zhou X, Kao MCJ, Wong WH. Transitive functional annotation by shortest-path analysis of gene expression data. Proceedings of the National Academy of Sciences. 2002;99(20):12783–12788. Available from:
  4. 4. Steffen M, Petti A, Aach J, D’haeseleer P, Church G. Automated modelling of signal transduction networks. BMC Bioinformatics. 2002;3(1):1–11. Available from:
  5. 5. Stuart JM, Segal E, Koller D, Kim SK. A Gene-Coexpression Network for Global Discovery of Conserved Genetic Modules. Science. 2003;302(5643):249–255. Available from: pmid:12934013
  6. 6. Carter SL, Brechbühler CM, Griffin M, Bond AT. Gene co-expression network topology provides a framework for molecular characterization of cellular state. Bioinformatics. 2004;20(14):2242–2250. Available from: pmid:15130938
  7. 7. Bergmann S, Ihmels J, Barkai N. Similarities and Differences in Genome-Wide Expression Data of Six Organisms. PLoS Biol. 2003 12;2(1). Available from: pmid:14737187
  8. 8. Eidsaa M, Almaas E. s-core network decomposition: a generalization of k-core analysis to weighted networks. Phys Rev E Stat Nonlin Soft Matter Phys. 2013 Dec;88(6):062819. pmid:24483523
  9. 9. Cabusora L, Sutton E, Fulmer A, Forst CV. Differential network expression during drug and stress response. Bioinformatics. 2005;21(12):2898–2905. Available from: pmid:15840709
  10. 10. Ge H, Liu Z, Church GM, Vidal M. Correlation between transcriptome and interactome mapping data from Saccharomyces cerevisiae. Nature Genetics. 2001 Dec;29(4):482–486. Available from: pmid:11694880
  11. 11. Jansen R, Greenbaum D, Gerstein M. Relating Whole-Genome Expression Data with Protein-Protein Interactions. Genome Research. 2002;12(1):37–46. Available from: pmid:11779829
  12. 12. Lee HK, Hsu AK, Sajdak J, Qin J, Pavlidis P. Coexpression Analysis of Human Genes Across Many Microarray Data Sets. Genome Research. 2004;14(6):1085–1094. Available from: pmid:15173114
  13. 13. Prieto C, Risueno A, Fontanillo C, De las Rivas J. Human gene coexpression landscape: confident network derived from tissue transcriptomic profiles. PLoS One. 2008;3(12):e3911. pmid:19081792
  14. 14. Nowick K, Gernat T, Almaas E, Stubbs L. Differences in human and chimpanzee gene expression patterns define an evolving network of transcription factors in brain. Proceedings of the National Academy of Sciences. 2009;106(52):22358–22363. Available from:
  15. 15. Yang Y, Han L, Yuan Y, Li J, Hei N, Liang H. Gene co-expression network analysis reveals common system-level properties of prognostic genes across cancer types. Nat Commun. 2014;5:3231. pmid:24488081
  16. 16. Kueltz D, Li J, Paguio D, Pham T, Eidsaa M, Almaas E. Population-specific renal proteomes of marine and freshwater three-spined sticklebacks. Journal of Proteomics. 2016;135:112–131. Proteomics in Evolutionary Ecology. Available from:
  17. 17. Childs KL, Davidson RM, Buell CR. Gene Coexpression Network Analysis as a Source of Functional Annotation for Rice Genes. PLoS ONE. 2011 07;6(7):1–12. Available from:
  18. 18. Miller JA, Horvath S, Geschwind DH. Divergence of human and mouse brain transcriptome highlights Alzheimer disease pathways. Proceedings of the National Academy of Sciences. 2010;107(28):12698–12703. Available from:
  19. 19. Voineagu I, Wang X, Johnston P, Lowe JK, Tian Y, Horvath S, et al. Transcriptomic analysis of autistic brain reveals convergent molecular pathology. Nature. 2011 Jun;474(7351):380–384. Available from: pmid:21614001
  20. 20. Oldham MC, Horvath S, Geschwind DH. Conservation and evolution of gene coexpression networks in human and chimpanzee brains. Proceedings of the National Academy of Sciences. 2006;103(47):17973–17978. Available from:
  21. 21. Langfelder P, Horvath S. WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics. 2008;9(1):559. Available from: pmid:19114008
  22. 22. de la Fuente A. From ‘differential expression’ to ‘differential networking’– identification of dysfunctional regulatory networks in diseases. Trends in Genetics. 2010;26(7):326–333. Available from: pmid:20570387
  23. 23. Yonekura-Sakakibara K, Fukushima A, Saito K. Transcriptome data modeling for targeted plant metabolic engineering. Current Opinion in Biotechnology. 2013;24(2):285–290. Available from: pmid:23219185
  24. 24. Wu C, Zhu J, Zhang X. Integrating gene expression and protein-protein interaction network to prioritize cancer-associated genes. BMC Bioinformatics. 2012;13(1):182. Available from: pmid:22838965
  25. 25. Choi JK, Yu U, Yoo OJ, Kim S. Differential coexpression analysis using microarray data and its application to human cancer. Bioinformatics. 2005;21(24):4348. Available from: + pmid:16234317
  26. 26. Reverter A, Ingham A, Lehnert SA, Tan SH, Wang Y, Ratnakumar A, et al. Simultaneous identification of differential gene expression and connectivity in inflammation, adipogenesis and cancer. Bioinformatics. 2006;22(19):2396. Available from: + pmid:16864591
  27. 27. Yu H, Liu BH, Ye ZQ, Li C, Li YX, Li YY. Link-based quantitative methods to identify differentially coexpressed genes and gene Pairs. BMC Bioinformatics. 2011;12(1):315. Available from: pmid:21806838
  28. 28. Amar D, Safer H, Shamir R. Dissection of Regulatory Networks that Are Altered in Disease via Differential Co-expression. PLOS Computational Biology. 2013 03;9(3):1–15. Available from:
  29. 29. Gao X, Arodz T. Detecting Differentially Co-expressed Genes for Drug Target Analysis. Procedia Computer Science. 2013;18:1392–1401. Available from:
  30. 30. Fukushima A. DiffCorr: An R package to analyze and visualize differential correlations in biological networks. Gene. 2013;518(1):209–214. Proceedings of the 23rd International Conference on Genome Informatics (GIW 2012)Proceedings of the 23rd International Conference on Genome Informatics (GIW 2012). Available from: pmid:23246976
  31. 31. Tesson BM, Breitling R, Jansen RC. DiffCoEx: a simple and sensitive method to find differentially coexpressed gene modules. BMC Bioinformatics. 2010;11(1):1–9. Available from:
  32. 32. Ha MJ, Baladandayuthapani V, Do KA. DINGO: differential network analysis in genomics. Bioinformatics. 2015;31(21):3413–3420. Available from: pmid:26148744
  33. 33. Liu BH, Yu H, Tu K, Li C, Li YX, Li YY. DCGL: an R package for identifying differentially coexpressed genes and links from gene expression microarray data. Bioinformatics. 2010;26(20):2637. Available from: + pmid:20801914
  34. 34. Zheng CH, Yuan L, Sha W, Sun ZL. Gene differential coexpression analysis based on biweight correlation and maximum clique. BMC Bioinformatics. 2014;15(15):S3. Available from: pmid:25474074
  35. 35. Zhang B, Horvath S, et al. A general framework for weighted gene co-expression network analysis. Statistical applications in genetics and molecular biology. 2005;4(1):1128.
  36. 36. Ardlie KG, Deluca DS, Segrè AV, Sullivan TJ, Young TR, Gelfand ET, et al. The Genotype-Tissue Expression (GTEx) pilot analysis: Multitissue gene regulation in humans. Science. 2015;348(6235):648–660. Available from:
  37. 37. Pierson E, Koller D, Battle A, Mostafavi S, the GTEx Consortium. Sharing and Specificity of Co-expression Networks across 35 Human Tissues. PLoS Comput Biol. 2015 05;11(5):1–19. Available from:
  38. 38. Derrida B, Flyvbjerg H. Statistical Properties of Randomly Broken Objects and of Multivalley Structures in Disordered Systems. J Phys. 1987;A20:5273.
  39. 39. Chen EY, Tan CM, Kou Y, Duan Q, Wang Z, Meirelles GV, et al. Enrichr: interactive and collaborative HTML5 gene list enrichment analysis tool. BMC Bioinformatics. 2013;14(1):1–14. Available from:
  40. 40. Kuleshov MV, Jones MR, Rouillard AD, Fernandez NF, Duan Q, Wang Z, et al. Enrichr: a comprehensive gene set enrichment analysis web server 2016 update. Nucleic Acids Research. 2016; Available from: pmid:27141961
  41. 41. Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, et al. Cytoscape: A Software Environment for Integrated Models of Biomolecular Interaction Networks. Genome Research. 2003;13(11):2498–2504. Available from: pmid:14597658
  42. 42. Hagberg AA, Schult DA, Swart PJ. Exploring network structure, dynamics, and function using NetworkX. In: Proceedings of the 7th Python in Science Conference (SciPy2008). Pasadena, CA USA; 2008. p. 11–15.
  43. 43. Zhang B, Li H, Riggins RB, Zhan M, Xuan J, Zhang Z, et al. Differential dependency network analysis to identify condition-specific topological changes in biological networks. Bioinformatics. 2009;25(4):526. Available from: + pmid:19112081
  44. 44. Marbach D, Schaffter T, Mattiussi C, Floreano D. Generating Realistic In Silico Gene Networks for Performance Assessment of Reverse Engineering Methods. Journal of Computational Biology. 2009;16(2):229–239. WingX. pmid:19183003
  45. 45. Schaffter T, Marbach D, Floreano D. GeneNetWeaver: In silico benchmark generation and performance profiling of network inference methods. Bioinformatics. 2011;27(16):2263–2270. Wingx. pmid:21697125
  46. 46. Eden E, Lipson D, Yogev S, Yakhini Z. Discovering Motifs in Ranked Lists of DNA Sequences. PLoS Comput Biol. 2007 03;3(3):1–15. Available from:
  47. 47. Eden E, Navon R, Steinfeld I, Lipson D, Yakhini Z. GOrilla: a tool for discovery and visualization of enriched GO terms in ranked gene lists. BMC Bioinformatics. 2009;10(1):1–7. Available from:
  48. 48. Takahashi K, Liu FC, Oishi T, Mori T, Higo N, Hayashi M, et al. Expression of FOXP2 in the developing monkey forebrain: Comparison with the expression of the genes FOXP1, PBX3, and MEIS2. The Journal of Comparative Neurology. 2008;509(2):180–189. Available from: pmid:18461604
  49. 49. Ho CY, Bar E, Giannini C, Marchionni L, Karajannis MA, Zagzag D, et al. MicroRNA profiling in pediatric pilocytic astrocytoma reveals biologically relevant targets, including PBX3, NFIB, and METAP2. Neuro-Oncology. 2013;15(1):69–82. Available from: pmid:23161775
  50. 50. Stüer C, Vilz B, Majores M, Becker A, Schramm J, Simon M. Frequent recurrence and progression in pilocytic astrocytoma in adults. Cancer. 2007;110(12):2799–2808. Available from: pmid:17973253
  51. 51. Sun Z, Shi S, Li H, Shu Xh, Chen Xy, Kong Qy, et al. Evaluation of resveratrol sensitivities and metabolic patterns in human and rat glioblastoma cells. Cancer chemotherapy and pharmacology. 2013 11;72(5):965–73. Available from: pmid:23989725
  52. 52. Schilling SH, Hjelmeland AB, Radiloff DR, Liu IM, Wakeman TP, Fielhauer JR, et al. NDRG4 Is Required for Cell Cycle Progression and Survival in Glioblastoma Cells. Journal of Biological Chemistry. 2009;284(37):25160–25169. Available from: pmid:19592488
  53. 53. Li S, Yang B, Li G, He S, Li Y. Downregulation of N-Myc downstream-regulated gene 4 influences patient survival in gliomas. Brain Tumor Pathology. 2013;30(1):8–14. Available from: pmid:22399192
  54. 54. Huang Zy, Wu Y, Burke SP, Gutmann DH. The 43,000 Growth-associated Protein Functions as a Negative Growth Regulator in Glioma. Cancer Research. 2003;63(11):2933–2939. Available from: pmid:12782600
  55. 55. Gutmann DH, Huang ZY, Hedrick NM, Ding H, Guha A, Watson MA. Mouse glioma gene expression profiling identifies novel human glioma-associated genes. Annals of Neurology. 2002;51(3):393–405. Available from: pmid:11891838
  56. 56. Foltz G, Ryu GY, Yoon JG, Nelson T, Fahey J, Frakes A, et al. Genome-Wide Analysis of Epigenetic Silencing Identifies BEX1 and BEX2 as Candidate Tumor Suppressor Genes in Malignant Glioma. Cancer Research. 2006;66(13):6665–6674. Available from: pmid:16818640
  57. 57. Li H, Zhang Y, Su T, Santella RM, Weinstein IB. Hint1 is a haplo-insufficient tumor suppressor in mice. Oncogene. 2005 Sep;25(5):713–721. Available from:
  58. 58. Ishii H, Vecchione A, Murakumo Y, Baldassarre G, Numata S, Trapasso F, et al. FEZ1/LZTS1 gene at 8p22 suppresses cancer cell growth and regulates mitosis. Proceedings of the National Academy of Sciences. 2001;98(18):10374–10379. Available from:
  59. 59. Mukherjee J, Phillips JJ, Zheng S, Wiencke J, Ronen SM, Pieper RO. Pyruvate Kinase M2 Expression, but Not Pyruvate Kinase Activity, Is Up-Regulated in a Grade-Specific Manner in Human Glioma. PLoS ONE. 2013 02;8(2):1–11. Available from:
  60. 60. Jaworski MD. Expression of pituitary adenylate cyclase-activating polypeptide (PACAP) and the PACAP-selective receptor in cultured rat astrocytes, human brain tumors, and in response to acute intracranial injury. Cell and Tissue Research. 2000;300(2):219–230. Available from: pmid:10867818
  61. 61. Barbarin A, Séité P, Godet J, Bensalma S, Muller JM, Chadéneau C. Atypical nuclear localization of {VIP} receptors in glioma cell lines and patients. Biochemical and Biophysical Research Communications. 2014;454(4):524–530. Available from: pmid:25450687
  62. 62. Nakamachi T, Sugiyama K, Watanabe J, Imai N, Kagami N, Hori M, et al. Comparison of Expression and Proliferative Effect of Pituitary Adenylate Cyclase-Activating Polypeptide (PACAP) and its Receptors on Human Astrocytoma Cell Lines. Journal of Molecular Neuroscience. 2014 11;54(3):388–394. Available from: pmid:25091859
  63. 63. Basille M, Vaudry D, Coulouarn Y, Jegou S, Lihrmann I, Fournier A, et al. Comparative distribution of pituitary adenylate cyclase-activating polypeptide (PACAP) binding sites and PACAP receptor mRNAs in the rat brain during development. The Journal of Comparative Neurology. 2000;425(4):495–509. Available from:<495::AID-CNE3>3.0.CO;2-A. pmid:10975876
  64. 64. Nakada N, Hongo S, Ohki T, Maeda A, Takeda M. Molecular characterization of NDRG4/Bdm1 protein isoforms that are differentially regulated during rat brain development. Developmental Brain Research. 2002;135(1–2):45–53. Available from: pmid:11978392
  65. 65. Qu X, Zhai Y, Wei H, Zhang C, Xing G, Yu Y, et al. Characterization and expression of three novel differentiation-related genes belong to the human NDRG gene family. Molecular and cellular biochemistry. 2002 01;229(1-2):35–44. Copyright—Kluwer Academic Publishers 2002; Last updated—2014-08-22. Available from: pmid:11936845
  66. 66. Alvarez E, Zhou W, Witta SE, Freed CR. Characterization of the Bex gene family in humans, mice, and rats. Gene. 2005;357(1):18–28. Available from: pmid:15958283
  67. 67. Mahalik TJ, Carrier A, Owens GP, Clayton G. The expression of {GAP43} mRNA during the late embryonic and early postnatal development of the {CNS} of the rat: an in situ hybridization study. Developmental Brain Research. 1992;67(1):75–83. Available from: pmid:1386294
  68. 68. Rolland T, Taşan M, Charloteaux B, Pevzner S, Zhong Q, Sahni N, et al. A Proteome-Scale Map of the Human Interactome Network. Cell. 2016 05;159(5):1212–1226. Available from:
  69. 69. Chatr-aryamontri A, Breitkreutz BJ, Oughtred R, Boucher L, Heinicke S, Chen D, et al. The BioGRID interaction database: 2015 update. Nucleic Acids Research. 2015;43(D1):D470–D478. Available from: pmid:25428363
  70. 70. Bax DA, Little SE, Gaspar N, Perryman L, Marshall L, Viana-Pereira M, et al. Molecular and Phenotypic Characterisation of Paediatric Glioma Cell Lines as Models for Preclinical Drug Development. PLOS ONE. 2009 04;4(4):1–9. Available from:
  71. 71. Conti A, Aguennouz M, Torre DL, Cardali S, Angileri FF, Buemi C, et al. Expression of the tumor necrosis factor receptor—associated factors 1 and 2 and regulation of the nuclear factor—kB antiapoptotic activity in human gliomas. Journal of Neurosurgery. 2005;103(5):873–881. PMID: 16304992. Available from: pmid:16304992
  72. 72. Dolcet X, Llobet D, Pallares J, Matias-Guiu X. NF-kB in development and progression of human cancer. Virchows Archiv. 2005;446(5):475–482. Available from: pmid:15856292
  73. 73. Zhang X, Tang N, Hadden TJ, Rishi AK. Akt, FoxO and regulation of apoptosis. Biochimica et Biophysica Acta (BBA)—Molecular Cell Research. 2011;1813(11):1978–1986. PI3K-AKT-FoxO axis in Cancer and Aging. Available from:
  74. 74. McDonald KL, O’Sullivan MG, Parkinson JF, Shaw JM, Payne CA, Brewer JM, et al. IQGAP1 and IGFBP2: Valuable Biomarkers for Determining Prognosis in Glioma Patients. Journal of Neuropathology & Experimental Neurology. 2007;66(5):405–417. Available from:
  75. 75. Lei BX, Liu ZH, Li ZJ, Li C, Deng YF. miR-21 induces cell proliferation and suppresses the chemosensitivity in glioblastoma cells via downregulation of FOXO1. Int J Clin Exp Med. 2014;7(8):2060–2066. pmid:25232387
  76. 76. Scrideli CA, Carlotti CG, Okamoto OK, Andrade VS, Cortez MAA, Motta FJN, et al. Gene expression profile analysis of primary glioblastomas and non-neoplastic brain tissue: identification of potential target genes by oligonucleotide microarray and real-time quantitative PCR. Journal of Neuro-Oncology. 2008;88(3):281–291. Available from: pmid:18398573
  77. 77. Elstner A, Stockhammer F, Nguyen-Dobinsky TN, Nguyen QL, Pilgermann I, Gill A, et al. Identification of diagnostic serum protein profiles of glioblastoma patients. Journal of Neuro-Oncology. 2011;102(1):71–80. Available from: pmid:20617365
  78. 78. Laughlin KM, Luo D, Liu C, Shaw G, Warrington KH, Qiu J, et al. Hematopoietic- and Neurologic-Expressed Sequence 1 Expression in the Murine GL261 and High-Grade Human Gliomas. Pathology & Oncology Research. 2009;15(3):437. Available from:
  79. 79. Feindt J, Becker I, Blömer U, Hugo HH, Mehdorn HM, Krisch B, et al. Expression of Somatostatin Receptor Subtypes in Cultured Astrocytes and Gliomas. Journal of Neurochemistry. 1995;65(5):1997–2005. Available from: pmid:7595483
  80. 80. Lo KC, Rossi MR, LaDuca J, Hicks DG, Turpaz Y, Hawthorn L, et al. Candidate glioblastoma development gene identification using concordance between copy number abnormalities and gene expression level changes. Genes, Chromosomes and Cancer. 2007;46(10):875–894. Available from: pmid:17620294
  81. 81. Lehner B, Park S. Cancer: Exploiting collateral damage. Nature. 2012 Aug;488(7411):284–285. Available from: pmid:22895327
  82. 82. Liu R, Tian B, Gearing M, Hunter S, Ye K, Mao Z. Cdk5-mediated regulation of the PIKE-A-Akt pathway and glioblastoma cell invasion. Proceedings of the National Academy of Sciences. 2008;105(21):7570–7575. Available from:
  83. 83. Nies AT, Jedlitschky G, König J, Herold-Mende C, Steiner HH, Schmitt HP, et al. Expression and immunolocalization of the multidrug resistance proteins, MRP1–MRP6 (ABCC1–ABCC6), in human brain. Neuroscience. 2004;129(2):349–360. Available from: pmid:15501592
  84. 84. Nevo I, Woolard K, Cam M, Li A, Webster JD, Kotliarov Y, et al. Identification of Molecular Pathways Facilitating Glioma Cell Invasion In Situ. PLOS ONE. 2014 11;9(11):1–12. Available from:
  85. 85. Dai B, Wan W, Zhang P, Zhang Y, Pan C, Meng G, et al. SET and MYND domain-containing protein 3 is overexpressed in human glioma and contributes to tumorigenicity. Oncology reports. 2015;34(5):2722–2730. pmid:26328527
  86. 86. Ellert-Miklaszewska A, Dabrowski M, Lipko M, Sliwa M, Maleszewska M, Kaminska B. Molecular definition of the pro-tumorigenic phenotype of glioma-activated microglia. Glia. 2013;61(7):1178–1190. Available from: pmid:23650109
  87. 87. Kounelakis MG, Zervakis ME, Giakos GC, Postma GJ, Buydens LMC, Kotsiakis X. On the Relevance of Glycolysis Process on Brain Gliomas. IEEE Journal of Biomedical and Health Informatics. 2013 Jan;17(1):128–135. pmid:22614725
  88. 88. Tang Y, He W, Wei Y, Qu Z, Zeng J, Qin C. Screening Key Genes and Pathways in Glioma Based on Gene Set Enrichment Analysis and Meta-analysis. Journal of Molecular Neuroscience. 2013;50(2):324–332. Available from: pmid:23494636
  89. 89. Xu DS, Yang C, Proescholdt M, Bründl E, Brawanski A, Fang X, et al. Neuronatin in a Subset of Glioblastoma Multiforme Tumor Progenitor Cells Is Associated with Increased Cell Proliferation and Shorter Patient Survival. PLOS ONE. 2012 05;7(5):1–7. Available from:
  90. 90. Kavsan VM, Dmitrenko VV, Shostak KO, Bukreieva TV, Vitak NY, Simirenko OE, et al. Comparison of microarray and SAGE techniques in gene expression analysis of human glioblastoma. Cytology and Genetics. 2007;41(1):30–48. Available from:
  91. 91. Costa H, Xu X, Overbeek G, Vasaikar S, Patro CPK, Kostopoulou ON, et al. Human cytomegalovirus may promote tumour progression by upregulating arginase-2. Oncotarget. 2016;Available from:
  92. 92. Shpak M, Hall AW, Goldberg MM, Derryberry DZ, Ni Y, Iyer VR, et al. An eQTL analysis of the human glioblastoma multiforme genome. Genomics. 2014;103(4):252–263. Available from: pmid:24607568
  93. 93. Cheng W, Li M, Jiang Y, Zhang C, Cai J, Wang K, et al. Association between small heat shock protein B11 and the prognostic value of MGMT promoter methylation in patients with high-grade glioma. Journal of Neurosurgery. 2016;125(1):7–16. Available from: pmid:26544773
  94. 94. Courson DS, Cheney RE. Myosin-X and disease. Experimental Cell Research. 2015;334(1):10–15. Invited Reviews: Molecular Motors. Available from: pmid:25819274
  95. 95. Zeng Y, Yang Z, Xu JG, Yang MS, Zeng ZX, You C. Differentially expressed genes from the glioblastoma cell line SHG-44 treated with all-trans retinoic acid in vitro. Journal of Clinical Neuroscience. 2009;16(2):285–294. Available from: pmid:19091570
  96. 96. Shim BI, Kim KN, Kim YR, Sohn SH, Seo SH, Lee SH, et al. Genetic analysis of tumors in human brain using by radioactive cDNA microarray. Journal of Nuclear Medicine. 2007;48(supplement 2):465P. Available from:
  97. 97. Chang CL. Genome-Wide Oligonucleotide Microarray Analysis of Gene-Expression Profiles of Taiwanese Patients with Anaplastic Astrocytoma and Glioblastoma Multiforme. Journal of Biomolecular Screening. 2008;13(9):912–921. Available from: pmid:18812569
  98. 98. Dmitrenko V, Bojko O, Shostak K, Vitak NY, Bukreeva T, Rozumenko V, et al. Characterization of genes, down-regulated in human glioma, potential tumor suppressor genes. Biopolymers and Cell. 2007;23(4):347–362.
  99. 99. Mangiola A, Saulnier N, De Bonis P, Orteschi D, Sica G, Lama G, et al. Gene Expression Profile of Glioblastoma Peritumoral Tissue: An Ex Vivo Study. PLOS ONE. 2013 03;8(3):1–15. Available from:
  100. 100. Ruano Y, Mollejo M, Ribalta T, Fiaño C, Camacho FI, Gómez E, et al. Identification of novel candidate target genes in amplicons of Glioblastoma multiforme tumors detected by expression and CGH microarray profiling. Molecular Cancer. 2006;5(1):39. Available from: pmid:17002787
  101. 101. Ducray F, de Reyniès A, Chinot O, Idbaih A, Figarella-Branger D, Colin C, et al. An ANOCEF genomic and transcriptomic microarray study of the response to radiotherapy or to alkylating first-line chemotherapy in glioblastoma patients. Molecular Cancer. 2010;9(1):234. Available from: pmid:20822523
  102. 102. Volodko N, Salla M, Zare A, Abulghasem EA, Vincent K, Benesch MGK, et al. RASSF1A Site-Specific Methylation Hotspots in Cancer and Correlation with RASSF1C and MOAP-1. Cancers. 2016;8(6):55. Available from:
  103. 103. Godlewski J, Nowicki MO, Bronisz A, Williams S, Otsuki A, Nuovo G, et al. Targeting of the Bmi-1 Oncogene/Stem Cell Renewal Factor by MicroRNA-128 Inhibits Glioma Proliferation and Self-Renewal. Cancer Research. 2008;68(22):9125–9130. Available from: pmid:19010882
  104. 104. Gao J, Chen T, Liu J, Liu W, Hu G, Guo X, et al. Loss of NECL1, a novel tumor suppressor, can be restored in glioma by HDAC inhibitor-Trichostatin A through Sp1 binding site. Glia. 2009;57(9):989–999. Available from: pmid:19062177
  105. 105. Joshi AD, Parsons DW, Velculescu VE, Riggins GJ. Sodium ion channel mutations in glioblastoma patients correlate with shorter survival. Molecular Cancer. 2011;10(1):17. Available from: pmid:21314958
  106. 106. Gupta MK, Jayaram S, Madugundu AK, Chavan S, Advani J, Pandey A, et al. Chromosome-centric Human Proteome Project: Deciphering Proteins Associated with Glioma and Neurodegenerative Disorders on Chromosome 12. Journal of Proteome Research. 2014;13(7):3178–3190. Available from: pmid:24804578
  107. 107. Wan Kim Y, Koul D, Kim S, Almeida J, Bogler O, Aldape K, et al. Abstract #2427: Novel gene set identification and pathway specific survival patterns using gene expression profiling of human glioblastoma: A study based on TCGA data analysis. Cancer Research. 2014;69(9 Supplement):2427–2427. Available from:
  108. 108. Dallol A, Krex D, Hesson L, Eng C, Maher ER, Latif F. Frequent epigenetic inactivation of the SLIT2 gene in gliomas. Oncogene. 0000;22(29):4611–4616. Available from:
  109. 109. Lai CPK, Bechberger JF, Thompson RJ, MacVicar BA, Bruzzone R, Naus CC. Tumor-Suppressive Effects of Pannexin 1 in C6 Glioma Cells. Cancer Research. 2007;67(4):1545–1554. Available from: pmid:17308093
  110. 110. Yu X, Feng L, Liu D, Zhang L, Wu B, Jiang W, et al. Quantitative proteomics reveals the novel co-expression signatures in early brain development for prognosis of glioblastoma multiforme. Oncotarget. 2016 March;7(12):14161–14171. Available from: pmid:26895104
  111. 111. Wang R, Gurguis CI, Gu W, Ko EA, Lim I, Bang H, et al. Ion channel gene expression predicts survival in glioma patients. Scientific Reports. 2015 Aug;5:11593 EP –. Article. Available from: pmid:26235283
  112. 112. Jin Y, Cui D, Ren J, Wang K, Zeng T, Gao L. CACNA2D3 is downregulated in gliomas and functions as a tumor suppressor. Molecular Carcinogenesis. 2016;p. n/a–n/a. Available from:
  113. 113. Rosa MD, Sanfilippo C, Libra M, Musumeci G, Malaguarnera L. Different pediatric brain tumors are associated with different gene expression profiling. Acta Histochemica. 2015;117(4–5):477–485. Immunomarkers in human developing and pediatric neoplastic tissues. Available from: pmid:25792036
  114. 114. Chen H, Mei L, Zhou L, Shen X, Guo C, Zheng Y, et al. PTEN restoration and PIK3CB knockdown synergistically suppress glioblastoma growth in vitro and in xenografts. Journal of Neuro-Oncology. 2011;104(1):155–167. Available from: pmid:21188471
  115. 115. Kinnersley B, Kamatani Y, Labussiere M, Wang Y, Galan P, Mokhtari K, et al. Search for new loci and low-frequency variants influencing glioma risk by exome-array analysis. Eur J Hum Genet. 2015 Aug;Article. Available from: pmid:26264438
  116. 116. Szeliga M, Bogacińska-Karaś M, Różycka A, Hilgier W, Marquez J, Albrecht J. Silencing of GLS and overexpression of GLS2 genes cooperate in decreasing the proliferation and viability of glioblastoma cells. Tumor Biology. 2014;35(3):1855–1862. Available from: pmid:24096582
  117. 117. Duncan CG, Killela PJ, Payne CA, Lampson B, Chen WC, Liu J, et al. Integrated genomic analyses identify ERRFI1 and TACC3 as glioblastoma-targeted genes. Oncotarget. 2010 Aug;1(4):265–277. Available from: pmid:21113414
  118. 118. Ruano Y, Mollejo M, Camacho FI, de Lope AR, Fiaño C, Ribalta T, et al. Identification of survival-related genes of the phosphatidylinositol 3’-kinase signaling pathway in glioblastoma multiforme. Cancer. 2008;112(7):1575–1584. Available from: pmid:18260157
  119. 119. MAEDA K, MATSUHASHI S, TABUCHI K, WATANABE T, KATAGIRI T, OYASU M, et al. Brain Specific Human Genes, NELL1 and NELL2, Are Predominantly Expressed in Neuroblastoma and Other Embryonal Neuroepithelial Tumors. Neurologia medico-chirurgica. 2001;41(12):582–589. pmid:11803583
  120. 120. Li G, Warden C, Zou Z, Neman J, Krueger JS, Jain A, et al. Altered Expression of Polycomb Group Genes in Glioblastoma Multiforme. PLOS ONE. 2013 11;8(11):1–8. Available from:
  121. 121. Shan S, Hui G, Hou F, Shi H, Zhou G, Yan H, et al. Expression of metastasis-associated protein 3 in human brain glioma related to tumor prognosis. Neurological Sciences. 2015;36(10):1799–1804. Available from: pmid:26002011
  122. 122. Lin N, Di C, Bortoff K, Fu J, Truszkowski P, Killela P, et al. Deletion or Epigenetic Silencing of AJAP1 on 1p36 in Glioblastoma. Molecular Cancer Research. 2012;10(2):208–217. Available from: pmid:22241217
  123. 123. Ernst A, Hofmann S, Ahmadi R, Becker N, Korshunov A, Engel F, et al. Genomic and Expression Profiling of Glioblastoma Stem Cell–Like Spheroid Cultures Identifies Novel Tumor-Relevant Genes Associated with Survival. Clinical Cancer Research. 2009;15(21):6541–6550. Available from: pmid:19861460
  124. 124. Yeo CWS, Ng FSL, Chai C, Tan JMM, Koh GRH, Chong YK, et al. Parkin Pathway Activation Mitigates Glioma Cell Proliferation and Predicts Patient Survival. Cancer Research. 2012;72(10):2543–2553. Available from: pmid:22431710
  125. 125. Zhang Z, Tang H, Wang Z, Zhang B, Liu W, Lu H, et al. MiR-185 Targets the DNA Methyltransferases 1 and Regulates Global DNA Methylation in human glioma. Molecular Cancer. 2011;10(1):124. Available from: pmid:21962230
  126. 126. LeFave CV, Squatrito M, Vorlova S, Rocco GL, Brennan CW, Holland EC, et al. Splicing factor hnRNPH drives an oncogenic splicing switch in gliomas. The EMBO Journal. 2011;30(19):4084–4097. Available from: pmid:21915099
  127. 127. Tatenhorst L, Püttmann S, Senner V, Paulus W. Genes Associated with Fast Glioma Cell Migration In Vitro and In Vivo. Brain Pathology. 2005;15(1):46–54. Available from: pmid:15779236
  128. 128. Fazi B, Felsani A, Grassi L, Moles A, D’Andrea D, Toschi N, et al. The transcriptome and miRNome profiling of glioblastoma tissues and peritumoral regions highlights molecular pathways shared by tumors and surrounding areas and reveals differences between short-term and long-term survivors. Oncotarget. 2015;6(26):22526. pmid:26188123
  129. 129. Mikkelsen T, Brodie C, Finniss S, Berens ME, Rennert JL, Nelson K, et al. Radiation sensitization of glioblastoma by cilengitide has unanticipated schedule-dependency. International Journal of Cancer. 2009;124(11):2719–2727. Available from: pmid:19199360
  130. 130. Gangemi RMR, Griffero F, Marubbi D, Perera M, Capra MC, Malatesta P, et al. SOX2 Silencing in Glioblastoma Tumor-Initiating Cells Causes Stop of Proliferation and Loss of Tumorigenicity. STEM CELLS. 2009;27(1):40–48. Available from: pmid:18948646
  131. 131. Park SG, Jung S, Ryu HH, Jung TY, Moon KS, Kim IY, et al. Role of 14-3-3-beta in the migration and invasion in human malignant glioma cell line U87MG. Neurological Research. 2012;34(9):893–900. Available from: pmid:22925547
  132. 132. Poli M, Derosas M, Luscieti S, Cavadini P, Campanella A, Verardi R, et al. Pantothenate kinase-2 (Pank2) silencing causes cell growth reduction, cell-specific ferroportin upregulation and iron deregulation. Neurobiology of Disease. 2010;39(2):204–210. Available from: pmid:20399859
  133. 133. Grunda JM, Fiveash J, Palmer CA, Cantor A, Fathallah-Shaykh HM, Nabors LB, et al. Rationally Designed Pharmacogenomic Treatment Using Concurrent Capecitabine and Radiotherapy for Glioblastoma; Gene Expression Profiles Associated with Outcome. Clinical Cancer Research. 2010;16(10):2890–2898. Available from: pmid:20460474
  134. 134. Yoshino A, Ogino A, Yachi K, Ohta T, Fukushima T, Watanabe T, et al. Gene expression profiling predicts response to temozolomide in malignant gliomas. International journal of oncology. 2010;36(6):1367. pmid:20428759
  135. 135. Com E, Clavreul A, Lagarrigue M, Michalak S, Menei P, Pineau C. Quantitative proteomic Isotope-Coded Protein Label (ICPL) analysis reveals alteration of several functional processes in the glioblastoma. Journal of Proteomics. 2012;75(13):3898–3913. Available from: pmid:22575386
  136. 136. Seznec J, Naumann U. Microarray Analysis in a Cell Death Resistant Glioma Cell Line to Identify Signaling Pathways and Novel Genes Controlling Resistance and Malignancy. Cancers. 2011;3(3):2827. Available from: pmid:24212935
  137. 137. Collet B, Guitton N, Saïkali S, Avril T, Pineau C, Hamlat A, et al. Differential analysis of glioblastoma multiforme proteome by a 2D-DIGE approach. Proteome Science. 2011;9(1):16. Available from: pmid:21470419
  138. 138. Leclerc C, Haeich J, Aulestia FJ, Kilhoffer MC, Miller AL, Néant I, et al. Calcium signaling orchestrates glioblastoma development: Facts and conjunctures. Biochimica et Biophysica Acta (BBA)—Molecular Cell Research. 2016;1863(6, Part B):1447–1459. Calcium and Cell Fate. Available from:
  139. 139. Valente V, Serafim RB, de Oliveira LC, Adorni FS, Torrieri R, da Cunha Tirapelli DP, et al. Modulation of HJURP (Holliday Junction-Recognizing Protein) Levels Is Correlated with Glioblastoma Cells Survival. PLOS ONE. 2013 04;8(4):1–10. Available from:
  140. 140. Furukawa K, Okajima T, Tsuchida A, Furukawa K. In: Taniguchi N, Honke K, Fukuda M, Narimatsu H, Yamaguchi Y, Angata T, editors. ST6 N-Acetylgalactosaminide Alpha-2,6-Sialyltransferase 5,6 (ST6GALNAC5,6). Tokyo: Springer Japan; 2014. p. 759–765. Available from:
  141. 141. Yokota T, Kouno J, Adachi K, Takahashi H, Teramoto A, Matsumoto K, et al. Identification of histological markers for malignant glioma by genome-wide expression analysis: dynein, α-PIX and sorcin. Acta Neuropathologica. 2006;111(1):29–38. Available from: pmid:16320026
  142. 142. Xue J, Zhou A, Wu Y, Morris SA, Lin K, Amin SB, et al. STAT3 activation promotes glioma tumorigenesis by inducing miR-182-5p. Cancer Research. 2016; Available from:
  143. 143. Feng SY, Dong CG, Wu WK, Wang XJ, Qiao J, Shao JF. Lentiviral expression of anti-microRNAs targeting miR-27a inhibits proliferation and invasiveness of U87 glioma cells. Molecular medicine reports. 2012;6(2):275–281. pmid:22614734
  144. 144. Mukasa A, Ueki K, Ge X, Ishikawa S, Ide T, Fujimaki T, et al. Selective Expression of a Subset of Neuronal Genes in Oligodendroglioma with Chromosome 1p Loss. Brain Pathology. 2004;14(1):34–42. Available from: pmid:14997935
  145. 145. Waha A, Felsberg J, Hartmann W, von dem Knesebeck A, Mikeska T, Joos S, et al. Epigenetic Downregulation of Mitogen-Activated Protein Kinase Phosphatase MKP-2 Relieves Its Growth Suppressive Activity in Glioma Cells. Cancer Research. 2010;70(4):1689–1699. Available from: pmid:20124482
  146. 146. Liu S, Yin F, Zhang J, Wicha MS, Chang AE, Fan W, et al. Regulatory Roles of miRNA in the Human Neural Stem Cell Transformation to Glioma Stem Cells. Journal of Cellular Biochemistry. 2014;115(8):1368–1380. Available from: pmid:24519663
  147. 147. Tso JL, Yang S, Menjivar JC, Yamada K, Zhang Y, Hong I, et al. Bone morphogenetic protein 7 sensitizes O6-methylguanine methyltransferase expressing-glioblastoma stem cells to clinically relevant dose of temozolomide. Molecular Cancer. 2015;14(1):189. Available from: pmid:26546412
  148. 148. Nobusawa S, Hirato J, Kurihara H, Ogawa A, Okura N, Nagaishi M, et al. Intratumoral Heterogeneity of Genomic Imbalance in a Case of Epithelioid Glioblastoma with BRAF V600E Mutation. Brain Pathology. 2014;24(3):239–246. Available from: pmid:24354918
  149. 149. Deighton RF, Le Bihan T, Martin SF, Barrios-Llerena ME, Gerth AMJ, Kerr LE, et al. The proteomic response in glioblastoma in young patients. Journal of Neuro-Oncology. 2014;119(1):79–89. Available from: pmid:24838487
  150. 150. Jensen SA, Calvert AE, Volpert G, Kouri FM, Hurley LA, Luciano JP, et al. Bcl2L13 is a ceramide synthase inhibitor in glioblastoma. Proceedings of the National Academy of Sciences. 2014;111(15):5682–5687. Available from:
  151. 151. Wakabayashi T, Natsume A, Hashizume Y, Fujii M, Mizuno M, Yoshida J. A phase I clinical trial of interferon-beta gene therapy for high-grade glioma: novel findings from gene expression profiling and autopsy. The Journal of Gene Medicine. 2008;10(4):329–339. Available from: pmid:18220319
  152. 152. Dang AQ, Aref AM, Paleo B, Shon DJ, Hong E, Gomez EJ, et al. Using REMBRANDT to paint in the details of glioma biology: Applications for future immunotherapy. INTECH Open Access Publisher; 2013.
  153. 153. Senger DL, Tudan C, Guiot MC, Mazzoni IE, Molenkamp G, LeBlanc R, et al. Suppression of Rac Activity Induces Apoptosis of Human Glioma Cells but not Normal Human Astrocytes. Cancer Research. 2002;62(7):2131–2140. Available from: pmid:11929835
  154. 154. Puustinen P, Junttila MR, Vanhatupa S, Sablina AA, Hector ME, Teittinen K, et al. PME-1 Protects Extracellular Signal-Regulated Kinase Pathway Activity from Protein Phosphatase 2A–Mediated Inactivation in Human Malignant Glioma. Cancer Research. 2009;69(7):2870–2877. Available from: pmid:19293187
  155. 155. Li Z, Bao S, Wu Q, Wang H, Eyler C, Sathornsumetee S, et al. Hypoxia-Inducible Factors Regulate Tumorigenic Capacity of Glioma Stem Cells. Cancer Cell. 2009;15(6):501–513. Available from: pmid:19477429
  156. 156. Sanchez Diaz PC, Chang JC, Dao T, Chen Y, Hung JY. Ubiquitin Carboxyl-Terminal Esterase L1 (UCHL1) Regulates Stem-Like Cancer Cell Populations in Pediatric High-Grade Glioma. The FASEB Journal. 2016;30(1 Supplement):1108.4. Available from:
  157. 157. Czernicki T, Zegarska J, Paczek L, Cukrowska B, Grajkowska W, Zajaczkowska A, et al. Gene expression profile as a prognostic factor in high-grade gliomas. International journal of oncology. 2007;30(1):55–64. pmid:17143512
  158. 158. Suzuki T, Maruno M, Wada K, Kagawa N, Fujimoto Y, Hashimoto N, et al. Genetic analysis of human glioblastomas using a genomic microarray system. Brain Tumor Pathology. 2004;21(1):27–34. Available from: pmid:15696966
  159. 159. van den Boom J, Wolter M, Blaschke B, Knobbe CB, Reifenberger G. Identification of novel genes associated with astrocytoma progression using suppression subtractive hybridization and real-time reverse transcription-polymerase chain reaction. International Journal of Cancer. 2006;119(10):2330–2338. Available from: pmid:16865689
  160. 160. Shi H, Du J, Wang L, Zheng B, Gong H, Wu Y, et al. Lower expression of Nrdp1 in human glioma contributes tumor progression by reducing apoptosis. IUBMB Life. 2014;66(10):704–710. Available from: pmid:25355637
  161. 161. Allingham-Hawkins D, Lea A, Levine S. DecisionDx-GBM gene expression assay for prognostic testing in glioblastoma multiform. PLOS Currents Evidence on Genomic Tests. 2010;.
  162. 162. Vital AL, Tabernero MD, Castrillo A, Rebelo O, Tão H, Gomes F, et al. Gene expression profiles of human glioblastomas are associated with both tumor cytogenetics and histopathology. Neuro-Oncology. 2010; Available from: pmid:20484145
  163. 163. Chen J, Xu J, Ying K, Cao G, Hu G, Wang L, et al. Molecular cloning and characterization of a novel human {BTB} domain-containing gene, BTBD10, which is down-regulated in glioma. Gene. 2004;340(1):61–69. Available from: pmid:15556295
  164. 164. Liu Z, Niu Y, Xie M, Bu Y, Yao Z, Gao C. Gene expression profiling analysis reveals that DLG3 is down-regulated in glioblastoma. Journal of Neuro-Oncology. 2014;116(3):465–476. Available from: pmid:24381070
  165. 165. Grobben B, De Deyn P, Slegers H. Rat C6 glioma as experimental model system for the study of glioblastoma growth and invasion. Cell and Tissue Research. 2002;310(3):257–270. Available from: pmid:12457224
  166. 166. Korshunov A, Sycheva R, Golanov A. Genetically distinct and clinically relevant subtypes of glioblastoma defined by array-based comparative genomic hybridization (array-CGH). Acta Neuropathologica. 2006;111(5):465. Available from: pmid:16557391
  167. 167. Nissou MF, El Atifi M, Guttin A, Godfraind C, Salon C, Garcion E, et al. Hypoxia-induced expression of VE-cadherin and filamin B in glioma cell cultures and pseudopalisade structures. Journal of Neuro-Oncology. 2013;113(2):239–249. Available from: pmid:23543272
  168. 168. Haas-Kogan D, Shalev N, Wong M, Mills G, Yount G, Stokoe D. Protein kinase B (PKB/Akt) activity is elevated in glioblastoma cells due to mutation of the tumor suppressor PTEN/MMAC. Current Biology. 1998;8(21):1195–S1. Available from: pmid:9799739
  169. 169. Qu Y, Zhang J, Wu S, Li B, Liu S, Cheng J. {SIRT1} promotes proliferation and inhibits apoptosis of human malignant glioma cell lines. Neuroscience Letters. 2012;525(2):168–172. Available from: pmid:22867969
  171. 171. Gene Ontology Consortium: going forward. Nucleic Acids Research. 2015;43(D1):D1049. Available from: + pmid:25428369