http://orcid.org/0000-0003-3426-8294
Figures
Abstract
Applying differentially expressed genes (DEGs) to identify feasible biomarkers in diseases can be a hard task when working with heterogeneous datasets. Expression data are strongly influenced by technology, sample preparation processes, and/or labeling methods. The proliferation of different microarray platforms for measuring gene expression increases the need to develop models able to compare their results, especially when different technologies can lead to signal values that vary greatly. Integrative meta-analysis can significantly improve the reliability and robustness of DEG detection. The objective of this work was to develop an integrative approach for identifying potential cancer biomarkers by integrating gene expression data from two different platforms. Pancreatic ductal adenocarcinoma (PDAC), where there is an urgent need to find new biomarkers due its late diagnosis, is an ideal candidate for testing this technology. Expression data from two different datasets, namely Affymetrix and Illumina (18 and 36 PDAC patients, respectively), as well as from 18 healthy controls, was used for this study. A meta-analysis based on an empirical Bayesian methodology (ComBat) was then proposed to integrate these datasets. DEGs were finally identified from the integrated data by using the statistical programming language R. After our integrative meta-analysis, 5 genes were commonly identified within the individual analyses of the independent datasets. Also, 28 novel genes that were not reported by the individual analyses (‘gained’ genes) were also discovered. Several of these gained genes have been already related to other gastroenterological tumors. The proposed integrative meta-analysis has revealed novel DEGs that may play an important role in PDAC and could be potential biomarkers for diagnosing the disease.
Citation: Irigoyen A, Jimenez-Luna C, Benavides M, Caba O, Gallego J, Ortuño FM, et al. (2018) Integrative multi-platform meta-analysis of gene expression profiles in pancreatic ductal adenocarcinoma patients for identifying novel diagnostic biomarkers. PLoS ONE 13(4): e0194844. https://doi.org/10.1371/journal.pone.0194844
Editor: Surinder K. Batra, University of Nebraska Medical Center, UNITED STATES
Received: November 10, 2017; Accepted: March 9, 2018; Published: April 4, 2018
Copyright: © 2018 Irigoyen et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: The data from both microarrays reported in this paper were deposited in the Gene Expression Omnibus (GEO) database (http://www.ncbi.nlm.nih.gov/geo) with accession numbers GSE49641 and GSE74629 for the Affymetrix and Illumina platforms, respectively.
Funding: This work was supported by the Instituto de Salud Carlos III (grant number DTS15/00201 to OC), Ministerio de Economía y Competitividad (the Spanish Ministry of Economy and Competitiveness) (grant number TIN2015-71873-R to IR), Consejería de Salud, Junta de Andalucía (PIN-0474-2016 to JP), Consejería de Economía, Innovación, Ciencia y Empleo, Junta de Andalucía (P12-TIC-2082 to IR) and the University de Granada (grant number 15/13 to OC). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: I have read the journal's policy and the authors of this manuscript have the following competing interests: Manuel Benavides has received honoraria from Roche and honoraria for advisory role from Roche. Carmen Guillen-Ponce has received honoraria from Roche; honoraria for advisory role from Roche Pharma, Bayer, Merck Serono, Sanofi Aventis, Celgene y Novocure; other remuneration from Celgene, Roche Pharma, Sanofi Aventis, Merck Serono. Enrique Aranda has received honoraria for advisory role from Amgen, Bayer, Celgene, Merck, Roche, Sanofi. The other authors have stated that they have no conflicts of interest. This does not alter our adherence to PLOS ONE policies on sharing data and materials.
Introduction
Pancreatic ductal adenocarcinoma (PDAC), the most common type of pancreatic cancer (PC), is the fourth leading cause of cancer death in Western countries, with a 5-year survival rate of about 4% and a median survival rate of less than 6 months [1]. At the time of diagnosis, 80% of patients with PDAC are found to have unresectable locally advanced or metastatic disease [2]. The absence of reliable biomarkers for population screening is one of the most important limitations in the management of this malignancy [3].
Currently, the only biomarker in routine clinical use for PDAC is the carbohydrate antigen 19–9 (CA19-9) [4]. However, recent studies found this biomarker to be an unreliable diagnostic tool due to its limited sensitivity (~80%) and specificity (80–90%) [5]. Furthermore, elevated levels of CA19–9 may also appear in pancreatitis [6], benign diseases of the hepatobiliary system [7] and other malignancies of the gastrointestinal tract [8].
Microarray techniques have become a useful tool for determining gene expression profiles in cancer, allowing the discovery of possible tumor biomarkers [9]. However, sometimes biopsy from tumoral tissues can be complex and present complications. In this context, peripheral blood mononuclear cells (PBMCs) constitute an alternative, non-invasive source for finding tumor biomarkers [10,11]. These cells suffer modifications in their gene expression profile when in contact with the tumor microenvironment [12], and may therefore be used as an accessible source of cancer biomarkers.
Additionally, the so-called meta-analysis techniques have been increasingly employed to integrate data from different microarray platforms, making this technology more consistent and powerful. These meta-analyses are especially useful for combining several datasets related to the same disease when they are limited in size, therefore improving their statistical power [13]. Meta-analyses have recently been applied to identify DEGs in several tumor studies, including in breast [14,15], ovarian [16], prostate [17] and pancreatic cancers [18]. One of the main challenges in a meta-analysis is to adequately integrate datasets obtained using different platforms in order to make them comparable. Various methods have been developed to normalize datasets and provide reliable integration, removing batch effects and making cross-platform corrections, such as Distance Weighted Discrimination (DWD) [19], empirical Bayes methods (ComBat) [20], and cross-platform normalization (XPN) [21]. In this sense, ComBat and XPN have been proven to outperform DWD in term of minimizing inter-platform variance [13].
In this study, an integrated meta-analysis of two gene expression datasets from PDAC data was proposed for identifying DEGs in patients. The datasets were collected from two different microarray platforms, namely Affymetrix and Illumina. The expression data was integrated using an empirical Bayes method (ComBat) to avoid bias between the platforms.
Materials and methods
Study population
All clinical investigations were conducted according to the principles expressed in the Declaration of Helsinki. All participants gave written informed consent to participate before their enrolment in the study. The study was approved by the respective Ethics Committee at the Hospital Universitario Puerta del Mar, Hospital Germans Trias i Pujol, Complejo Hospitalario de Navarra, Hospital Reina Sofia, Hospital General de Valencia, Hospital Sant Pau, Hospital Virgen de la Salud, Hospital Parc Taulí, Hospital Universitario Ramón y Cajal, Hospital Carlos Haya, Hospital Universitario Marques de Valdecilla, Hospital General de Elche, Hospital Son Llatzer, Hospital Universitario de Donostia, and Hospital Virgen de las Nieves.
The 54 patients with unresectable PDAC recruited in this study were divided into two independent cohorts. Samples from cohort 1, selected from our previous study [22], include 18 patients with PDAC recruited from January 2009 to July 2012 at the Virgen de las Nieves University Hospital in Granada. Cohort 2 was also independent and included 36 new patients with PDAC, from a phase 2 randomized trial, recruited from March 2012 to February 2013 from 15 different hospitals mediated by the Spanish cooperative group for gastrointestinal tumor therapy (TTD). The diagnosis of PDAC was based on clinical evaluation and imaging studies, which were histologically confirmed by surgery or imaging-guided biopsy. The same enrolment criteria were applied to both cohorts. Finally, 18 gender-, age-, and habit- matched healthy controls were included. The study was approved by the Ethics Committee of the different hospitals, and all clinical investigations were conducted according to the principles expressed in the Declaration of Helsinki. Written informed consent was obtained from all patients and controls before their enrolment in the study.
Blood collection and isolation of total RNA from PBMCs
Prior to any chemotherapy regimen, peripheral blood samples (12 ml) from all patients and healthy controls were collected in PAXgene Blood RNA Tubes (PreAnalytix) and stored at room temperature for 24 hours, to achieve complete lysis of the blood cells and immediate and persistent RNA stabilization. The RNA from PBMCs was isolated using the PAXgene Blood RNA Kit (PreAnalytix) according to the manufacturer's instructions. The final concentration of purified RNA was quantified by absorbance at 260 nm in a NanoDrop 2000c spectrophotometer (Thermo Scientific). The quality was determined using the 2100 Bioanalyzer (Agilent Technologies). All samples presented an RNA integrity number (RIN) >7.0 and a 28S:18S rRNA ratio >1.0.
cDNA microarray analysis
Whole genome cDNA microarray hybridization of samples was performed using two different platforms to identify potential PDAC markers. Affymetrix microarray-based gene expression profiling was carried out on the samples from the patients included in Cohort 1 and 18 healthy controls, using GeneChip® Human Gene ST 1.0 Arrays (Affymetrix Inc.) according to the recommended protocol. Briefly, l μg of high-quality total RNA was used to synthesize double-stranded cDNA, and biotin-tagged cRNA was produced. This cRNA was recovered, purified and then hybridized to the chips overnight at 45°C. After being washed and stained, the arrays were scanned with a GeneChip Scanner 3000 7G (Affymetrix Inc.) following the manufacturer’s protocol.
The gene expression levels of multi-plat were measured using the HumanHT-12 v4 Expression BeadChip (Illumina Inc.). In addition, the expression data for the same 18 healthy controls were recalculated using Illumina technology. Both Affymetrix and Illumina expression values from healthy controls were considered for the integrative meta-analysis. Briefly, 1 μg of high-quality total RNA isolated using the Illumina TotalPrep RNA Amplification Kit (Ambion) was amplified. Then, it was reverse transcripted into first and second strand cDNA, and biotin labeled cRNA were generated following the manufacturer’s instructions. This labeled cRNA was hybridized overnight to the arrays. The beadchips were washed, stained with dye-labeled streptavidin, and scanned with an Illumina IScan to measure the intensity. The raw data images were analyzed with Illumina Genome Studio software, which generated an average probe intensity for each sample.
Data deposition: the data from both microarrays reported in this paper were deposited in the Gene Expression Omnibus (GEO) database (http://www.ncbi.nlm.nih.gov/geo) with accession numbers GSE49641 and GSE74629 for the Affymetrix and Illumina platforms, respectively.
Microarray data processing and integrative meta-analysis
All data processing and integration procedures were performed using the R statistical programming language. The data were integrated by adapting the scheme from Turbull et al. [13] with particular stages for the analysis of PDAC data in Affymetrix and Illumina. The workflow of the proposed meta-analysis is shown in Fig 1. More specifically, hybridization data from Affymetrix (Cohort 1) were first normalized using Robust Multi-array Average (RMA) analysis from the Bio-conductor R package oligo [23]. In the same way, Illumina expression data (Cohort 2) was pre-processed by applying Quantile Normalization (QN) from the R package lumi [24]. In both cases, genes with low variability expression values were discarded to reduce false-positive rates.
Data from both platforms were integrated with the virtualArray software R package [25]. This software allows data from different microarray platforms to be merged by considering several batch effect removal and cross-platform correction methods. Specifically, the data were integrated using the empirical Bayes method (ComBat) [19]. The ComBat method merges the information from several genes with similar expression distributions in each dataset to estimate the average and variance in each of those genes [26]. From the integrated data, those genes most likely to be differentially expressed in PDAC patients versus controls were selected by analyzing the gene expression microarray data with the linear models for microarray data (limma) software package [27]. The R script for the integrative meta-analysis is included as S1 File.
To validate the selected genes as PDAC biomarkers, a leave-one-out cross-validation (LOOCV) was performed with them. In this validation, one sample is consecutively discarded from the initial dataset, leaving a temporary training set and one left-out sample (test sample). This validation procedure is extensively used to assess a prediction model when no validation dataset is available.
Finally, we performed a GO enrichment analysis over the set of newly discovered genes after meta-analysis. For this purpose, an enrichment test using the Kolmogorov-Smirnov (KS) statistical test was carried out from topGO Bioconductor-R package. This analysis identified those biological functions and process that are shared by the differentially expressed genes.
Results
Patient characteristics
Our study included two independent cohorts of patients. The first group (1) included 18 PDAC patients and the independent cohort (2) comprised 36 PDAC patients. Table 1 shows the most relevant clinical characteristics of the patients from each cohort.
Cohort 1, selected from our previous study [22], consisted of 9 men (50%) and 9 women (50%) with a mean age of 61.4 (range 37–76). None of the patients had a history of chronic pancreatitis, but 7 (38.9%) had a history of type II diabetes mellitus prior to being diagnosed with PDAC. At the time of diagnosis, 12 patients (66.6%) had stage IV tumors and 6 (33.4%) presented stage III tumors.
The PDAC patients from Cohort 2 comprised 24 men (67%) and 12 women (33%) with a mean age of 60.0 (range 42–73). Only 2 patients (5.6%) had a history of chronic pancreatitis, however, 14 patients (38.9%) had a history of type II diabetes mellitus. At the time of diagnosis, all patients had stage IV tumors.
Also, 18 healthy subjects were also included in the study. The control group consisted of 10 men (55.6%) and 8 women (44.4%) with a mean age of 60.4 (age range 35–74 years); none of these subjects had a history of either chronic pancreatitis or type II diabetes mellitus.
Differential gene expression profiling of PBMCs from PDAC patients
After normalization and integration using virtual Array, the statistical differences in gene expression between the PDAC patients and healthy controls were analyzed with limma software. The data were integrated following the ComBat approach in order to reduce the batch effect produced amongst arrays. The effectiveness of the ComBat method in our integration for batch removal can be confirmed according to the comparative boxplots and density plot at S1 Fig. From this meta-analysis, 72 genes were consistently identified as being differentially expressed (p<0.01) with at least a 1.5-fold differential expression between the groups. Of these 72 genes, 39 were overexpressed and 33 repressed (Table 2 and S1–S3 Tables).
The meta-analysis findings were also compared with those obtained by individual analyses in both datasets to evaluate bias and reproducibility across the microarray studies. As a result, 14 genes already identified in the Affymetrix study were also highlighted by our meta-analysis, whereas 35 genes were shared with the Illumina study (Fig 2). Five of these genes were consistently identified by the three studies (Affymetrix, Illumina and integrated meta-analysis) (Table 2). Also ROC curves and areas under the curve (AUC) metrics were calculated for those 5 genes (Fig 3). Finally, a leave-one-out cross-validation was performed over these genes to demonstrate their predictive power. The accuracy values (sensitivity/specificity) obtained from this cross-validation is shown in Table 3.
a Coincident genes in the three analyzes: Affymetrix, Illumina and integrated meta-analysis (Table 2). b Remaining differentially expressed genes in individual Illumina and the integrative meta-analysis (S1 Table). c Remaining differentially expressed genes in individual Affymetrix and the integrative meta-analysis (S2 Table). d Differentially expressed genes in the integrative meta-analysis but not in individual analysis (gained genes) (S3 Table).
Curves are provided for both Illumina and Affymetrix individual analyses as well as our integrative meta-analysis. The Area Under the Curve (AUC) metrics are also provided for each curve.
Additionally, 28 gained genes were found. Gained genes are those identified as differentially expressed in the meta-analysis but not in the individual studies. These genes may be only weakly relevant individually but provide more consistent expression patterns when several datasets are integrated [28]. In order to determine the predictive power of these gained genes, their individual ROC curves were also studied (S3 Fig). Therefore, each individual gene was able to discriminate between PDAC patients and healthy controls with an average sensitivity and specificity of 74.8% and 73.3%, respectively. The same prediction analysis was performed combining the 5 commonly expressed genes as well as the 28 gained genes (S4 Fig). In this case, the sensitivity and specificity results reached the 100% and 94% for the 5 commons genes and 91% and 87% for the 28 gained genes.
Finally, we applied a GO enrichment analysis for the 28 gained genes. A total of 12 biological processes were found to be significant across these genes (Table 4).
The Kolmogorov-Smirnov statistical test was performed to determine their significance (p-value < 0.05).
Discussion
Affymetrix GeneChips and Illumina BeadChips are the main platforms used for gene expression microarrays. However, non-trivial systematic bias (batch effects) can occur in both making it necessary to use appropriate correction methods when integrating the datasets from the two technologies [13]. Also, differences in sequences and the number of probes make it even more difficult to integrate their datasets. Consequently, a complex integration method is mandatory in order to successfully perform consistent meta-analyses.
Several batch correction and cross-platform normalization approaches have been proposed for this purpose, including mean-centering (MC), DWD, and empirical Bayesian (ComBat) method. Even though the three proposals were compared for this integrated meta-analysis, the ComBat approach was finally selected. ComBat has been highly recommended in the literature due to its reduced computational cost and the fact it is independent of sample size [26]. It has also proven to be useful in reducing inter-platform variance, outperforming other similar approaches such as DWD or MC (see S2 Fig) [13]. Nevertheless, it is important to highlight that this methodology is still being carefully revised. Thus, novel alternatives are continually being proposed in the literature trying to correct bias more efficiently among disease samples, for instance, using co-normalization of control samples [29] or combining with other normalization approach like LOESS, SVN or QN [30,31].
Also, given that the multi-platform integration with virtualArray is based on ExpressionSet format, the proposed meta-analysis could be easily extended to integratditionale other data sources like RNA-Seq from next-generation sequencing technologies [25]. In fact, the widely used RNA-Seq expression analysis with the R package DESeq [32] already applies variance-stabilizing transformation to convert and normalize raw count values to ExpressionSet format.
Other similar meta-analyses have already been carried out to identify biomarkers in pancreatic cancers from several microarray datasets [18]. Nevertheless, these solutions provide DEGs merely by statistically determining the intersection between datasets. In contrast, a more thorough integrative approach including batch correction and cross-platform normalization is proposed in this work.
After this integration, 5 genes, namely Fas apoptotic inhibitory molecule 3 (FAIM3 or TOSO), IL-1 Receptor-Associated Kinase 3 (IRAK3), DENN/MADD Domain Containing 2D gene (DENND2D), Phospholipase B Domain Containing 1 (PLBD1) and 1-Acylglycerol-3-Phosphate O-Acyltransferase 9 (AGPAT9 or MAG-1), were identified as being commonly differentially expressed by the individual analyses in Affymetrix and Illumina as well as by the integrated meta-analysis. These genes were shown to be potential predictors for PDAC diagnosis given they showed areas under the curve (AUC) metrics higher than 0.9 for their corresponding ROC curves (Fig 3). Therefore, these genes were considered reliable targets since they showed consistent differential expression in the integrated analysis and higher predictive metrics. In fact, IRAK-3 has already been studied and validated by RT-qPCR in our previous study using Affymetrix [22]. Also, the other three genes validated in Affymetrix, namely ANKRD22, CLEC4D and VNN1 were similarly identified in the proposed meta-analysis.
More specifically, our results showed downregulation of the gene FAIM3, which plays an important role in the immune system as it encodes an Fc receptor for immunoglobulins (Ig), M. Fc receptors specifically bind to the Fc region of Igs to mediate the unique functions of each class [33,34]. The expression of FAIM3 is reported in peripheral blood leukocytes and detected in high levels in chronic lymphocytic leukemia cells [35]. It has been demonstrated that a decrease in FAIM3 expression results in increased apoptosis, however, increased FAIM3 expression resulting from CD25 antibody treatment protects T cells from IL-2-mediated activation-induced cell death (AICD) [36] underlining an involvement in the immune process.
The upregulation of the gene IRAK3 may provide a clue about the mechanisms leading to immune evasion by tumor cells. This gene is expressed in monocytes and macrophages [37] and can be triggered by Toll-like receptors (TLRs) [38,39], which are expressed in various types of cancer [40]. Overall, IRAK3 activation leads to immunosuppression [41] and allows the communication between tumor cells and macrophages facilitating cancer progression and a favorable microenvironment for the tumor [42,43]. In fact, monocytes from chronic myeloid leukemia and metastatic cancer patients present IRAK3 upregulation, leading to tumor formation and growth [44]. In this sense, a study with mouse models carried out by Rothschild et al. [45] demonstrated the connection between IRAK3 expression and both inflammation and colorectal cancer.
The DENND2D gene, another modified gene related to the immune system, has been suggested as a tumor suppressor gene [46]. DENN-domain proteins are differentially expressed in normal and neoplastic cells and regulate Rab GTPases, which play important roles in differentiation, proliferation processes, and regulation of cancer cells, among other things [47,48]. DENND2D has been proposed to suppress the tumorigenicity and proliferation of lung cancer cells [49,50]. In addition, the DENND2D mRNA expression level has been found to be significantly lower in esophageal squamous cell carcinoma tissues, hepatocellular carcinoma [51], lung cancers, immortalized bronchial epithelial cell lines and other precancerous lesions [46,50].
In our study, the PLBD1 gene expression level coding was shown to be elevated in PDAC patients. This gene is highly expressed in neutrophils and monocytes [52] and members of this family have been related to antibacterial defense [53].
Metastatic ability is one of the major problems associated with pancreatic cancer. In this regard, our study reveals the overexpression of the AGPAT9 gene, which has been associated with the metastatic process in lung cancer [54,55]. Various important functions of AGPAT9 have been described in this metastatic process. First, AGPAT9 is involved in the adaptation to the microenvironment, regulating the metabolism and hypoxia, and contributing to vascular development increasing the expression of VEGF. Furthermore, AGPAT9 is involved in mTOR pathway activation which is key in the metastatic process [56].
Additionally, 28 novel gained genes were found to have more robust patterns in the meta-analysis than in individual studies, making them statistically more significant as possible biomarkers (S3 Table).
Upregulated Annexin A3 (ANXA3) and downregulated Membrane-Spanning 4-Domains Subfamily A Member 1 (MS4A1) were novel gained genes discovered using this technique. These results are supported by Baine et al., who also included both genes as part of a predictor set of biomarkers in the PBMC of PC patients [57]. Also, Haptoglobin (HP) and Lipocalin 2 (LCN2) appeared upregulated in this new set of genes. The presence of fucosylated HP in serum has been associated to many cancers including hepatocellular, gastric and colon cancers, but the highest incidence has been observed in PC, mainly at an advanced stage [58]. Increased LCN2 levels have been related to the epithelial to mesenchymal transition [59] and proposed as a serum marker for familial PC [60]. Moreover, we observed the upregulation of other genes like CD177 Molecule (CD177), Phospholipid Scramblase 1 (PLSCR1), Secretory Leukocyte Peptidase Inhibitor (SLPI), S100 Calcium Binding Protein A12 (S100A12) and Integrin Beta 3 (ITGB3), all of them related to the development of different gastrointestinal tumors [61–66].
It is also noteworthy that all the novel genes that appeared downregulated are associated with the immune response: Granulysin (GNLY) functions as a chemotactic for T-lymphocytes, monocytes and other inflammatory cells [67]; Natural Killer Cell Granule Protein 7 (NKG7) is expressed in several cell types, including NK and T-cells [68]; C-type Lectin Domain Family 2, Member D (CLEC2D) is a receptor present in NK cells [69]; TXK Tyrosine Kinase (TKX) takes part in the Th1 cytokine production and is implicated in the adaptive immune response [70]; and RAS Guanyl Releasing Protein 1 (RASGRP1) has been found to play an important role in T-cell development [71].
Conclusions
An innovative meta-analysis has been performed to combine two gene expression datasets containing PDAC data and identify robust DEGs in these patients. Integrative meta-analyses have been shown to be powerful tools for identifying more robust DEGs when working with different data sources. Thus, an empirical Bayes approach (ComBat) has been employed in this study to integrate data from two different microarray technologies, namely Affymetrix GeneChip® Human Gene ST 1.0 Arrays and Illumina HumanHT-12 v4 Expression BeadChip, removing the batch effect between technologies and increasing the statistical significance of the subsequent analysis. The integrative analysis has confirmed the DEGs previously published for the Affymetrix data but has also located a set of gained genes that were not robust enough to be identified in the individual analyses. Thus, most of the genes identified have already been annotated as biomarkers in PDAC whereas other gained genes observed in this meta-analysis have also been related to several gastroenterological cancers. The proposed method has therefore been proven useful for more in-depth analysis of heterogeneous expression datasets, improving the identification of DEGs and discovering novel potential biomarkers for diagnosing PDAC. Future RT-qPCR studies will be performed to validate the gained genes that are considered interesting for this purpose. The proposed meta-analysis is also planned to be extended using RNA-Seq data from additional PDAC samples.
Supporting information
S1 File. R Script including code used to obtain results showed in this paper for the integrative meta-analysis.
https://doi.org/10.1371/journal.pone.0194844.s001
(ZIP)
S1 Fig. Graphical analysis of the batch analysis removal.
(A) Boxplots for the gene expression distributions in Cohort 1 (Affymetrix), Cohort 2 (Illumina) and healthy controls before applying ComBat batch removal. (B) Same boxplots after ComBat batch removal. The distributions show the normalization and reduction of technical differences between cohorts. (C) Density plot and standard deviation of expression across arrays after integration. The red dotted line indicates the median of the standard deviation. An approximately horizontal red line indicates an effective removal of bias and batch effects among arrays.
https://doi.org/10.1371/journal.pone.0194844.s002
(PNG)
S2 Fig. Comparison of batch removal method.
(A) Boxplots and standard deviation of expression after applying the mean-centering (MC) method. (B) Boxplots and standard deviation after applying the distance discretization method. Although differences cannot be appreciated in boxplots, the median of the standard deviation (red dotted line) indicated a slightly better linearity in ComBat method (see S1 Fig). Additionally, the median standard deviation is also clearly lower for ComBat batch removal.
https://doi.org/10.1371/journal.pone.0194844.s003
(PNG)
S3 Fig. Individual ROC curve for the 28 gained genes.
ROC curves for the gained genes. The area under the curve (AUC) is performed to estimate the predictive power of each gene. A cut-off is determined to optimize the discrimination between PDAC patients and healthy controls. The corresponding specificity and sensitivity values are calculated accordingly.
https://doi.org/10.1371/journal.pone.0194844.s004
(PDF)
S4 Fig. ROC curves for combined genes.
(A) The ROC curve and its corresponding AUC, sensitivity and specificity are obtained for the combination of the 5 genes shared by the three studies (Illumina, Affymetrix and meta-analysis). (B) The ROC curve as well as AUC, sensitivity and specificity values is also obtained for the combination of the 28 gained genes.
https://doi.org/10.1371/journal.pone.0194844.s005
(PNG)
S1 Table. Remaining differentially expressed genes in individual Illumina and the integrative meta-analysis.
https://doi.org/10.1371/journal.pone.0194844.s006
(PDF)
S2 Table. Remaining differentially expressed genes in individual Affymetrix and the integrative meta-analysis.
https://doi.org/10.1371/journal.pone.0194844.s007
(PDF)
S3 Table. Differentially expressed genes in the integrative meta-analysis but not in individual analysis (gained genes).
https://doi.org/10.1371/journal.pone.0194844.s008
(PDF)
References
- 1. Siegel RL, Miller KD, Jemal A. Cancer statistics, 2015. CA Cancer J Clin. 2015;65(1):5–29. pmid:25559415
- 2. Hidalgo M. Pancreatic cancer. N Engl J Med. 2010;362(17):1605–17. pmid:20427809
- 3. Chan A, Diamandis EP, Blasutig IM. Strategies for discovering novel pancreatic cancer biomarkers. J Proteomics. 2013;81:126–34. pmid:23026552
- 4. Ducreux M, Cuhna AS, Caramella C, Hollebecque A, Burtin P, Goere D, et al. Cancer of the pancreas: ESMO Clinical Practice Guidelines for diagnosis, treatment and follow-up. Ann Oncol. 2015;26 Suppl 5:v56–68. pmid:26314780
- 5. Goonetilleke KS, Siriwardena AK. Systematic review of carbohydrate antigen (CA 19–9) as a biochemical marker in the diagnosis of pancreatic cancer. Eur J Surg Oncol. 2007;33(3):266–70. pmid:17097848
- 6. Steinberg W. The clinical utility of the CA 19–9 tumor-associated antigen. Am J Gastroenterol. 1990;85(4):350–5. pmid:2183589
- 7. O'Brien DP, Sandanayake NS, Jenkinson C, Gentry-Maharaj A, Apostolidou S, Fourkala EO, et al. Serum CA19-9 is significantly upregulated up to 2 years before diagnosis with pancreatic cancer: implications for early disease detection. Clin Cancer Res. 2015;21(3):622–31. pmid:24938522
- 8. Duffy MJ, Sturgeon C, Lamerz R, Haglund C, Holubec VL, Klapdor R, et al. Tumor markers in pancreatic cancer: a European Group on Tumor Markers (EGTM) status report. Ann Oncol. 2010;21(3):441–7. pmid:19690057
- 9. Sotiriou C, Piccart MJ. Taking gene-expression profiling to the clinic: when will molecular signatures become relevant to patient care? Nat Rev Cancer. 2007;7(7):545–53. pmid:17585334
- 10. Huang H, Dong X, Kang MX, Xu B, Chen Y, Zhang B, et al. Novel blood biomarkers of pancreatic cancer-associated diabetes mellitus identified by peripheral blood-based gene expression profiles. Am J Gastroenterol. 2010;105(7):1661–9. pmid:20571492
- 11. Tanday S. Biomarkers in blood could help to detect pancreatic cancer. Lancet Oncol. 2014;15(3):e108.
- 12. Liotta LA, Ferrari M, Petricoin E. Clinical proteomics: written in blood. Nature. 2003;425(6961):905. pmid:14586448
- 13. Turnbull AK, Kitchen RR, Larionov AA, Renshaw L, Dixon JM, Sims AH. Direct integration of intensity-level data from Affymetrix and Illumina microarrays improves statistical power for robust reanalysis. BMC Med Genomics. 2012;5:35. pmid:22909195
- 14. Pavlou MP, Dimitromanolakis A, Martinez-Morillo E, Smid M, Foekens JA, Diamandis EP. Integrating meta-analysis of microarray data and targeted proteomics for biomarker identification: application in breast cancer. J Proteome Res. 2014;13(6):2897–909. pmid:24799281
- 15. Yasrebi H. Comparative study of joint analysis of microarray gene expression data in survival prediction and risk assessment of breast cancer patients. Brief Bioinform. 2016;17(5):771–85. pmid:26504096
- 16. Waldron L, Haibe-Kains B, Culhane AC, Riester M, Ding J, Wang XV, et al. Comparative meta-analysis of prognostic gene signatures for late-stage ovarian cancer. J Natl Cancer Inst. 2014;106(5). pmid:24700801
- 17. Pashaei E, Ahmady M, Ozen M, Aydin N. Meta-analysis of miRNA expression profiles for prostate cancer recurrence following radical prostatectomy. PLoS One. 2017;12(6):e0179543. pmid:28651018
- 18. Goonesekere NC, Wang X, Ludwig L, Guda C. A meta analysis of pancreatic microarray datasets yields new targets as cancer genes and biomarkers. PLoS One. 2014;9(4):e93046. pmid:24740004
- 19. Benito M, Parker J, Du Q, Wu J, Xiang D, Perou CM, et al. Adjustment of systematic microarray data biases. Bioinformatics. 2004;20(1):105–14. pmid:14693816
- 20. Johnson WE, Li C, Rabinovic A. Adjusting batch effects in microarray expression data using empirical Bayes methods. Biostatistics. 2007;8(1):118–27. pmid:16632515
- 21. Shabalin AA, Tjelmeland H, Fan C, Perou CM, Nobel AB. Merging two gene-expression studies via cross-platform normalization. Bioinformatics. 2008;24(9):1154–60. pmid:18325927
- 22. Caba O, Prados J, Ortiz R, Jimenez-Luna C, Melguizo C, Alvarez PJ, et al. Transcriptional profiling of peripheral blood in pancreatic adenocarcinoma patients identifies diagnostic biomarkers. Dig Dis Sci. 2014;59(11):2714–20. pmid:25069573
- 23. Carvalho BS, Irizarry RA. A framework for oligonucleotide microarray preprocessing. Bioinformatics. 2010;26(19):2363–7. pmid:20688976
- 24. Du P, Kibbe WA, Lin SM. lumi: a pipeline for processing Illumina microarray. Bioinformatics. 2008;24(13):1547–8. pmid:18467348
- 25. Heider A, Alt R. virtualArray: a R/bioconductor package to merge raw data from different microarray platforms. BMC Bioinformatics. 2013;14:75. pmid:23452776
- 26. Lazar C, Meganck S, Taminau J, Steenhoff D, Coletta A, Molter C, et al. Batch effect removal methods for microarray gene expression data integration: a survey. Brief Bioinform. 2013;14(4):469–90. pmid:22851511
- 27.
Smyth G. Limma: linear models for microarray data. In:Gentleman R, Carey V, Huber W, Irizarry R, Dudoit S, editors. Bioinformatics and Computational Biology Solutions using R and Bioconductor. New York: Springer; 2005. p. 397–420.
- 28. Toro-Dominguez D, Carmona-Saez P, Alarcon-Riquelme ME. Shared signatures between rheumatoid arthritis, systemic lupus erythematosus and Sjogren's syndrome uncovered through gene expression meta-analysis. Arthritis Res Ther. 2014;16(6):489. pmid:25466291
- 29. Sweeney TE, Wong HR, Khatri P. Robust classification of bacterial and viral infections via integrated host gene expression diagnostics. Sci Transl Med. 2016;8(346):346ra91. pmid:27384347
- 30. Muller C, Schillert A, Rothemeier C, Tregouet DA, Proust C, Binder H, et al. Removing Batch Effects from Longitudinal Gene Expression—Quantile Normalization Plus ComBat as Best Approach for Microarray Transcriptome Data. PLoS One. 2016;11(6):e0156594. pmid:27272489
- 31. Delfani P, Dexlin Mellby L, Nordstrom M, Holmer A, Ohlsson M, Borrebaeck CA, et al. Technical Advances of the Recombinant Antibody Microarray Technology Platform for Clinical Immunoproteomics. PLoS One. 2016;11(7):e0159138. pmid:27414037
- 32. Anders S, Huber W. Differential expression analysis for sequence count data. Genome Biol. 2010;11(10):R106. pmid:20979621
- 33. Kubagawa H, Carroll MC, Jacob CO, Lang KS, Lee KH, Mak T, et al. Nomenclature of Toso, Fas apoptosis inhibitory molecule 3, and IgM FcR. J Immunol. 2015;194(9):4055–7. pmid:25888699
- 34. Lang KS, Lang PA, Meryk A, Pandyra AA, Boucher LM, Pozdeev VI, et al. Involvement of Toso in activation of monocytes, macrophages, and granulocytes. Proc Natl Acad Sci U S A. 2013;110(7):2593–8. pmid:23359703
- 35. Vire B, David A, Wiestner A. TOSO, the Fcmicro receptor, is highly expressed on chronic lymphocytic leukemia B cells, internalizes upon IgM binding, shuttles to the lysosome, and is downregulated in response to TLR activation. J Immunol. 2011;187(8):4040–50. pmid:21908732
- 36. Richter GH, Mollweide A, Hanewinkel K, Zobywalski C, Burdach S. CD25 blockade protects T cells from activation-induced cell death (AICD) via maintenance of TOSO expression. Scand J Immunol. 2009;70(3):206–15. pmid:19703010
- 37. Kumar Pachathundikandi S, Brandt S, Madassery J, Backert S. Induction of TLR-2 and TLR-5 expression by Helicobacter pylori switches cagPAI-dependent signalling leading to the secretion of IL-8 and TNF-alpha. PLoS One. 2011;6(5):e19614. pmid:21573018
- 38. O'Neill LA. The interleukin-1 receptor/Toll-like receptor superfamily: 10 years of progress. Immunol Rev. 2008;226:10–8. pmid:19161412
- 39. Park JS, Gamboni-Robertson F, He Q, Svetkauskaite D, Kim JY, Strassheim D, et al. High mobility group box 1 protein interacts with multiple Toll-like receptors. Am J Physiol Cell Physiol. 2006;290(3):C917–24. pmid:16267105
- 40. Jain A, Kaczanowska S, Davila E. IL-1 Receptor-Associated Kinase Signaling and Its Role in Inflammation, Cancer Progression, and Therapy Resistance. Front Immunol. 2014;5:553. pmid:25452754
- 41. Zhou H, Yu M, Fukuda K, Im J, Yao P, Cui W, et al. IRAK-M mediates Toll-like receptor/IL-1R-induced NFkappaB activation and cytokine production. EMBO J. 2013;32(4):583–96. pmid:23376919
- 42. del Fresno C, Otero K, Gomez-Garcia L, Gonzalez-Leon MC, Soler-Ranger L, Fuentes-Prior P, et al. Tumor cells deactivate human monocytes by up-regulating IL-1 receptor associated kinase-M expression via CD44 and TLR4. J Immunol. 2005;174(5):3032–40. pmid:15728517
- 43. Soares-Schanoski A, Jurado T, Cordoba R, Siliceo M, Fresno CD, Gomez-Pina V, et al. Impaired antigen presentation and potent phagocytic activity identifying tumor-tolerant human monocytes. Biochem Biophys Res Commun. 2012;423(2):331–7. pmid:22659741
- 44. Kuo CC, Shih YL, Su HY, Yan MD, Hsieh CB, Liu CY, et al. Methylation of IRAK3 is a novel prognostic marker in hepatocellular carcinoma. World J Gastroenterol. 2015;21(13):3960–9. pmid:25852282
- 45. Rothschild DE, Zhang Y, Diao N, Lee CK, Chen K, Caswell CC, et al. Enhanced Mucosal Defense and Reduced Tumor Burden in Mice with the Compromised Negative Regulator IRAK-M. EBioMedicine. 2017;15:36–47. pmid:27939424
- 46. Hibino S, Kanda M, Oya H, Takami H, Shimizu D, Nomoto S, et al. Reduced expression of DENND2D through promoter hypermethylation is an adverse prognostic factor in squamous cell carcinoma of the esophagus. Oncol Rep. 2014;31(2):693–700. pmid:24317529
- 47. Subramani D, Alahari SK. Integrin-mediated function of Rab GTPases in cancer progression. Mol Cancer. 2010;9:312. pmid:21143914
- 48. Cheng KW, Lahad JP, Kuo WL, Lapuk A, Yamada K, Auersperg N, et al. The RAB25 small GTPase determines aggressiveness of ovarian and breast cancers. Nat Med. 2004;10(11):1251–6. pmid:15502842
- 49. Marat AL, Dokainish H, McPherson PS. DENN domain proteins: regulators of Rab GTPases. J Biol Chem. 2011;286(16):13791–800. pmid:21330364
- 50. Ling B, Zheng H, Fu G, Yuan J, Shi T, Chen S, et al. Suppression of non-small cell lung cancer proliferation and tumorigenicity by DENND2D. Lung Cancer. 2013;79(2):104–10. pmid:23182661
- 51. Kanda M, Nomoto S, Oya H, Takami H, Hibino S, Hishida M, et al. Downregulation of DENND2D by promoter hypermethylation is associated with early recurrence of hepatocellular carcinoma. Int J Oncol. 2014;44(1):44–52. pmid:24189587
- 52. Xu S, Zhao L, Larsson A, Venge P. The identification of a phospholipase B precursor in human neutrophils. FEBS J. 2009;276(1):175–86. pmid:19019078
- 53. Koduri RS, Gronroos JO, Laine VJ, Le Calvez C, Lambeau G, Nevalainen TJ, et al. Bactericidal properties of human and murine groups I, II, V, X, and XII secreted phospholipases A(2). J Biol Chem. 2002;277(8):5849–57. pmid:11694541
- 54. Jiang Y, Zhang W, Kondo K, Klco JM, St Martin TB, Dufault MR, et al. Gene expression profiling in a renal cell carcinoma cell line: dissecting VHL and hypoxia-dependent pathways. Mol Cancer Res. 2003;1(6):453–62. pmid:12692265
- 55. Zhang J, Meng Y, Lin H, Liu G, Wang Y, Chen H, et al. [Promotion of MAG-1 on Metastasis of Lung Cancer Cells in vitro and Its Expression in Lung Cancer Tissue of 24 Cases.]. Zhongguo Fei Ai Za Zhi. 2009;12(2):93–9. pmid:20716399
- 56. Wang Y, Jia H, Lin H, Tan X, Du Z, Chen H, et al. Metastasis-associated gene, mag-1 improves tumour microenvironmental adaptation and potentiates tumour metastasis. J Cell Mol Med. 2012;16(12):3037–51. pmid:22985252
- 57. Baine MJ, Chakraborty S, Smith LM, Mallya K, Sasson AR, Brand RE, et al. Transcriptional profiling of peripheral blood mononuclear cells in pancreatic cancer patients identifies novel genes with potential diagnostic utility. PLoS One. 2011;6(2):e17014. pmid:21347333
- 58. Okuyama N, Ide Y, Nakano M, Nakagawa T, Yamanaka K, Moriwaki K, et al. Fucosylated haptoglobin is a novel marker for pancreatic cancer: a detailed analysis of the oligosaccharide structure and a possible mechanism for fucosylation. Int J Cancer. 2006;118(11):2803–8. pmid:16385567
- 59. Rodvold JJ, Mahadevan NR, Zanetti M. Lipocalin 2 in cancer: when good immunity goes bad. Cancer Lett. 2012;316(2):132–8. pmid:22075378
- 60. Slater EP, Fendrich V, Strauch K, Rospleszcz S, Ramaswamy A, Matthai E, et al. LCN2 and TIMP1 as Potential Serum Markers for the Early Detection of Familial Pancreatic Cancer. Transl Oncol. 2013;6(2):99–103. pmid:23544163
- 61. Toyoda T, Tsukamoto T, Yamamoto M, Ban H, Saito N, Takasu S, et al. Gene expression analysis of a Helicobacter pylori-infected and high-salt diet-treated mouse gastric tumor model: identification of CD177 as a novel prognostic factor in patients with gastric cancer. BMC Gastroenterol. 2013;13:122. pmid:23899160
- 62. Fan CW, Chen CY, Chen KT, Shen CR, Kuo YB, Chen YS, et al. Blockade of phospholipid scramblase 1 with its N-terminal domain antibody reduces tumorigenesis of colorectal carcinomas in vitro and in vivo. J Transl Med. 2012;10:254. pmid:23259795
- 63. Guo J, Li G, Zhuang J, Ji C, Liu F, Tao G, et al. [Expression and clinical significance of secretory leucocyte protease inhibitor in colon carcinoma]. Nan Fang Yi Ke Da Xue Xue Bao. 2013;33(6):898–901. pmid:23803207
- 64. Thierolf M, Hagmann ML, Pfeffer M, Berntenis N, Wild N, Roessler M, et al. Towards a comprehensive proteome of normal and malignant human colon tissue by 2-D-LC-ESI-MS and 2-DE proteomics and identification of S100A12 as potential cancer biomarker. Proteomics Clin Appl. 2008;2(1):11–22. pmid:21136775
- 65. Lei Y, Huang K, Gao C, Lau QC, Pan H, Xie K, et al. Proteomics identification of ITGB3 as a key regulator in reactive oxygen species-induced migration and invasion of colorectal cancer cells. Mol Cell Proteomics. 2011;10(10):M110 005397. pmid:21622897
- 66. Du XY, Liu X, Wang ZJ, Wang YY. SLPI promotes the gastric cancer growth and metastasis by regulating the expression of P53, Bcl-2 and Caspase-8. Eur Rev Med Pharmacol Sci. 2017;21(7):1495–501. pmid:28429358
- 67. Krensky AM, Clayberger C. Biology and clinical relevance of granulysin. Tissue Antigens. 2009;73(3):193–8. pmid:19254247
- 68. Turman MA, Yabe T, McSherry C, Bach FH, Houchins JP. Characterization of a novel gene (NKG7) on human chromosome 19 that is expressed in natural killer cells and T cells. Hum Immunol. 1993;36(1):34–40. pmid:8458737
- 69. Germain C, Meier A, Jensen T, Knapnougel P, Poupon G, Lazzari A, et al. Induction of lectin-like transcript 1 (LLT1) protein cell surface expression by pathogens and interferon-gamma contributes to modulate immune responses. J Biol Chem. 2011;286(44):37964–75. pmid:21930700
- 70. Tavares TS, Nanus D, Yang XJ, Gudas LJ. Gene microarray analysis of human renal cell carcinoma: the effects of HDAC inhibition and retinoid treatment. Cancer Biol Ther. 2008;7(10):1607–18. pmid:18769122
- 71. Ksionda O, Limnander A, Roose JP. RasGRP Ras guanine nucleotide exchange factors in cancer. Front Biol (Beijing). 2013;8(5):508–32. pmid:24744772