Correction
2 Sep 2015: Romero-Cordoba S, Rodriguez-Cuevas S, Rebollar-Vega R, Quintanar-Jurado V, Maffuz-Aziz A, et al. (2015) Correction: Identification and Pathway Analysis of microRNAs with No Previous Involvement in Breast Cancer. PLOS ONE 10(9): e0137738. https://doi.org/10.1371/journal.pone.0137738 View correction
Figures
Abstract
microRNA expression signatures can differentiate normal and breast cancer tissues and can define specific clinico-pathological phenotypes in breast tumors. In order to further evaluate the microRNA expression profile in breast cancer, we analyzed the expression of 667 microRNAs in 29 tumors and 21 adjacent normal tissues using TaqMan Low-density arrays. 130 miRNAs showed significant differential expression (adjusted P value = 0.05, Fold Change = 2) in breast tumors compared to the normal adjacent tissue. Importantly, the role of 43 of these microRNAs has not been previously reported in breast cancer, including several evolutionary conserved microRNA*, showing similar expression rates to that of their corresponding leading strand. The expression of 14 microRNAs was replicated in an independent set of 55 tumors. Bioinformatic analysis of mRNA targets of the altered miRNAs, identified oncogenes like ERBB2, YY1, several MAP kinases, and known tumor-suppressors like FOXA1 and SMAD4. Pathway analysis identified that some biological process which are important in breast carcinogenesis are affected by the altered microRNA expression, including signaling through MAP kinases and TP53 pathways, as well as biological processes like cell death and communication, focal adhesion and ERBB2-ERBB3 signaling. Our data identified the altered expression of several microRNAs whose aberrant expression might have an important impact on cancer-related cellular pathways and whose role in breast cancer has not been previously described.
Citation: Romero-Cordoba S, Rodriguez-Cuevas S, Rebollar-Vega R, Quintanar-Jurado V, Maffuz-Aziz A, Jimenez-Sanchez G, et al. (2012) Identification and Pathway Analysis of microRNAs with No Previous Involvement in Breast Cancer. PLoS ONE 7(3): e31904. https://doi.org/10.1371/journal.pone.0031904
Editor: Joaquín Dopazo, Centro de Investigación Príncipe Felipe, Spain
Received: November 1, 2011; Accepted: January 15, 2012; Published: March 16, 2012
Copyright: © 2012 Romero-Cordoba 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.
Funding: This work was funded by the National Institute of Genomic Medicine (INMEGEN/CI/019/2009). S. Romero-Cordoba, RRV, and RAL received a PhD scholarship from the Mexican National Council of Science and Technology. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: The authors have declared that no competing interests exist.
Introduction
microRNAs (miRNAs) are small non coding RNAs which regulate the expression of coding genes at a post-transcriptional level through inhibition and destabilization of messenger RNAs [1]. MicroRNAs participate in diverse biological processes like cell division, proliferation, differentiation, death, growth and development, stem cell regulation, metabolism and stress response [2], [3].
Aberrant expression of microRNAs is related with the development of different pathologies, including cancer, where they can act either as tumor suppressors or as “oncomirs” (oncogenes). Approximately 50% of the microRNA genes are located in regions commonly affected by chromosomal alterations (amplification, deletions and fragile sites) in the cancer genome [4]. As in the case of messenger RNA expression patterns, microRNA expression signatures can also be used to classify human tumors and to identify molecular signatures associated with relevant clinical characteristics [5], [6]. During the biogenesis of microRNAs, an intermediate RNA duplex represents an obligatory intermediate. After the maturation process, the mature, leading strand is preferentially incorporated into the silencing complex, while the role of the other strand (miRNA*) was regarded as a simple passenger. However bioinformatic and functional analyses have identified important regulatory roles of the miRNA*, both in normal and in pathological states, including cancer [7].
In breast cancer, analysis of microRNA expression patterns has led to the identification of signatures which can differentiate tumor from normal tissues [8], [9]. Analysis of the messenger RNA targets of microRNAs with differential expression in normal and tumor breast tissues, indicates that their aberrant expression impact the regulation of important cellular networks known to drive breast cancer [10]. This is supported by the observation that several clinically relevant breast tumor features, such as tumor size, nodal involvement, vascular invasion, hormone receptor and HER2 status, are also related to the expression of particular microRNAs [8], [9], [11]. Additionally, microRNAs might be used as markers of the metastatic potential of primary breast tumors [12].
In order to further analyze the differences in microRNA expression patterns in breast tumors, we evaluated the expression profile of 667 microRNAs in 29 breast tumors compared to 21 adjacent normal tissues. We also compared the expression patterns between four fresh frozen and their corresponding paraffin embedded tumor tissue pairs, in order to evaluate the robustness of the TaqMan low density array platform as a tool for retrospective studies.
Our analyses identified 130 differentially expressed microRNAs between the tumor and normal tissues, 43 of them whose involvement in breast cancer has not been previously described, to our best knowledge. Differential expression of 14 of these microRNAs was validated in an independent set of normal and tumor samples, suggesting that their aberrant expression might play important roles in breast cancer. Several evolutionary conserved miRNA* were included in this expression signature, showing expression rates similar to their mature strand, suggesting their potential regulatory role in breast tumors.
Results
MicroRNA expression profiles in breast tumors
Comparative Ct analysis (2- ΔΔCt) was used to identify a set of 130 microRNAs that were differentially expressed between the normal and tumor tissue (adjusted P value≤0.05, fold change: 2). 17 were over-expressed and 113 were down-regulated in the tumor compared to the normal tissue (Table 1 shows the microRNAs with the highest differential expression values, a complete list including all 113 is shown in Table S1). This is in accordance with other studies performed using different types of platforms, such as bead-based flow cytometry [5] or miRNA microarrays [13].
Unsupervised hierarchical clustering analysis of the log-transformed delta Ct values of the differentially expressed microRNAs, showed that this set of markers is able to differentiate the tumors from the normal breast tissues (Figure 1, figure S1).
The heatmap (Spearman correlation, Euclidean distance) represents log transformed Delta Ct values. Heat map colors correspond to microRNA expression as indicated in the color key: red (over-expressed) and green (down-regulated). Blue line: Control samples, red line: tumor samples.
We observed an overlap between microRNAs expressed in normal and tumor samples, indicating that miRNA expression levels, rather than a differential tissue-specific pattern, is driving the separation between normal and tumor tissue.
Identification of microRNAs with previously unknown involvement in breast cancer
Out of the 130 differentially expressed microRNAs we detected in this study, 43 (30%) have not been previously reported in the literature as involved in breast cancer, to our best knowledge (Table 2). Some of these represent the passenger strand (miRNA*) of pre-miRNAs. In some cases, like miR-10b* and miR-145*, their corresponding leading strand have important, proven roles in breast cancer, but the role of the star strand has not been explored. Interestingly, for most of the microRNA* included in our profile, their guiding opposite strand is differentially expressed in the tumor tissue, in most cases with a very similar expression rate (Figure 2).
The bars show the normalized expression values of the microRNA pairs: driver (microRNA) and passenger (miRNA*) strands present in the breast cancer profile.
The published and validated transcriptional targets (oncogenes, estrogen regulators, tumor suppressors, miRNA biogenesis machinery and epigenetic master regulators) of the miRNAs with no previous involvement in breast cancer, indicate that they might have important biological implications in breast tumor biology (Table 2).
Evaluation of the reproducibility of the microRNA screening method
microRNA expression profiles were analyzed in a set of 29 breast tumor tissues compared to 21 normal, non paired, adjacent tissues (clinical characteristics of these samples is presented in Table 3). 23 tumors and two pools of normal samples were run in triplicate to assess the reproducibility of the TaqMan low-density array. The standard deviation between the technical triplicates was 0.1675507, while the Spearman Correlation was 97% (Max: 99%, Min: 96%). The Spearman correlation as well as the standard deviation values between our triplicates, showed a high correlation between each technical and biological replicates (Figure S2).
Hormone receptor status is associated to the expression of different microRNAs
Delta Ct analysis identified differential microRNA expression signatures related with ER and PR status in the tumor samples. ER+ tumors showed differential expression of miR-342-5p, miR-29c*, miR-29b-2*, miR-30e, miR-190b, miR-769-5p, miR-30d and miR-432, with a (P value ≤0.05) compared to ER- tumors (Table 4, figure S3). Differential expression of miR-145*, miR-34a* and miR-193b* (adjusted P value ≤0.05) was able to discriminate between the PR+ from the PR- samples (Figure S4).
Analysis of the miRNA/miRNA* sequence conservation
Most of the miRNA/miRNA* pairs included in the expression profile were conserved in the seed sequence region across the five different vertebrate genomes we analyzed (Figure S5). Some of the miRNA*, like miR-10b* and miR-30a* presented divergence in its sequence, reflected in the percentage of nucleotide substitutions. However, most of the miRNA/miRNA* showed a high degree of evolutionary conservation of the passenger miRNA* strand with a low percentage of nucleotide substitutions (miR-19b, miR-19b*, miR-125, miR-125*, miR-26b, miR-26b*, miR-145, miR-145*, miR-335, miR-335*, miR-214 and miR-214) (Figure S6). This analysis determined that the miRNA* detected in our analyses is both differentially expressed between the normal and tumor tissues, in most cases with a very similar expression pattern compared to the corresponding leading strand and is also evolutionary conserved, suggesting that they might have a biological role in breast cancer.
Validation of differentially expressed microRNAs in an independent set of breast tumor tissues
A set of 17 microRNAs was selected for further analysis in an independent set of samples through evaluation of their expression using independent TaqMan assays. This set included 13 microRNAs with differential expression between the tumor and normal tissues and 4 with non-differential expression. Expression profiles were concordant in 14/17 (82.3%) of the selected microRNAs, and only three failed to replicate. A similar expression value was obtained for the same microRNA in both of the TLDA and the single probe assay. (Pearson Correaltion: 97.3153%) (Table 5).
Deregulated microRNAs and their putative transcriptional targets
To define potential mRNA targets of the differentially expressed microRNAs, and their impact on cellular pathways, we performed an mRNA target prediction analysis with at least 3 different algorithms (Tables 6–7) followed by enrichment analysis of the predicted mRNA targets using Diana, mir-Path and the Reactome databases. The list of the top pathways ranked by the enrichment P-value is presented in Table 8.
Comparison of microRNA profiles between Paraffin embedded and fresh-frozen tissues
Formalin fixed paraffin embedded (FFPE) tissue represents a major source of potentially useful biological material for retrospective analysis. To determine the performance and robustness of the microRNA TLDA system in the analysis of microRNAs obtained from FFPE tissues, we compared the expression patterns of pairs of fresh-frozen and FFPE tissues from the same patient. Correlation between these results was analyzed using Spearman correlation and unsupervised hierarchical clustering. We observed cluster aggregation, as well as a high correlation value between the fresh and the FFPE tissues obtained from the same patient (average 93.75%, minimum of 90%; maximum of 98%), indicating that results obtained from RNA isolated from FFPE tissues retain the same expression signature as the fresh frozen tissue (Figure 3).
A) Unsupervised hierarchical cluster of the delta Ct values of all the analyzed microRNAs represented as a Heat Map. B) Scatter plot of the Ct values in pair-wise comparisons from fresh (T) and FFPE samples (TP).
Discussion
Differentially expressed microRNAs with no previous involvement and potential relevance in breast cancer
We have analyzed the microRNA expression patterns in a set of breast cancer tumors and compared them to normal tissue. Our analyses identified a set of differentially expressed microRNAs whose role in breast cancer has not been previously described, to our best knowledge. Several evolutionary conserved miRNA* were included in this expression signature, showing expression rates similar to their mature strand. Differential expression of a set of these microRNAS was validated in an independent set of tumor and normal breast tissues, suggesting their potential regulatory role in breast tumors.
Finally, we evaluated the performance of the TaqMan low-density array through the comparison of microRNA expression patterns obtained from fresh-frozen and FFPE tissue pairs, obtaining similar results in both cases.
Between the microRNAs without previous involvement in breast cancer, we identified the down-regulation of miR-129-3p. In mouse lung epithelial cells, lentiviral mediated expression of miR-129-3p results in G1 phase arrest and cell death through down-regulation of CDK6, ERK1 and ERK2, indicating its activity in cell proliferation. Epigenetic repression of miR-129 also leads to over-expression of the SOX1 oncogene in gastric and endometrial cancer [14]. In breast cancer, SOX1 is activated due to the loss of miR-335, which was also found down-regulated in our study (Table 1) suggesting that miR-129 might also play a role in the SOX1 mediated acquisition of metastatic capacity [12], [15].
Decreased expression of miR-215, down-regulated in our expression profile, has been associated with cell proliferation rate and has been proposed as a tumor suppressor candidate in colon cancer. miR-215 reduces cell proliferation and cell cycle G2-arrest through regulation of dihydrofolate reductase thymidylate synthase (DTL), and increased expression of TP53 and p21 [16]. DTL has been implicated in cell proliferation, cell cycle arrest and cell invasion in diverse tumor types, including hepatocellular carcinoma and breast cancer [17].
We found down-regulation of some of the miR-99 family members, including miR-99a, this has also been described in advanced prostate cancer cell lines and tissues. The direct targets of the miR-99 family are the chromatin remodeling factors SMARCA5, SMARCD1 and the growth regulatory kinase mTOR, which is also an important pathway activated in breast cancer [18]. miR-99a, has also been found downregulated in serous ovarian carcinoma [19].
Senescence represents a potent tumor suppressive mechanism, and involvement of microRNAs in this process has been already described. miR-668, down-regulated in head and neck squamous cell carcinoma cell lines and in our expression profile, has been identified as a senescence-inducing microRNAs, playing an important role not only in cancer pathogenesis, but also as an interesting target for the development of new therapeutic targets [20].
Over-expression of mir-425 has been described in several human cancer cells [21] and its pri-miRNA sequence is evolutionarily conserved in different mammals (human, mouse, dog and opossum) supporting the idea that miR-425 plays a regulatory role in eukaryotic cells [22]. TargetScan, Mirtarget2, Miranda, miRTar, PITA and RNA hybrid algorithms, predicted DICER1 and SMAD2 as potential targets of miR-425. SMAD is involved in the regulation of DROSHA, another key player in small RNA processing [23], indicating that aberrant expression of miR-425 might have important effects in the biogenesis of small RNAs.
miR-592 was over-expressed in our tumor dataset. This microRNA has been found differentially expressed between DNA mismatch repair deficient and proficient colon tumors. The interactions between miR-592 and genes associated with the mismatch repair system suggest an oncogenic role of this miRNA, possibly acting through inhibition of tumor suppressor genes [24]. Upregulation of miR-592 has also been reported as part of the microRNA signature of kidney cancer [25].
miR-877 is a DROSHA independent intronic microRNA, up-regulated in our breast tumor samples. It is part of the coding region of the ATP-binding cassette subfamily F member 1 (ABCF1), which is over expressed in breast cancer. ABCF1 is a transporter of molecules through membranes, and has been associated to drug resistance and the development of some types of cancer [26].
Together with this set of novel microRNAs, we also confirmed the differential expression of microRNAs whose role and biological targets in breast cancer have been well described (Table 5). This is the case of the down-regulation of miR-125b [13], [27], let-7 [5], [13], [28] miR-205 [29]–[31], miR-145 [13], [31], miR10b [13], miR-222 [32], miR34a [33], miR-31[34], miR-206 [12], [13], [35]; and over-expression of miR-210 [13], [36], [37] and miR-21 [13], [28], [38] (check reference [39] for a review).
As part of our expression profile we found an important presence of miRNAs*. Recent bioinformatic and experimental data show a high degree of conservation over vertebrate evolution, particularly in the seed regions of expressed miRNAs* [7]. The miRNA/miRNA* ratios also change in different developmental stages [40] and have been involved in the regulation of different biological networks in normal physiological conditions [41]. In cancer, miRNA* expression has been detected in childhood acute lymphoblastic leukemia [42], myelodysplastic syndrome [43], cell lines of tumors of the female reproductive tract [44] and melanoma [45] indicating their potential role in cellular transformation. The set of miRNA* we found differentially expressed between the normal and tumor breast tissues show a high degree of evolutionary conservation, according to our analysis in five animal genomes, as well as a similar expression rate than their corresponding leading strand. These results suggest that these miRNA* might be playing regulatory roles in breast cancer.
Breast cancer etiology includes genomic alterations that drive cancer cell development, like loss of heterozygosity, amplifications, deletions and fragile sites, which can promote oncogene activity or repress the expression of tumor suppressors [46]. More than half of the human miRNAs (60%) are located in regions commonly affected in the cancer genome, a situation that might affect their expression [4]. Our analysis identified down-regulation of 22 microRNAs located in the 14q32 region, which is deleted in approx. 10% of breast tumors [20], and has been reported as a chromosomal region where several breast cancer-related microRNAs are located [47] (Figure S7), suggesting that loss of this chromosomal region and the down-regulation of the miRNAs codified in this locus, might be correlated in a fraction of breast tumors.
Hormone receptor status is an important tumor characteristic to classify breast cancer and to determine clinical treatment. However, there is limited information about the genetic mechanisms regulating the expression of hormone receptors. We identified a set of miRNAs which can differentiate hormone receptor positive and negative tumors. Analysis of the mRNA targets of these miRNAs identified biological pathways relevant to breast cancer, like apoptosis, DNA repair, cell cycle check-points, etc. (Table 3). The miRNAs in the ER signature can directly regulate transcripts like ESR1 (miR-342-5p, miR-190b, miR-432), ESRRG (miR-30d, miR-30e), ESRRA (miR-432); ERN2 (miR-342-5p), which induces translational repression in response to ER stress, coactivators of the estrogen receptor like PELP1 (miR-342-5p) and SRC (miR-342-5p); mediators of cell cycle trough estrogen activation like E2F (miR-30d, miR-30e and miR-432) and transcription factors like DP1 (miR-30e). While miRNAs of the PR profile can regulate the activity of the progesterone receptors PRDM4 (miR-34a*), PRDM16 (miR-145*) and co-activators of the progesterone pathway like SRC (miR-34*, mir-145*) and MAPK (miR-145*, miR-193*, miR-34*). miR-190 and miR-345 have already been reported as discriminators of ER status [47], suggesting its importance in the establishment of this phenotype.
Deregulation of microRNA expression might potentially affect the regulation of multiple cancer-related genes; for this reason it's important to define the biological networks affected by differentially expressed microRNAs and their transcriptional targets. Pathway analysis of our expression profile determined different transcripts and protein-protein interactions, which can be activated or repressed by these microRNAs. Examples of these pathways are ERBB signaling, which plays a determinant role in breast cancer [48] through its contribution to tumor development, cellular transformation, involvement in the development of central nervous system metastases and targeted therapy [49]. Another important pathway affected by the differentially expressed miRNAs is the mitogen-activated protein kinase (MAPK), which is activated in breast cancer and is involved in the initiation and pathogenesis of breast tumors [50]. Analysis of the targets affected by the miRNAs with no previous relation with breast cancer included in our signature, also identified several cancer-related pathways, including KRAS, EGFR, MAPK, VEGF, ERBB, PTEN, FOS, AKT1, etc. (Table 6, table 8, figure S8).
Finally, in order to evaluate the potential application of the TLDA platform in the retrospective evaluation of miRNA expression patterns in breast cancer, we carried out a comparison between results obtained from fresh frozen and FFPE tissue. Results of the comparison showed a high correlation between the two tissues, indicating that the platform can be used in retrospective studies using FFPE tissue [51].
Materials and Methods
Breast tissue samples and RNA extraction
After obtaining the written patient's informed consent, tumor and normal adjacent breast samples were collected during surgery at the Institute of Breast Diseases (FUCAM) in Mexico City. The protocol was reviewed and approved by the Ethics and Research committees of the National Institute of Genomic Medicine and the Institute of Breast Diseases in Mexico City (CE2009/11). Tissues were macroscopically analyzed by a trained pathologist and stored at −80°C until further processing. Frozen sample sections were evaluated histologically to assure that only samples with more than 80% of tumor cells were included in our analyses. Frozen tissues were disrupted with a Tissue Ruptor (Qiagen Inc., Valencia, CA) and total RNA was obtained using the Trizol protocol (Invitrogen, Carlsbad, CA). For the FFPE tissues, total RNA was isolated with the Recoverall kit (Ambion, Austin, USA) according to the manufacture's protocol. Briefly, 10-8 µm sections were incubated in xylene for 3 minutes at 50°C for de-paraffinization, followed by two brief washes in 100% ethanol. Once ethanol was evaporated, RNA extraction was done as described in the kit's protocol. Total RNA concentration was evaluated by spectrofotometry (NanoDrop Technologies, Wilmington, Delaware). Total RNA integrity from the frozen samples was analyzed using the Agilent 2100 Bioanalyzer with the Nano-Eukariotic chip.
MicroRNA expression analysis
The Megaplex TLDA, v2.0 (TaqMan® Low Density Array, Applied Biosystems (ABI), Foster City, CA) platform was used to measure miRNA expression. There are two plates in this system: plate A, containing well-characterized and widely expressed microRNAs, while plate B presents less characterized microRNAs. The combined plates evaluate the expression of 667 unique human specific microRNAs (present in V14 of the Sanger miRBase) in parallel. Briefly, the procedure begins with the retro-transcription of 70 ng of total RNA with stem-loop primers to obtain a cDNA template. A pre-amplification step was included in order to increase the concentration of the original material and to detect microRNAs that are expressed at low levels. The pre-amplified product was loaded into the TaqMan® Low Density Arrays and amplification signal detection was carried out using the 7900 FAST real time thermal cycler (ABI). A total of 29 tumor and 21 normal samples (two pools: one containing five normal samples, other containing 12 normal samples, plus 4 independent normal samples) were analyzed. 23 tumors and the two normal pools were processed by triplicate, representing 82% of the total samples. Raw miRNA expression data is available at the Gene Expression Omnibus (GEO), with accession number GSE35412.
Statistical analysis
To determine the expression level of each miRNA, the comparative Ct (2−ΔΔCt) method was used. RNU44 and RNU48 showed the most stable expression between samples and were selected for normalization across all experiments [52]. All analyses were done using R (HTqPCR, gplots-bioconductor). The Ct raw data (fractional cycles numbers at which the fluorescence cross the threshold) was determined using an automatic baseline and a threshold of 0.2. Samples with a Ct value of <36 cycles were excluded from the analyses and the normal tissue samples were used as calibrators. A geometric mean was used to obtain the media between replicates and the outliers among replicates were excluded. A 2-fold change value obtained by the comparative Ct method (2−ΔCT) was used to determine the differentially expressed microRNAs. An adjusted t-test was used to evaluate the significance differences in the Ct values between controls and tumors as well as between hormone receptor positive and negatives tumors. Only microRNAs with an adjusted P value of 0.05, a fold change of 2 and consistent expression in at least 80% of the samples were considered as differentially expressed. Unsupervised clustering analysis, using Spearman correlation and average linkage, was used to identify different sub-groups defined by miRNA expression profiles. The rank-invariant normalized data was evaluated through Spearman correlation analysis between technical and biological replicates.
Analysis of potential mRNAs targeted by differentially expressed microRNAs
Possible mRNA targets of the differentially expressed microRNAs were identified using the mirDIP (2011) [53] and miRwalk databases (2011) [54], through an integrative evaluation with different algorithms: TargetScan v5.1 (http://www.targetscan.org), PicTar (http://pictar.mdc-berlin.de), miRanda (www.microrna.org/microrna/getGeneForm.do), and Pita (genie.weizmann.ac.il/pubs/mir07/mir07_data.html). We only considered as potential mRNA targets those who were predicted by three of these algorithms. Gene ontology and cellular pathway analysis altered by the aberrant expression of the microRNAs was done using the Reactome [55] and DIANA lab software [56], which obtained the information from TargetScan 5 (2009) [57], PicTar 4-way (2007) [58], and visualized in Wikipathways [59].
Biological replication of differentially expressed microRNAs
Independent RT-PCR analysis using specific TaqMan microRNA assays was performed to replicate the expression of 17 microRNAs (miR-10b, miR-668, miR-431, miR-136*, miR-129-3p, miR-488, miR-99a, miR25*, miR-27a, miR149*, miR-206, miR492, miR-21) in an independent set of 20 normal and 55 fresh-frozen breast tumor samples. All assays were run in duplicate. These microRNAs were chosen based on differential expression between tumors and normal tissues and statistical confidence. Of this set of miRNAs, there is no previous information regarding the expression of miR- 136*, miR-99a, miR-488 and miR-668, in breast tumors. MiR-184, miR-24, miR-492 and miR-326, whose expression did not changed significantly between tumors and normal tissues in the TLDA assay, were selected as experimental controls, while RNU-44 and RNU-48 were used as endogenous controls for normalization.
Conservation of mature miRNA/miRNA* sequence analysis
We made a comprehensive computational survey of miRNA conservation sequence across 5 animal genomes: Gasterosteus aculeatus (fish), Xenopus tropicalis (frog), Anolis carolinensis (reptile), Gallus gallus (bird), Monodelphis domestica (marsupial), Mus musculus (rodent) and Homo sapiens. We used the miROrtho database [60], to make multiple ortholog alignments and evaluate the conservation of the RNA secondary structure. For this analysis, we blasted the hairpin structure sequence of each miRNA obtained from miRBase.
Correlation between miRNA profiles in FFPE tissues and fresh frozen samples
Four fresh-frozen samples and their corresponding FFPE tissues obtained from the same patient's tumor were analyzed with the TLDA platform (plate A) to evaluate the effect of formalin fixation and paraffin embedding process on the microRNA expression patterns. Correlation between the fresh-frozen and the FFPE results was evaluated using Spearman Correlation. For all statistical analysis, the log transformed delta Ct values were used. Non-supervised clustering analysis was done using Euclidian distance and average linkage including all samples. The results were visualized in a heat map.
In conlusion, our analysis identified a set of microRNAs with no previously known involvement in breast cancer, whose altered expression target relevant cellular pathways. The identification of a set of evolutionary conserved microRNA* showing differential expression between the normal and tumor tissues interesting research opportunities to study the role of the passenger microRNA strand in cancer.
Supporting Information
Figure S1.
Principal Component analysis based in the miRNA differential expression profile. The two most informative components were plotted. Clustering of the normal tissues and tumor tissues is observed.
https://doi.org/10.1371/journal.pone.0031904.s001
(TIF)
Figure S2.
Signal correlation Plot between the biological and technical samples analyzed. A) Scarlet plots of the correlation between expression values between control samples evaluated by Spearman correlation (correlation: 100-93%) B) Scarlet plots of the correlation between expression values between breast tumor tissues (correlation: 100-84%).
https://doi.org/10.1371/journal.pone.0031904.s002
(TIF)
Figure S3.
Unsupervised hierarchical clustering using the differentially expressed miRNAs between Estrogen Receptor (ER) positive and ER negative samples. The heatmap (Spearman correlation, Euclidean distance, complete linkage) represents Delta Ct values. Heat map colors correspond to miRNA expression as indicated in the color key: red over-expressed and green down-regulated. Salmon line: ER negative and Dark blue line: ER positive.
https://doi.org/10.1371/journal.pone.0031904.s003
(TIF)
Figure S4.
Unsupervised hierarchical clustering using the differentially expressed miRNAs in Progesterone Receptor (PR) positive and PR negative samples. The heatmap (Spearman correlation, Euclidean distance, complete linkage) represents Delta Ct values. Heat map colors correspond to miRNA expression as indicated in the color key: red over-expressed and green down-regulated. Salmon line: PR negative and Dark blue line: PR positive.
https://doi.org/10.1371/journal.pone.0031904.s004
(TIF)
Figure S5.
Analysis of evolutionary conservation by multiple sequence aligments. The upper panel shows sequence alignments with the consensus hairpin sequence and the conservation profile displayed in the grey histogram. The mature miRNA sequence is underlined. The miRNA sequence is located at the left side of the aligned sequences while the miRNA* is at the right. The inferior panel shows the consensus secondary structure of the orthologous sequence. The color-coding of the nucleotide changes is shown in the box.
https://doi.org/10.1371/journal.pone.0031904.s005
(TIF)
Figure S6.
Consensus secondary structure of the orthologous sequence. Percentage of the miRNAs nucleotide subtitutions in each miRNA/miRNA* of the seed regions (2–8 nucleotide). The blue bars represents the miRNA strand, the red bars represents the miRNA* strand.
https://doi.org/10.1371/journal.pone.0031904.s006
(TIF)
Figure S7.
Chromosome 14 and policistronic miRNAs. A) Number of miRNAs included in the expression profile and their chromosomal location. Asterisks indicate chromosomes with the higher numbers of differentially expressed miRNAs. B) microRNAs with differential expression in chromosome 14. Green lines indicate the miRNAs that are included in our differential profile.
https://doi.org/10.1371/journal.pone.0031904.s007
(TIF)
Figure S8.
Gene Ontology analysis of the pathways affected by the differentially expressed novel microRNAs. Enrichment analysis made with the mRNA targets of the not previously reported miRNAs. Yellow circles indicate the pathways associated with breast cancer.
https://doi.org/10.1371/journal.pone.0031904.s008
(TIF)
Table S1.
Complete list of the 130 differentially expressed miRNAs between normal and breast tumors.
https://doi.org/10.1371/journal.pone.0031904.s009
(DOC)
Acknowledgments
We want to thank all the patients participating in the study, Esperanza Monterrubio Flores for support in the immunohistochemical analysis and Karol Carrillo and Hayde Miranda for their support in the TLDA assay.
Author Contributions
Conceived and designed the experiments: AHM S. Romero-Cordoba S. Rodriguez-Cuevas. Performed the experiments: AHM S. Romero-Cordoba RRV VQJ VBP RAL. Analyzed the data: AHM S. Romero-Cordoba VQJ VBP GJS. Contributed reagents/materials/analysis tools: AHM S. Romero-Cordoba RRV VQJ VBP RAL AMA GJS. Wrote the paper: AHM S. Romero-Cordoba.
References
- 1. Rana TM (2007) Illuminating the silence: understanding the structure and function of small RNAs. Nat Rev Mol Cell Biol 8: 23–36.
- 2. Krol J, Loedige I, Filipowicz W (2010) The widespread regulation of microRNA biogenesis, function and decay. Nat Rev Genet 11: 597–610.
- 3. Cheng AM, Byrom MW, Shelton J, Ford LP (2005) Antisense inhibition of human miRNAs and indications for an involvement of miRNA in cell growth and apoptosis. Nucleic acids research 33: 1290–1297.
- 4. Zhang L, Huang J, Yang N, Greshock J, Megraw MS, et al. (2006) microRNAs exhibit high frequency genomic alterations in human cancer. Proceedings of the National Academy of Sciences of the United States of America 103: 9136–9141.
- 5. Lu J, Getz G, Miska EA, Alvarez-Saavedra E, Lamb J, et al. (2005) MicroRNA expression profiles classify human cancers. Nature 435: 834–838.
- 6. Volinia S, Calin GA, Liu CG, Ambs S, Cimmino A, et al. (2006) A microRNA expression signature of human solid tumors defines cancer gene targets. Proc Natl Acad Sci U S A 103: 2257–2261.
- 7. Yang JS, Phillips MD, Betel D, Mu P, Ventura A, et al. (2010) Widespread regulatory activity of vertebrate microRNA* species. RNA 17: 312–326.
- 8. Iorio MV, Ferracin M, Liu CG, Veronese A, Spizzo R, et al. (2005) MicroRNA gene expression deregulation in human breast cancer. Cancer Res 65: 7065–7070.
- 9. Blenkiron C, Goldstein LD, Thorne NP, Spiteri I, Chin SF, et al. (2007) MicroRNA expression profiling of human breast cancer identifies new markers of tumor subtype. Genome Biol 8: R214.
- 10. O'Day E, Lal A (2010) MicroRNAs and their target gene networks in breast cancer. Breast Cancer Res 12: 201.
- 11. Lowery AJ, Miller N, Devaney A, McNeill RE, Davoren PA, et al. (2009) MicroRNA signatures predict oestrogen receptor, progesterone receptor and HER2/neu receptor status in breast cancer. Breast Cancer Res 11: R27.
- 12. Tavazoie SF, Alarcon C, Oskarsson T, Padua D, Wang Q, et al. (2008) Endogenous human microRNAs that suppress breast cancer metastasis. Nature 451: 147–152.
- 13. Iorio MV, Ferracin M, Liu CG, Veronese A, Spizzo R, et al. (2005) MicroRNA gene expression deregulation in human breast cancer. Cancer research 65: 7065–7070.
- 14. Huang YW, Liu JC, Deatherage DE, Luo J, Mutch DG, et al. (2009) Epigenetic repression of microRNA-129-2 leads to overexpression of SOX4 oncogene in endometrial cancer. Cancer Res 69: 9038–9046.
- 15. Negrini M, Calin GA (2008) Breast cancer metastasis: a microRNA story. Breast Cancer Res 10: 203.
- 16. Karaayvaz M, Pal T, Song B, Zhang C, Georgakopoulos P, et al. (2011) Prognostic Significance of miR-215 in Colon Cancer. Clinical colorectal cancer.
- 17. Ueki T, Nishidate T, Park JH, Lin ML, Shimo A, et al. (2008) Involvement of elevated expression of multiple cell-cycle regulator, DTL/RAMP (denticleless/RA-regulated nuclear matrix associated protein), in the growth of breast cancer cells. Oncogene 27: 5672–5683.
- 18. Sun D, Lee YS, Malhotra A, Kim HK, Matecic M, et al. (2011) miR-99 family of MicroRNAs suppresses the expression of prostate-specific antigen and prostate cancer cell proliferation. Cancer research 71: 1313–1324.
- 19. Nam EJ, Yoon H, Kim SW, Kim H, Kim YT, et al. (2008) MicroRNA expression profiles in serous ovarian carcinoma. Clinical cancer research: an official journal of the American Association for Cancer Research 14: 2690–2695.
- 20. Gao Y, Niu Y, Wang X, Wei L, Zhang R, et al. (2011) Chromosome aberrations associated with centrosome defects: a study of comparative genomic hybridization in breast cancer. Human pathology.
- 21. Landgraf P, Rusu M, Sheridan R, Sewer A, Iovino N, et al. (2007) A mammalian microRNA expression atlas based on small RNA library sequencing. Cell 129: 1401–1414.
- 22. Grinchuk OV, Jenjaroenpun P, Orlov YL, Zhou J, Kuznetsov VA (2010) Integrative analysis of the human cis-antisense gene pairs, miRNAs and their transcription regulation patterns. Nucleic acids research 38: 534–547.
- 23. Davis BN, Hilyard AC, Lagna G, Hata A (2008) SMAD proteins control DROSHA-mediated microRNA maturation. Nature 454: 56–61.
- 24. Oberg AL, French AJ, Sarver AL, Subramanian S, Morlan BW, et al. (2011) miRNA expression in colon polyps provides evidence for a multihit model of colon cancer. PloS one 6: e20465.
- 25. Juan D, Alexe G, Antes T, Liu H, Madabhushi A, et al. (2010) Identification of a microRNA panel for clear-cell kidney cancer. Urology 75: 835–841.
- 26. Gillet JP, Efferth T, Steinbach D, Hamels J, de Longueville F, et al. (2004) Microarray-based detection of multidrug resistance in human tumor cells by expression profiling of ATP-binding cassette transporter genes. Cancer research 64: 8987–8993.
- 27. Saetrom P, Biesinger J, Li SM, Smith D, Thomas LF, et al. (2009) A risk variant in an miR-125b binding site in BMPR1B is associated with breast cancer pathogenesis. Cancer research 69: 7459–7465.
- 28. Volinia S, Calin GA, Liu CG, Ambs S, Cimmino A, et al. (2006) A microRNA expression signature of human solid tumors defines cancer gene targets. Proceedings of the National Academy of Sciences of the United States of America 103: 2257–2261.
- 29. Iorio MV, Casalini P, Piovan C, Di Leva G, Merlo A, et al. (2009) microRNA-205 regulates HER3 in human breast cancer. Cancer research 69: 2195–2200.
- 30. Gregory PA, Bert AG, Paterson EL, Barry SC, Tsykin A, et al. (2008) The miR-200 family and miR-205 regulate epithelial to mesenchymal transition by targeting ZEB1 and SIP1. Nature cell biology 10: 593–601.
- 31. Sempere LF, Christensen M, Silahtaroglu A, Bak M, Heath CV, et al. (2007) Altered MicroRNA expression confined to specific epithelial cell subpopulations in breast cancer. Cancer research 67: 11612–11620.
- 32. Di Leva G, Gasparini P, Piovan C, Ngankeu A, Garofalo M, et al. (2010) MicroRNA cluster 221–222 and estrogen receptor alpha interactions in breast cancer. Journal of the National Cancer Institute 102: 706–721.
- 33. Gaur A, Jewell DA, Liang Y, Ridzon D, Moore JH, et al. (2007) Characterization of microRNA expression levels and their biological correlates in human cancer cell lines. Cancer research 67: 2456–2468.
- 34. Valastyan S, Reinhardt F, Benaich N, Calogrias D, Szasz AM, et al. (2009) A pleiotropically acting microRNA, miR-31, inhibits breast cancer metastasis. Cell 137: 1032–1046.
- 35. Leivonen SK, Makela R, Ostling P, Kohonen P, Haapa-Paananen S, et al. (2009) Protein lysate microarray analysis to identify microRNAs regulating estrogen receptor signaling in breast cancer cell lines. Oncogene 28: 3926–3936.
- 36. Camps C, Buffa FM, Colella S, Moore J, Sotiriou C, et al. (2008) hsa-miR-210 Is induced by hypoxia and is an independent prognostic factor in breast cancer. Clinical cancer research : an official journal of the American Association for Cancer Research 14: 1340–1348.
- 37. Foekens JA, Sieuwerts AM, Smid M, Look MP, de Weerd V, et al. (2008) Four miRNAs associated with aggressiveness of lymph node-negative, estrogen receptor-positive human breast cancer. Proceedings of the National Academy of Sciences of the United States of America 105: 13021–13026.
- 38. Yan LX, Huang XF, Shao Q, Huang MY, Deng L, et al. (2008) MicroRNA miR-21 overexpression in human breast cancer is associated with advanced clinical stage, lymph node metastasis and patient poor prognosis. RNA 14: 2348–2360.
- 39. O'Day E, Lal A (2010) MicroRNAs and their target gene networks in breast cancer. Breast cancer research : BCR 12: 201.
- 40. Ro S, Park C, Young D, Sanders KM, Yan W (2007) Tissue-dependent paired expression of miRNAs. Nucleic Acids Res 35: 5944–5953.
- 41. Zhou H, Huang X, Cui H, Luo X, Tang Y, et al. (2010) miR-155 and its star-form partner miR-155* cooperatively regulate type I interferon production by human plasmacytoid dendritic cells. Blood 116: 5885–5894.
- 42. Schotte D, Moqadam FA, Lange-Turenhout EA, Chen C, van Ijcken WF, et al. (2011) Discovery of new microRNAs by small RNAome deep sequencing in childhood acute lymphoblastic leukemia. Leukemia 25: 1389–1399.
- 43. Beck D, Ayers S, Wen J, Brandl MB, Pham TD, et al. (2011) Integrative analysis of next generation sequencing for small non-coding RNAs and transcriptional regulation in Myelodysplastic Syndromes. BMC Med Genomics 4: 19.
- 44. Creighton CJ, Benham AL, Zhu H, Khan MF, Reid JG, et al. (2010) Discovery of novel microRNAs in female reproductive tract using next generation sequencing. PLoS One 5: e9637.
- 45. Stark MS, Tyagi S, Nancarrow DJ, Boyle GM, Cook AL, et al. (2010) Characterization of the Melanoma miRNAome by Deep Sequencing. PLoS One 5: e9685.
- 46. Haverty PM, Fridlyand J, Li L, Getz G, Beroukhim R, et al. (2008) High-resolution genomic and expression analyses of copy number alterations in breast tumors. Genes, chromosomes & cancer 47: 530–542.
- 47. Lowery AJ, Miller N, Devaney A, McNeill RE, Davoren PA, et al. (2009) MicroRNA signatures predict oestrogen receptor, progesterone receptor and HER2/neu receptor status in breast cancer. Breast cancer research : BCR 11: R27.
- 48. Idirisinghe PK, Thike AA, Cheok PY, Tse GM, Lui PC, et al. (2010) Hormone receptor and c-ERBB2 status in distant metastatic and locally recurrent breast cancer. Pathologic correlations and clinical significance. American journal of clinical pathology 133: 416–429.
- 49. Da Silva L, Simpson PT, Smart CE, Cocciardi S, Waddell N, et al. (2010) HER3 and downstream pathways are involved in colonization of brain metastases from breast cancer. Breast cancer research : BCR 12: R46.
- 50. Santen RJ, Song RX, McPherson R, Kumar R, Adam L, et al. (2002) The role of mitogen-activated protein (MAP) kinase in breast cancer. The Journal of steroid biochemistry and molecular biology 80: 239–256.
- 51. Hui AB, Shi W, Boutros PC, Miller N, Pintilie M, et al. (2009) Robust global micro-RNA profiling with formalin-fixed paraffin-embedded breast cancer tissues. Laboratory investigation; a journal of technical methods and pathology 89: 597–606.
- 52. Vandesompele J, De Preter K, Pattyn F, Poppe B, Van Roy N, et al. (2002) Accurate normalization of real-time quantitative RT-PCR data by geometric averaging of multiple internal control genes. Genome biology 3: RESEARCH0034.
- 53. Shirdel EA, Xie W, Mak TW, Jurisica I (2011) NAViGaTing the micronome–using multiple microRNA prediction databases to identify signalling pathway-associated microRNAs. PloS one 6: e17429.
- 54. Dweep H, Sticht C, Pandey P, Gretz N (2011) miRWalk - Database: Prediction of possible miRNA binding sites by “walking” the genes of three genomes. Journal of biomedical informatics.
- 55. D'Eustachio P (2011) Reactome knowledgebase of human biological pathways and processes. Methods in molecular biology 694: 49–61.
- 56. Papadopoulos GL, Alexiou P, Maragkakis M, Reczko M, Hatzigeorgiou AG (2009) DIANA-mirPath: Integrating human and mouse microRNAs in pathways. Bioinformatics 25: 1991–1993.
- 57. Lewis BP, Shih IH, Jones-Rhoades MW, Bartel DP, Burge CB (2003) Prediction of mammalian microRNA targets. Cell 115: 787–798.
- 58. Krek A, Grun D, Poy MN, Wolf R, Rosenberg L, et al. (2005) Combinatorial microRNA target predictions. Nature genetics 37: 495–500.
- 59. Pico AR, Kelder T, van Iersel MP, Hanspers K, Conklin BR, et al. (2008) WikiPathways: pathway editing for the people. PLoS biology 6: e184.
- 60. Gerlach D, Kriventseva EV, Rahman N, Vejnar CE, Zdobnov EM (2009) miROrtho: computational survey of microRNA genes. Nucleic acids research 37: D111–117.
- 61. Wu H, Mo YY (2009) Targeting miR-205 in breast cancer. Expert opinion on therapeutic targets 13: 1439–1448.
- 62. Wang S, Bian C, Yang Z, Bo Y, Li J, et al. (2009) miR-145 inhibits breast cancer cell growth through RTKN. International journal of oncology 34: 1461–1466.
- 63. Schmittgen TD (2010) miR-31: a master regulator of metastasis? Future oncology 6: 17–20.
- 64. Negrini M, Calin GA (2008) Breast cancer metastasis: a microRNA story. Breast cancer research : BCR 10: 203.
- 65. Friedman JM, Jones PA, Liang G (2009) The tumor suppressor microRNA-101 becomes an epigenetic player by targeting the polycomb group protein EZH2 in cancer. Cell cycle 8: 2313–2314.
- 66. Kondo N, Toyama T, Sugiura H, Fujii Y, Yamashita H (2008) miR-206 Expression is down-regulated in estrogen receptor alpha-positive human breast cancer. Cancer research 68: 5004–5008.
- 67. Zhang Z, Sun H, Dai H, Walsh RM, Imakura M, et al. (2009) MicroRNA miR-210 modulates cellular response to hypoxia through the MYC antagonist MNT. Cell cycle 8: 2756–2768.