Literature DB >> 23722663

MicroRNA-regulated protein-protein interaction networks and their functions in breast cancer.

Chia-Hsien Lee1, Wen-Hong Kuo, Chen-Ching Lin, Yen-Jen Oyang, Hsuan-Cheng Huang, Hsueh-Fen Juan.   

Abstract

MicroRNAs, which are small endogenous RNA regulators, have been associated with various types of cancer. Breast cancer is a major health threat for women worldwide. Many miRNAs were reported to be associated with the progression and carcinogenesis of breast cancer. In this study, we aimed to discover novel breast cancer-related miRNAs and to elucidate their functions. First, we identified confident miRNA-target pairs by combining data from miRNA target prediction databases and expression profiles of miRNA and mRNA. Then, miRNA-regulated protein interaction networks (PINs) were constructed with confident pairs and known interaction data in the human protein reference database (HPRD). Finally, the functions of miRNA-regulated PINs were elucidated by functional enrichment analysis. From the results, we identified some previously reported breast cancer-related miRNAs and functions of the PINs, e.g., miR-125b, miR-125a, miR-21, and miR-497. Some novel miRNAs without known association to breast cancer were also found, and the putative functions of their PINs were also elucidated. These include miR-139 and miR-383. Furthermore, we validated our results by receiver operating characteristic (ROC) curve analysis using our miRNA expression profile data, gene expression-based outcome for breast cancer online (GOBO) survival analysis, and a literature search. Our results may provide new insights for research in breast cancer-associated miRNAs.

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Year:  2013        PMID： 23722663      PMCID： PMC3709748          DOI： 10.3390/ijms140611560

Source DB:  PubMed          Journal:  Int J Mol Sci        ISSN： 1422-0067            Impact factor:   5.923


1. Introduction

Breast cancer is a global health threat for women. According to a 2008 survey [1], breast cancer was the leading cause of cancer deaths in women. Our knowledge of possible risk factors has led to developments in diagnostic methods, drugs, and surgery procedures for treatment [2,3]; however, the details of breast carcinoma progression, and perhaps most importantly, how to cure breast cancer, remain elusive. Previous research has identified a number of risk factors for breast cancer. Early menarche, late menopause, obesity, late first full pregnancy, and hormone replacement therapy were considered as high risk factors for breast cancer [2]. Breast cancer risk has also been reported to be related to fat intake in diets rich in red meats and high-fat dairy foods [3]. MicroRNAs (miRNAs) [4], are short endogenous non-coding RNAs which are able to regulate gene expression. After miRNA precursors are transcribed from the genome or generated from spliceosomes, they are exported to the cytoplasm and further processed by the Dicer complex [5]. The mature miRNA is then bound to Argonaute protein, forming a miRNA-protein complex known as the RNA-induced silencing complex (RISC) [6], miRNP, or RNAi (RNA interference) enzyme complex [7,8]. The RISC has been reported to down-regulate target genes by translational repression [9] or mRNA cleavage [10]. Like other protein-based regulators, miRNAs have been associated with cancer. Calin et al., reported that miR-15 and miR-16 were deleted in leukemia [11], which was believed to be one of the earliest reports associating miRNAs with cancer [12]. After this report, many miRNAs were found to act as tumor suppressors or oncogenes (also known as oncomirs). For example, miR-21 was identified as an oncomir in hepatocellular cancer [13], breast cancer [14], and kidney cancer [15]. On the other hand, let-7c was found to be a tumor suppressor in prostate cancer [16], and miR-181a was reported as a tumor suppressor in glioma [17]. Further, miR-125b [18], and miR-145 [19] were identified as tumor suppressors in breast cancer, and miR-125a was found to repress tumor growth in breast cancer [20]. Thus, it is highly likely that miRNAs play an important role in breast cancer. Since miRNA functions by regulating its target genes, we may deduce the effects of miRNAs by analyzing their regulated networks. To use such a method to elucidate miRNA functions, targets of miRNAs should be deduced. Currently, predictions in most target prediction database are based on sequence and statistical methods [21]. For example, in TargetScan, seed base pairing, target site context, conservation of target site and miRNA, and site accessibility are considered in the prediction process [22]. Another method to elucidate miRNA targets is to integrate expression profiles of miRNA and mRNA. In the work of Huang et al. [23], a Bayesian-based algorithm, GenMiR++, was developed to predict possible targets of 104 miRNAs in humans. They also verified their results by RT-PCR and microarray experiments. However, the power of other sequence-based target prediction algorithms was not utilized in their work. It is also possible to combine sequence-based target prediction and expression-based target prediction methods. By integrating expression data into sequence-based predictions, possible false positives can be reduced. Previously, miRNA-mRNA interactions were explored with splitting-averaging Bayesian networks [24]. In that work, expression profiles of miRNA and mRNA from public databases, miRNA target prediction databases, and miRNA sequence information were integrated together to discover miRNA-mRNA interaction networks. Here, we combined expression profiles of miRNA and mRNA, and three target prediction databases, TargetScan, PicTar and miRanda, to obtain confident miRNA-mRNA relationships and construct miRNA-regulated protein-protein networks for breast cancer. Furthermore, we explored the functions of the miRNAs by inspecting the underlying protein interaction networks (PINs) of the miRNAs with functional enrichment analysis. This method, as described in Figure 1, was used to elucidate the functions of gastric cancer-related miRNAs in our previous work [25]. In that study, a gastric cancer-associated miRNA, miR-148a, was identified and validated as being involved in tumor proliferation, invasion, migration, and the survival rate of the patients. By using a similar method, we aimed to elucidate breast cancer-related miRNA-regulated PINs and their functions.
Figure 1

Analysis flow chart used in this work. After expression profiles and target prediction databases were fetched and preprocessed, they were subjected to the analysis process described here and in the “Experimental Section”.

2. Results and Discussion

To construct miRNA-regulated PINs, differentially expressed miRNAs and genes from the dataset from Farazi et al. [26] were extracted following proper processing of the expression profiles. From our selected public miRNA dataset, we found 89 down-regulated miRNAs (93 prior to fold-change filtering) and only 1 up-regulated miRNA (Table S1). In gene expression dataset GSE29174, we found a total of 1268 down-regulated genes and 587 up-regulated genes before applying the fold change filter. There were 726 down-regulated genes (Table S2) and 437 up-regulated genes (Table S3) after significantly and differentially expressed genes were filtered by fold change (fold change >2). From the results of SAM analysis, we identified some well-known breast cancer-related miRNAs (Table S1). For example, miR-214-3p [27] and miR-335-5p [28] have been previously reported to be down-regulated in breast cancer. Let-7c was found to be down-regulated in this work, while let-7a, another member of the let-7 family, was found to be down-regulated in another work [29]. MicroRNAs of the let-7 family were also reportedly down-regulated in several types of cancer [30]. We also found that miR-21-5p, the sole up-regulated miRNA in our list, was also previously found to be up-regulated [14,31]. However, changes in the expression of most of the miRNAs in our down-regulated list have not been reported in the literature. Therefore, we could not rule out the possibility that these miRNAs were novel breast cancer-related miRNAs. There are also some well-known miRNAs not presented in our list (for such a list, one may see [32-34]). The reason that some known miRNAs, for example, miR-19a, miR-155 and miR-205, did not show up in our result might be that we used a very stringent threshold (described in Experimental Section) when selecting differentially expressed miRNAs for PIN construction. Since the miRNAs of the miRNA-regulated PINs were differentially expressed between normal and tumor tissues, and we identified some cancer-related functions in our functional enrichment analysis, the miRNAs may potentially be useful diagnostic markers for breast cancer. To verify this, we applied ROC curve analysis on the miRNA expression profile that was not used in constructing the miRNA-regulated PINs. Notably, our results (Figures 2 and S1 and Table S4) showed that let-7c (Figure 2), miR-497-5p, miR-125b-5p, and some other miRNAs of miRNA-regulated PINs, performed well when used as breast cancer diagnostic markers.
Figure 2

Receiver operating characteristic (ROC) curve of let-7c from our miRNA array dataset. For ROC curves of other miRNAs, see Figure S1.

Following elucidation of differentially expressed miRNAs and genes, miRNA-regulated PINs could then be constructed. We identified and constructed partial networks, containing the miRNA and its direct target, with the differentially expressed miRNAs and genes as described in the “Experimental Section”. We then extended the network by appending known interactions from the HPRD database. Finally, 18 miRNA-regulated PINs were constructed by the steps described above (Figure 3, Figures S2–S13, and Table S5).
Figure 3

The let-7c-regulated protein interaction network (PIN). This is one of the 18 miRNA-regulated PINs constructed in this work. Figures of all other miRNA-regulated PINs are displayed in Supplementary Figures S2–S13.

After construction of the 18 PINs was completed, we observed that the sizes of the PINs were not similar: some of the miRNAs seemed to regulate larger sized PINs, while other miRNAs affected only a small number of genes. Small miRNA-regulated PINs may be caused by the strict q-value threshold set during SAM analysis, the processing steps performed on the target prediction databases discussed previously, and possibly by lack of protein-protein interaction data for some proteins in the HPRD. Although the HPRD may be considered the most comprehensive source of protein-protein interaction data [35], some proteins may not have been considered and researched by other investigators, and therefore, interaction data for those proteins would not be included in the HPRD. However, it may be true that some of the miRNA-regulated PINs were small in breast cancer, since the construction of the PINs were based on differentially expressed miRNAs and genes between normal tissues and tumor samples, and those miRNAs with small PINs may not be as important as others with larger PINs. To elucidate the functions of a miRNA with a regulated PIN, GO enrichment analysis was applied to the miRNA-regulated PINs. We did not consider the miRNAs with ≤5 genes in their regulated PINs, and some of the miRNA-regulated PINs had no enriched functions using the defined threshold, FDR < 0.0001. To exclude GO terms that describe a broad range of concepts, we only included high level GO terms, i.e., larger than 5. The results of the GO enrichment analysis for let-7c related to cancer are listed in Table 1. (Results of all miRNA-regulated PINs were in Table S6). We defined a GO term as cancer-related if a GO term contained “cell proliferation”, “cell death”, “apoptosis”, “signaling”, “microtubule”, and “actin”. We noted that 7 miRNAs had enriched GO terms related to apoptosis, cell death, and cell proliferation, i.e., miR-520d-3p, miR-497-5p, miR-125b-5p, miR-21-5p, miR-31-5p, let-7c, and miR-125-5p. Further, some miRNA-regulated PINs may have functions other than cell survival. For example, the nerve growth factor receptor pathway was enriched in miR-regulated PINs of miR-520d-3p, miR-497-5p, miR-125a-5p, miR-125b-5p, and miR-31-5p, and the epidermal growth factor receptor pathway was enriched in miR-regulated PINs of miR-520d-3p, miR-21-5p, and miR-497-5p. Most of the miRNAs had been previously described and were known to be implicated in breast cancer. Let-7c was not only likely to be down-regulated in breast cancer [29], but was also found to be a tumor suppressor in prostate cancer [16]. Another reported tumor suppressor was miR-125b-5p, which was found to be down-regulated in breast tumor tissue [18], and this finding was consistent with our functional enrichment results. The miR-125a-5p-regulated PIN was found to be able to inhibit apoptosis and regulate epithelial cell proliferation, and has been reported to repress cell growth [36].
Table 1

Selected enriched functions of let-7c. Member genes of the let-7c-regulated network annotated with corresponding enriched functions are listed.

MIMAT0000064(hsa-let-7c)
GO termGenesAdj. p-value
GO:0043067, Regulation of programmed cell deathRRM2B, ACVR1, TP53, RASA1, TGFBR1, PSMA3, BIRC5, ACTN2, HOXA13, IRS2, FASTK, VAV1, PSMB6, BCL2, CDK1, HDAC1, SOX10, TIA1, AKT1, AURKB3.43 × 10−8
GO:0043069, Negative regulation of programmed cell deathRRM2B, ACVR1, TP53, RASA1, TGFBR1, BIRC5, IRS2, BCL2, CDK1, HDAC1, SOX10, AKT1, AURKB6.58 × 10−6
GO:0060548, Negative regulation of cell deathRRM2B, ACVR1, TP53, RASA1, TGFBR1, BIRC5, IRS2, BCL2, CDK1, HDAC1, SOX10, AKT1, AURKB8.92 × 10−6
GO:0015630, Microtubule cytoskeletonINCENP, SNTB2, SEPT1, TACC1, BIRC5, RACGAP1, PIN4, CDCA8, CDK1, PHF1, AKT1, AURKB, NINL, CCDC85B5.15 × 10−5
We also found that some breast cancer-specific functions were enriched in our results. For example, in the miR-497-5p-regulated PIN, the term “androgen receptor signaling pathway” was enriched. Although it is not clear whether androgens are related to breast cancer, androgen receptors are known to be up-regulated in breast cancer and related to node invasiveness [37]. To further verify the results of the enriched cancer-related functions, we used GOBO for survival analysis. Our hypothesis is that expression of genes annotated with enriched cancer-related terms may be related to survival outcome of patients. With exception to some cell death/proliferation-related terms, it is already known that some pathways or functions are also related to clinical outcomes. For example, cell proliferation-related GO terms have a high probability of affecting survival of cancer tissues, and patient outcome may worsen if cancer tissue survives. In addition, some signaling pathways were known to enhance invasiveness, migration abilities, or were associated with reduced patient survival. For example, the BMP signaling pathway is known to confer various tumor cells with enhanced migration and invasion abilities [38], and nerve growth factor receptor (NGFR) was found to be associated with overall survival of breast cancer [38]. Furthermore, Toll-like receptor 4 has been reported to promote adhesion and invasive migration in breast cancer [39]. Finally, the cytoskeleton plays an important role in regulating cell motility in all cells. Actin filaments are known to participate in the invasive migration of cancer cells [40]. Since some of these functions were present in our enriched terms, we wished to test if the expression of a gene set annotated to the cancer-related enriched terms in the PIN would be related to clinical outcome of patients. As shown in Figure 4, Figure S14 and Table S7, only some of the enriched terms were significantly associated with clinical outcome. This may be because the changes in these key genes occurred at the protein level, such as in protein expression or even post-translational modification; therefore, mRNA expression-centric tools like GOBO cannot explore association of such genes to clinical outcomes. Alternatively, it is possible the miRNA did not regulate the whole pathway, or the miRNA did not target the key part of the pathway directly, and thus the clinical outcome of gene sets of the enriched GO terms in this condition cannot be determined. However, some functions associated with clinical outcomes were observed. For example, proteins annotated with the terms “microtubule cytoskeleton”, “negative regulation of programmed cell death”, and “negative regulation of cell death” in the let-7c-regulated PIN were related to 10 year survival rate of patients, as reported by GOBO (Figure 4). Also, the enriched term “regulation of epithelial cell proliferation” for both miR-125a-5p and miR-125b-5p were found to be associated with the 10-year survival rate of patients. Therefore, these results further supported the GO enrichment analysis discussed previously.
Figure 4

Validation of let-7c result. Gene expression-based outcome for breast cancer online (GOBO) survival analysis of let-7c-regulated PIN members marked with the following functions: (A) Microtubule cytoskeleton; (B) Negative regulation of programmed cell death; and (C) negative regulation of cell death. Red: samples with high expression of selected gene set (PIN members); grey: samples with low expression of selected gene set (PIN members).

3. Experimental Section

3.1. miRNA Microarray Experiments

We performed a miRNA microarray to obtain the expression profiles for receiver operating characteristic (ROC) curve analysis. This dataset was deposited in Gene Expression Omnibus (GEO, http://www.ncbi.nlm.nih.gov/geo/, Series Accession: GSE45666). In total, there were 15 normal samples and 101 tumor samples in the expression profile. Detailed pathophysiogical characteistics of these samples were in Table S8 and GSE45666. All human tissue samples collected from breast cancer patients were approved and human subject confidentiality was protected by the Institute Review Board of National Taiwan University Hospital (IRB, 20071211R). Total RNA was extracted from tissues collected from the patients using Trizol® Reagent (Invitrogen, Carlsbad, CA, USA, USA) according to the manufacturer’s protocol. Purified RNA was quantified at OD260nm by using a ND-1000 spectrophotometer (Nanodrop Technology, Wilmington, DE, USA) and qualitated by using a Bioanalyzer 2100 with the RNA 6000 Nano LabChip kit (Agilent Technologies, Santa Clara, CA, USA). After RNA extraction, 100 ng of total RNA was dephosphorylated and labeled with pCp-Cy3 using the Agilent miRNA Complete Labeling and Hyb Kit in conjunction with the microRNA Spike-In kit (Agilent Technologies, Santa Clara, CA, USA). Briefly, 2X hybridization buffer (Agilent Technologies) was added to the labeled mixture to a final volume of 45 μL. The mixture was heated for 5 min at 100 °C and immediately cooled to 0 °C. Each 45 μL sample was hybridized onto an Agilent human miRNA Microarray Release 12.0, 8 × 15 K (Agilent Technologies) at 55 °C for 20 h. After hybridization, slides were washed for 5 min in Gene Expression Wash Buffer 1 at room temperature, then for 5 min in Gene Expression Wash Buffer 2 at 37 °C. Slides were scanned on an Agilent microarray scanner (Agilent Technologies, model G2505C) at 100% and 5% sensitivity settings. Feature Extraction (Agilent Technologies) software version 10.7.3.1 (Agilent Technologies, Santa Clara, CA, USA) was used for image analysis.

3.2. mRNA Expression Profiles

For miRNA-regulated protein-protein interaction network construction, the mRNA expression profile was fetched from GEO (Series Accession: GSE29174). This dataset was produced by Farazi et al. [26], which was the only dataset publicly available with large size of tumor samples and reasonably sized normal samples. In total, 161 clinical samples were collected from breast cancer patients by biopsy: 110 invasive ductal carcinoma (IDC), 11 normal, 17 ductal carcinoma in situ (DCIS), 1 mucinous A, 8 atypical medullary, 4 apocrine, 8 metaplastic, and 2 adenoid, as classified by Farazi et al. The 110 IDC samples were classified as the tumor group and the 11 normal samples were classified as the normal group in this study.

3.3. miRNA Expression Profiles

The miRNA expression profile used in this work was fetched from Table S4A of the work of Farazi et al. [26]. There were 189 samples in this dataset, with 6 of them from cell lines, and another 183 samples from patient tissues. In the 183 clinical samples collected from breast cancer patients by biopsy in this dataset, there were 128 IDC, 11 normal, 18 DCIS, 1 mucinous A, 8 atypical medullary, 4 apocrine, 9 metaplastic, and 2 adenoid cases, as classified by Farazi et al. The 128 IDC samples were classified as the tumor group and the 11 normal samples as were classified as the normal group.

3.4. Data Analysis

The overall workflow design was similar to the previous work of Tseng et al. [25] (see Figure 1). However, we applied the workflow on breast cancer expression profiles instead of gastric cancer as in the work of Tseng et al. Additionally, we used 3 miRNA target prediction databases, TargetScan (v6.0) [22,41,42], PicTar [43,44], and miRanda (release August 2010) [45] here, while only TargetScan was used in the previous study. To construct the networks, we first elucidated differentially expressed miRNAs and mRNAs from published datasets. Both of miRNA (generated with miRNA-Seq technique) and mRNA array expression data were produced by Farazi et al. [26] from the same batch of clinical tumor samples. To obtain a list of differentially expressed genes and miRNAs between normal and tumor groups, we used significance analysis of microarrays (SAM) [46] implemented in R package samr (version 2.0; Stanford University, Stanford, CA, USA). We set the false discovery rate (FDR) as ≤0.0001%, fold change as ≥2.5 for miRNA, and fold change as ≥1.9 for genes as thresholds to reduce false positives. If a gene/miRNA was expressed higher in the normal group compared to the tumor group, we defined that gene/miRNA as a down-regulated gene/miRNA, and vice versa. Following this, we paired the miRNAs and mRNAs with different expression trends. For example, an up-regulated miRNA would be paired with a down-regulated mRNA. If such pairs could be found in 2 (or more) of the 3 miRNA target prediction databases, they were then added to their corresponding miRNA-regulated network. To further extend the coverage of our network, we incorporated the human protein reference database (HPRD) [47], which contains experimentally verified interaction data, into our miRNA-regulated PINs. Finally, we used gene ontology (GO) enrichment analysis to explore the function of the miRNA-regulated PINs. Hypergeometric tests were used to determine if a GO term was enriched in a PIN. In this section, we excluded PINs with less than 5 proteins. We also excluded GO terms with levels of less than 5 to avoid non-specific GO terms. Since we tested multiple GO terms on each miRNA-regulated PIN, we adjusted the significance of the test with the FDR method developed by Benjamini et al. [48]. We used the adjusted p-value < 0.0001 as our threshold. A GO term would be excluded if its p-value was larger than 0.0001. Therefore, for each network, we ran the GO enrichment analysis, collected the calculated p-values, and adjusted these values using the methods described above.

3.5. ROC and GOBO Survival Analysis

After the PINs were constructed, we attempted to verify our results by literature search, ROC, and GOBO survival analysis [49]. To determine if the miRNAs we found could serve as classification markers for discriminating between normal and tumor samples, we applied ROC analysis on our miRNA array data (described in Section 2.1). ROC analysis is usually used to evaluate the efficiency of a classifier or a biological marker. R package ROCR [50] (version 1.0.4) was used to plot the ROC curve and calculate the area under curve (AUC). The standard error of AUC was then calculated as described in the work of Hanlye and McNeil [51]. The p-value of AUC was thus calculated with standard error obtained in the previous step. To further validate if the PINs we found were related to cancer, we used survival analysis implemented in GOBO [49] (available at http://co.bmc.lu.se/gobo), which provides a large amount of breast cancer gene expression profiles collected from public databases with clinical outcome data. In both ROC analysis and GOBO survival analysis, we considered our results significant when the p-value was smaller than 0.05.

4. Conclusions

Using integrative analysis of miRNA and mRNA expression profiles, we have identified not only breast cancer-related miRNAs and genes, but also putative roles for miRNAs in cancer as elucidated from miRNA-regulated PINs constructed in this work. Here, some previously known functions of miRNAs were again presented in our results, e.g., the relationship between the miRNAs, let-7c, miR-125a-5p, miR-125b-5p, and miR-21-5p, and breast cancer were demonstrated in this research. Furthermore, we have identified additional miRNAs and their related functions that have not been previously reported or discussed, providing valuable resources for further research in breast cancer. Significantly differentially expressed miRNAs found in miRNA dataset in Farazi et al. [26]. There are 89 down-regulated miRNAs and 1 up-regulated miRNA in this list. Q-values reported by SAM were 0 for all miRNAs in this list. Down-regulated genes found in dataset GSE29174. There are 726 down-regulated genes in this list. Q-values reported by SAM were 0 for all genes in this list. Up-regulated genes found in dataset GSE29174. There are 437 up-regulated genes in this list. Q-values reported by SAM were 0 for all genes in this list. Result of ROC curve analysis on our miRNA array data. ROC analysis was done to validate the diagnostic value of the miRNA in the miRNA-regulated PINs. AUC: area under (ROC) curve; p-value < 0.05; p-value < 0.001. Summary of constructed miRNA-regulated networks. L0 gene: genes connected directly to the miRNA (i.e., direct target of miRNA); L1 gene: genes not connected directly to the miRNA. Specific enriched GO terms of each miRNA-regulated PINs. Genes annotated with the specific GO term in the PIN were also listed in this table. Adj. p-value: multiple-test adjusted p-value calculated by the method described in the work of Benjamini and Yekutieli [48]. GOBO survival analysis results. Genes which was annotated with the specified GO term of proteins in the PIN would be used as input gene set for GOBO analysis. p < 0.05, p < 0.01; p < 0.001. Pathophysiogical characteristics of miRNA array data used in ROC curve analysis.
Table S1

Significantly differentially expressed miRNAs found in miRNA dataset in Farazi et al. [26]. There are 89 down-regulated miRNAs and 1 up-regulated miRNA in this list. Q-values reported by SAM were 0 for all miRNAs in this list.

miRBase AccessionmiRNA NameFold Change
MIMAT0004761hsa-miR-483-5p0.01
MIMAT0004552hsa-miR-139-3p0.01
MIMAT0000738hsa-miR-3830.02
MIMAT0002856hsa-miR-520d-3p0.02
MIMAT0002811hsa-miR-202-3p0.03
MIMAT0002177hsa-miR-486-5p0.04
MIMAT0022721hsa-miR-1247-3p0.05
MIMAT0002175hsa-miR-485-5p0.06
MIMAT0000265hsa-miR-204-5p0.07
MIMAT0000752hsa-miR-3280.07
MIMAT0000421hsa-miR-122-5p0.07
MIMAT0000447hsa-miR-1340.08
MIMAT0000722hsa-miR-3700.09
MIMAT0004513hsa-miR-101-5p0.09
MIMAT0000446hsa-miR-127-3p0.10
MIMAT0000097hsa-miR-99a-5p0.10
MIMAT0004566hsa-miR-218-2-3p0.10
MIMAT0000729hsa-miR-376a-3p0.11
MIMAT0009197hsa-miR-205-3p0.11
MIMAT0004615hsa-miR-195-3p0.11
MIMAT0005899hsa-miR-1247-5p0.11
MIMAT0000720hsa-miR-376c0.12
MIMAT0000762hsa-miR-324-3p0.12
MIMAT0004679hsa-miR-296-3p0.12
MIMAT0004614hsa-miR-193a-5p0.12
MIMAT0003880hsa-miR-671-5p0.12
MIMAT0004795hsa-miR-574-5p0.12
MIMAT0004599hsa-miR-143-5p0.13
MIMAT0000423hsa-miR-125b-5p0.13
MIMAT0004957hsa-miR-7600.13
MIMAT0004911hsa-miR-8740.14
MIMAT0004603hsa-miR-125b-2-3p0.15
MIMAT0004952hsa-miR-6650.15
MIMAT0018205hsa-miR-39280.15
MIMAT0004767hsa-miR-193b-5p0.15
MIMAT0002861hsa-miR-518e-3p0.15
MIMAT0004604hsa-miR-127-5p0.16
MIMAT0002807hsa-miR-491-5p0.16
MIMAT0004689hsa-miR-377-5p0.16
MIMAT0004762hsa-miR-486-3p0.16
MIMAT0000732hsa-miR-378a-3p0.17
MIMAT0017981hsa-miR-3605-5p0.18
MIMAT0004605hsa-miR-129-2-3p0.19
MIMAT0006789hsa-miR-14680.20
MIMAT0000737hsa-miR-382-5p0.21
MIMAT0000077hsa-miR-22-3p0.21
MIMAT0000089hsa-miR-31-5p0.21
MIMAT0004612hsa-miR-186-3p0.21
MIMAT0004592hsa-miR-125b-1-3p0.22
MIMAT0001639hsa-miR-409-3p0.22
MIMAT0015032hsa-miR-3158-3p0.22
MIMAT0004496hsa-miR-23a-5p0.22
MIMAT0000690hsa-miR-296-5p0.22
MIMAT0000731hsa-miR-378a-5p0.23
MIMAT0000448hsa-miR-136-5p0.23
MIMAT0004796hsa-miR-576-3p0.23
MIMAT0010133hsa-miR-21100.23
MIMAT0004951hsa-miR-8870.23
MIMAT0003239hsa-miR-574-3p0.25
MIMAT0005901hsa-miR-12490.25
MIMAT0000510hsa-miR-320a0.26
MIMAT0002172hsa-miR-376b0.26
MIMAT0000250hsa-miR-139-5p0.27
MIMAT0005825hsa-miR-11800.27
MIMAT0000437hsa-miR-145-5p0.28
MIMAT0004601hsa-miR-145-3p0.28
MIMAT0003322hsa-miR-652-3p0.28
MIMAT0000756hsa-miR-3260.28
MIMAT0000098hsa-miR-100-5p0.29
MIMAT0003296hsa-miR-6270.29
MIMAT0002820hsa-miR-497-5p0.31
MIMAT0004507hsa-miR-92a-1-5p0.31
MIMAT0000271hsa-miR-214-3p0.32
MIMAT0004702hsa-miR-339-3p0.33
MIMAT0004611hsa-miR-185-3p0.33
MIMAT0000064hsa-let-7c0.34
MIMAT0004673hsa-miR-29c-5p0.35
MIMAT0000733hsa-miR-379-5p0.35
MIMAT0004594hsa-miR-132-5p0.35
MIMAT0000765hsa-miR-335-5p0.35
MIMAT0002819hsa-miR-193b-3p0.36
MIMAT0000088hsa-miR-30a-3p0.36
MIMAT0005951hsa-miR-1307-3p0.36
MIMAT0004597hsa-miR-140-3p0.37
MIMAT0004556hsa-miR-10b-3p0.37
MIMAT0000272hsa-miR-2150.37
MIMAT0004511hsa-miR-99a-3p0.37
MIMAT0000443hsa-miR-125a-5p0.38
MIMAT0004482hsa-let-7b-3p0.38
MIMAT0000076hsa-miR-21-5p6.58
Table S2

Down-regulated genes found in dataset GSE29174. There are 726 down-regulated genes in this list. Q-values reported by SAM were 0 for all genes in this list.

NCBI gene IDGene SymbolFold Change
2949GSTM50.06
10894LYVE10.06
5950RBP40.07
762CA40.09
54997TESC0.09
3489IGFBP60.09
3952LEP0.09
213ALB0.09
3131HLF0.10
4023LPL0.10
10633RASL10A0.11
364AQP70.11
1908EDN30.11
1811SLC26A30.11
91851CHRDL10.11
729359PLIN40.13
1149CIDEA0.13
5959RDH50.13
5348FXYD10.14
5346PLIN10.14
10249GLYAT0.14
158800RHOXF10.14
221476PI160.14
3040HBA20.14
6939TCF150.14
79645EFCAB10.14
80343SEL1L20.14
9413FAM189A20.15
26289AK50.15
25891PAMR10.15
3679ITGA70.15
1264CNN10.15
92304SCGB3A10.15
2167FABP40.15
23285KIAA11070.15
7145TNS10.16
4881NPR10.16
1028CDKN1C0.16
1036CDO10.16
130271PLEKHH20.16
8736MYOM10.16
8908GYG20.16
619373MBOAT40.17
130399ACVR1C0.17
1646AKR1C20.17
80763C12orf390.17
2159F100.18
84889SLC7A30.18
1308COL17A10.18
83699SH3BGRL20.18
84417C2orf400.18
4081MAB21L10.18
3484IGFBP10.18
5239PGM50.19
4969OGN0.19
2719GPC30.19
116362RBP70.19
948CD360.19
5764PTN0.19
3043HBB0.19
56920SEMA3G0.20
94274PPP1R14A0.20
57447NDRG20.20
84795PYROXD20.20
84649DGAT20.20
2690GHR0.20
22802CLCA40.20
5179PENK0.20
6663SOX100.20
6649SOD30.21
54922RASIP10.21
8406SRPX0.21
1446CSN1S10.21
7123CLEC3B0.22
9647PPM1F0.22
1842ECM20.22
3909LAMA30.22
8639AOC30.23
2934GSN0.23
9370ADIPOQ0.23
3202HOXA50.23
9452ITM2A0.23
6290SAA3P0.23
4604MYBPC10.23
79785RERGL0.16
221091LRRN4CL0.17
3991LIPE0.17
27175TUBG20.24
1346COX7A10.24
6376CX3CL10.24
50486G0S20.24
6285S100B0.24
443ASPA0.24
947CD340.25
84632AFAP1L20.25
3866KRT150.25
147463ANKRD290.25
2878GPX30.25
7079TIMP40.25
54345SOX180.25
51277DNAJC270.25
84870RSPO30.25
55323LARP60.25
6387CXCL120.25
137835TMEM710.25
5212VIT0.25
26577PCOLCE20.25
845CASQ20.25
6422SFRP10.25
10351ABCA80.26
10840ALDH1L10.26
65983GRAMD30.26
84327ZBED30.26
57124CD2480.26
3235HOXD90.26
2192FBLN10.26
91653BOC0.26
4147MATN20.26
126669SHE0.27
2788GNG70.27
129804FBLN70.27
270AMPD10.27
79656BEND50.27
58503PROL10.27
3316HSPB20.27
729440CCDC610.27
54438GFOD10.27
5243ABCB10.27
1128CHRM10.23
83878USHBP10.24
63970TP53AIP10.24
79192IRX10.28
3400ID40.28
57519STARD90.29
57666FBRSL10.29
3590IL11RA0.29
57664PLEKHA40.29
197257LDHD0.29
66036MTMR90.29
2321FLT10.29
126ADH1C0.29
1363CPE0.29
56131PCDHB40.29
22915MMRN10.29
7069THRSP0.29
57161PELI20.30
770CA110.30
53342IL17D0.30
79987SVEP10.30
857CAV10.30
222166C7orf410.30
27190IL17B0.30
116159CYYR10.30
4487MSX10.30
9068ANGPTL10.30
10411RAPGEF30.30
3199HOXA20.30
2944GSTM10.30
2920CXCL20.30
201134CEP1120.31
220001VWCE0.31
83888FGFBP20.31
6366CCL210.31
6711SPTBN10.31
85378TUBGCP60.31
26040SETBP10.31
4692NDN0.31
25890ABI3BP0.31
23531MMD0.31
30846EHD20.31
6196RPS6KA20.31
2009EML10.31
810CALML30.27
6898TAT0.27
5648MASP10.28
25999CLIP30.28
125875CLDND20.28
7102TSPAN70.28
1879EBF10.28
23252OTUD30.28
5493PPL0.28
83987CCDC80.28
9073CLDN80.28
221981THSD7A0.28
64102TNMD0.28
137872ADHFE10.33
27151CPAMD80.33
387923SERP20.33
145581LRFN50.33
6263RYR30.33
2354FOSB0.33
51302CYP39A10.33
4128MAOA0.34
117248GALNTL20.34
10268RAMP30.34
7730ZNF1770.34
10873ME30.34
7461CLIP20.34
7049TGFBR30.34
79901CYBRD10.34
5152PDE9A0.34
50805IRX40.34
8644AKR1C30.34
5915RARB0.34
2770GNAI10.34
549962-Mar0.35
79791FBXO310.35
54776PPP1R12C0.35
9079LDB20.35
57104PNPLA20.35
30008EFEMP20.35
91461PKDCC0.35
23368PPP1R13B0.35
23461ABCA50.35
9572NR1D10.35
23338PHF150.35
6289SAA20.31
345275HSD17B130.31
2701GJA40.32
112609MRAP20.32
727C50.32
477ATP1A20.32
9627SNCAIP0.32
4435CITED10.32
10974C10orf1160.32
11005SPINK50.32
80325ABTB10.33
221395GPR1160.33
10014HDAC50.33
1489CTF10.37
35ACADS0.37
3749KCNC40.37
140738TMEM370.37
2791GNG110.37
23604DAPK20.37
10217CTDSPL0.37
23550PSD40.37
4306NR3C20.37
119587CPXM20.37
7942TFEB0.37
3815KIT0.37
1805DPT0.37
23242COBL0.37
4313MMP20.37
4139MARK10.37
9104RGN0.37
2329FMO40.37
25802LMOD10.38
4239MFAP40.38
10392NOD10.38
6794STK110.38
85458DIXDC10.38
4123MAN2C10.38
54476RNF2160.38
9920KBTBD110.38
6329SCN4A0.38
10253SPRY20.38
1910EDNRB0.38
9249DHRS30.38
22869ZNF5100.38
114800CCDC85A0.35
2550GABBR10.35
4638MYLK0.35
2327FMO20.35
139411PTCHD10.35
10391CORO2B0.35
25854FAM149A0.35
55701ARHGEF400.36
1759DNM10.36
22849CPEB30.36
57716PRX0.36
1628DBP0.36
80031SEMA6D0.36
259217HSPA12A0.36
6909TBX20.36
1511CTSG0.36
79971WLS0.36
90865IL330.36
11343MGLL0.36
55800SCN3B0.36
1949EFNB30.36
284217LAMA10.36
22927HABP40.37
23645PPP1R15A0.39
342574KRT270.39
83543AIF1L0.39
624BDKRB20.39
347APOD0.39
84935C13orf330.39
858CAV20.39
5138PDE2A0.40
114928GPRASP20.40
58190CTDSP10.40
513ATP5D0.40
57684ZBTB260.40
7041TGFB1I10.40
5787PTPRB0.40
7294TXK0.40
56301SLC7A100.40
55937APOM0.40
6368CCL230.40
55020TTC380.40
134265AFAP1L10.40
4485MST10.40
3384ICAM20.38
8613PPAP2B0.38
1950EGF0.38
55273TMEM1000.38
6297SALL20.38
9365KL0.38
8863PER30.38
8404SPARCL10.38
2202EFEMP10.38
8369HIST1H4G0.38
5187PER10.39
30815ST6GALNAC60.39
256364EML30.39
57381RHOJ0.39
761CA30.39
83989FAM172A0.39
1408CRY20.39
2281FKBP1B0.39
51222ZNF2190.39
54540FAM193B0.39
4053LTBP20.39
55184DZANK10.39
5740PTGIS0.39
84814PPAPDC30.42
79365BHLHE410.42
316AOX10.42
23380SRGAP20.42
84033OBSCN0.42
90353CTU10.42
9013TAF1C0.42
474344GIMAP60.42
84883AIFM20.42
58480RHOU0.42
65982ZSCAN180.42
666BOK0.42
79762C1orf1150.42
525ATP6V1B10.42
4675NAP1L30.42
3257HPS10.43
55781RIOK20.43
63947DMRTC10.43
1969EPHA20.43
25927CNRIP10.43
57685CACHD10.43
51559NT5DC30.40
7169TPM20.40
51705EMCN0.40
8938BAIAP30.40
10365KLF20.40
59ACTA20.40
80309SPHKAP0.40
3779KCNMB10.41
10826C5orf40.41
219654ZCCHC240.41
92162TMEM880.41
7450VWF0.41
10266RAMP20.41
25875LETMD10.41
1938EEF20.41
121551BTBD110.41
2119ETV50.41
9696CROCC0.41
1031CDKN2C0.41
9037SEMA5A0.41
3397ID10.41
84707BEX20.41
57616TSHZ30.41
1471CST30.41
55214LEPREL10.41
3914LAMB30.41
57478USP310.41
3783KCNN40.41
8839WISP20.41
1583CYP11A10.42
10124ARL4A0.42
738C11orf20.42
29800ZDHHC10.42
23135KDM6B0.44
171024SYNPO20.44
10350ABCA90.44
3691ITGB40.44
2348FOLR10.44
11145PLA2G160.44
554AVPR20.45
64072CDH230.45
80177MYCT10.45
5957RCVRN0.45
408ARRB10.45
29997GLTSCR20.43
26051PPP1R16B0.43
83604TMEM470.43
2308FOXO10.43
55225RAVER20.43
54839LRRC490.43
122953JDP20.43
29775CARD100.43
166AES0.43
25924MYRIP0.43
2852GPER0.43
51421AMOTL20.43
124936CYB5D20.43
1294COL7A10.43
127435PODN0.43
84952CGNL10.43
83483PLVAP0.43
1958EGR10.43
230ALDOC0.43
65987KCTD140.43
4804NGFR0.44
64852TUT10.44
84253GARNL30.44
5866RAB3IL10.44
10608MXD40.44
4211MEIS10.44
83547RILP0.44
9172MYOM20.44
57192MCOLN10.44
255877BCL6B0.44
56904SH3GLB20.44
51285RASL120.44
3425IDUA0.44
402117VWC2L0.46
81490PTDSS20.46
283748PLA2G4D0.46
23523CABIN10.46
6146RPL220.46
85360SYDE10.46
60468BACH20.46
57451ODZ20.46
4013VWA5A0.46
339768ESPNL0.46
3860KRT130.46
144699FBXL140.45
83719YPEL30.45
22841RAB11FIP20.45
283927NUDT70.45
293SLC25A60.45
90507SCRN20.45
37ACADVL0.45
112744IL17F0.45
6709SPTAN10.45
8086AAAS0.45
7423VEGFB0.45
64221ROBO30.45
7273TTN0.45
2657GDF10.45
59271C21orf630.45
132160PPM1M0.45
27244SESN10.45
51310SLC22A170.45
4828NMB0.45
54360CYTL10.45
203245NAIF10.45
23166STAB10.45
2121EVC0.45
116496FAM129A0.45
23239PHLPP10.45
51673TPPP30.45
64094SMOC20.45
6383SDC20.45
2180ACSL10.45
23770FKBP80.45
55901THSD10.46
25895METTL21B0.46
23731C9orf50.46
126393HSPB60.46
4056LTC4S0.46
79825CCDC480.46
10810WASF30.46
29911HOOK20.46
583BBS20.46
28984C13orf150.46
1465CSRP10.46
55258THNSL20.46
161198CLEC14A0.46
3699ITIH30.48
7094TLN10.46
4232MEST0.46
1410CRYAB0.46
57452GALNTL10.47
63935PCIF10.47
25873RPL360.47
9812KIAA01410.47
51665ASB10.47
64123ELTD10.47
6122RPL30.47
222962SLC29A40.47
23102TBC1D2B0.47
3476IGBP10.47
93408MYL100.47
5310PKD10.47
4628MYH100.47
221935SDK10.47
23328SASH10.47
8522GAS70.47
10023FRAT10.47
7301TYRO30.47
2767GNA110.47
9457FHL50.47
4094MAF0.47
65268WNK20.47
54585LZTFL10.47
375449MAST40.47
138311FAM69B0.47
160622GRASP0.47
22837COBLL10.47
51435SCARA30.47
217ALDH20.47
6236RRAD0.47
8322FZD40.47
653275CFC1B0.47
10908PNPLA60.47
57526PCDH190.47
8424BBOX10.47
9905SGSM20.48
10435CDC42EP20.48
23087TRIM350.48
60314C12orf100.48
1073CFL20.48
5256PHKA20.49
92922CCDC102A0.48
65057ACD0.48
9095TBX190.48
6441SFTPD0.48
22846VASH10.48
51066C3orf320.48
23179RGL10.48
4664NAB10.48
50511SYCP30.48
6430SRSF50.48
11078TRIOBP0.48
78991PCYOX1L0.48
6623SNCG0.48
23384SPECC1L0.48
53826FXYD60.48
9397NMT20.48
6041RNASEL0.48
113510HELQ0.48
64788LMF10.48
2217FCGRT0.48
79720VPS37B0.48
6764ST50.48
252969NEIL20.48
8987STBD10.48
41ACCN20.48
7905REEP50.48
5919RARRES20.48
10544PROCR0.48
6876TAGLN0.48
8436SDPR0.49
23500DAAM20.49
130132RFTN20.49
80310PDGFD0.49
4215MAP3K30.49
282775OR5J20.49
51161C3orf180.49
29098RANGRF0.49
53336CPXCR10.49
9081PRY0.49
9459ARHGEF60.49
2995GYPC0.49
23057NMNAT20.49
4669NAGLU0.49
6452SH3BP20.49
6237RRAS0.49
5288PIK3C2G0.49
10252SPRY10.49
79026AHNAK0.49
9693RAPGEF20.49
51226COPZ20.49
158326FREM10.49
1956EGFR0.49
5360PLTP0.49
290ANPEP0.49
1756DMD0.49
5118PCOLCE0.49
56654NPDC10.49
9254CACNA2D20.49
55536CDCA7L0.49
124975GGT60.49
1906EDN10.49
81029WNT5B0.49
2646GCKR0.49
9811CTIF0.50
145376PPP1R360.50
222865TMEM1300.50
92999ZBTB470.50
168002DACT20.50
6829SUPT5H0.50
9992KCNE20.50
58509C19orf290.50
79706PRKRIP10.50
1153CIRBP0.50
9639ARHGEF100.50
4054LTBP30.50
1120CHKB0.50
286046XKR60.50
9590AKAP120.50
64115C10orf540.50
2067ERCC10.50
7507XPA0.50
22897CEP1640.50
652BMP40.50
55702CCDC940.50
57613KIAA14670.50
28514DLL10.50
169270ZNF5960.50
83982IFI27L20.50
51458RHCG0.49
1112FOXN30.49
29954POMT20.49
9612NCOR20.49
3198HOXA10.49
5311PKD20.49
2946GSTM20.49
2109ETFB0.49
56062KLHL40.49
6915TBXA2R0.50
64288ZNF3230.50
5195PEX140.50
84557MAP1LC3A0.50
6164RPL340.50
8835SOCS20.50
2735GLI10.50
26022TMEM980.50
3908LAMA20.50
1825DSC30.50
5730PTGDS0.50
162515SLC16A110.51
274BIN10.51
79654HECTD30.51
22863ATG140.51
25949SYF20.51
84872ZC3H100.51
23187PHLDB10.51
5434POLR2E0.51
6181RPLP20.51
6141RPL180.51
84747UNC119B0.51
23399CTDNEP10.51
599BCL2L20.51
197258FUK0.51
5207PFKFB10.51
8131NPRL30.51
25839COG40.51
10816SPINT30.51
60485SAV10.51
5681PSKH10.51
80318GKAP10.51
57088PLSCR40.51
93129ORAI30.51
5829PXN0.51
2247FGF20.50
26248OR2K20.50
84303CHCHD60.50
3615IMPDH20.50
1813DRD20.50
80148PQLC10.50
390081OR52E40.50
352954GATS0.50
90871C9orf1230.50
50945TBX220.52
5204PFDN50.52
5338PLD20.52
94ACVRL10.52
54039PCBP30.52
7691ZNF1320.52
338APOB0.52
84658EMR30.52
283232TMEM800.52
5430POLR2A0.52
54623PAF10.52
11070TMEM1150.52
10395DLC10.52
57140RNPEPL10.52
79781IQCA10.52
1838DTNB0.52
51386EIF3L0.52
56919DHX330.52
57542KLHDC50.52
3628INPP10.52
4520MTF10.52
8547FCN30.52
60401EDA2R0.52
8082SSPN0.52
80755AARSD10.52
710SERPING10.52
56246MRAP0.52
10555AGPAT20.52
949SCARB10.52
23743BHMT20.52
3910LAMA40.52
60370AVPI10.52
5021OXTR0.52
55997CFC10.52
23144ZC3H30.52
56776FMN20.51
85456TNKS1BP10.51
283ANG0.51
7035TFPI0.51
51232CRIM10.51
112616CMTM70.51
22981NINL0.51
8727CTNNAL10.51
9902MRC20.51
10900RUNDC3A0.51
51299NRN10.51
79632FAM184A0.52
80820EEPD10.52
150709ANKAR0.52
6591SNAI20.52
10129FRY0.52
5166PDK40.52
146433IL340.52
118812MORN40.53
10516FBLN50.53
9463PICK10.53
127495LRRC390.53
7753ZNF2020.53
79827CLMP0.53
203260CCDC1070.53
83657DYNLRB20.53
Table S1

Up-regulated genes found in dataset GSE29174. There are 437 up-regulated genes in this list. Q-values reported by SAM were 0 for all genes in this list.

NCBI gene IDGene SymbolFold Change
1300COL10A142.74
3007HIST1H1D29.72
8366HIST1H4B25.58
6286S100P25.19
1301COL11A124.72
3627CXCL1017.83
4283CXCL915.88
1387CREBBP12.83
27299ADAMDEC112.78
54986ULK412.46
55771PRR1112.02
54790TET211.25
6241RRM210.60
3433IFIT210.49
6999TDO29.73
1656DDX69.72
55088C10orf1189.37
9648GCC29.24
6696SPP18.92
2803GOLGA48.57
83540NUF27.73
10112KIF20A7.66
9833MELK7.59
55165CEP557.50
10142AKAP97.44
9447AIM27.42
54443ANLN5.79
6710SPTB5.71
7272TTK5.64
10635RAD51AP15.49
4069LYZ5.37
55183RIF15.34
891CCNB15.34
91543RSAD25.31
81610FAM83D5.24
64581CLEC7A5.10
10051SMC45.02
4085MAD2L14.96
55872PBK4.83
991CDC204.82
9221NOLC14.74
2124EVI2B4.66
375248ANKRD364.66
1164CKS24.64
1230CCR14.62
890CCNA24.56
127933UHMK14.49
10274STAG14.45
597BCL2A14.43
55355HJURP4.41
54210TREM14.36
253558LCLAT14.26
2706GJB27.33
6498SKIL7.13
219285SAMD9L7.06
10261IGSF67.01
2335FN16.95
699BUB16.75
1058CENPA6.75
332BIRC56.73
51203NUSAP16.59
259266ASPM6.54
1063CENPF6.49
165918RNF1686.44
9232PTTG16.34
5996RGS16.07
29089UBE2T5.96
22974TPX25.94
4321MMP125.91
983CDK15.89
85444LRRCC15.87
29121CLEC2D3.83
4090SMAD53.80
2123EVI2A3.80
57695USP373.79
133418EMB3.76
4131MAP1B3.76
9787DLGAP53.75
9768KIAA01013.74
54625PARP143.73
2215FCGR3B3.71
9134CCNE23.70
3117HLA-DQA13.68
10380BPNT13.67
79056PRRG43.63
10673TNFSF13B3.63
8467SMARCA53.61
115908CTHRC13.61
3428IFI163.61
1520CTSS3.61
10797MTHFD23.57
55681SCYL23.57
9749PHACTR23.57
94240EPSTI13.56
64151NCAPG3.51
25879DCAF133.51
1033CDKN34.24
79801SHCBP14.23
126731C1orf964.21
6772STAT14.20
55729ATF7IP4.14
6713SQLE4.14
157570ESCO24.10
79871RPAP24.09
9493KIF234.09
4751NEK24.05
10631POSTN4.03
23515MORC34.02
7153TOP2A4.02
10403NDC804.00
10915TCERG13.99
57650KIAA15243.99
23049SMG13.93
80231CXorf213.87
5111PCNA3.86
79682MLF1IP3.11
29123ANKRD113.09
5429POLH3.09
701BUB1B3.07
200030NBPF113.06
55677IWS13.06
160418TMTC33.04
9147NEMF3.04
11320MGAT4A3.04
5238PGM33.03
2820GPD23.02
388886FAM211B3.01
7852CXCR43.00
57082CASC52.99
22926ATF62.98
7594ZNF432.98
968CD682.97
7171TPM42.96
11004KIF2C2.96
10808HSPH12.95
84909C9orf32.94
1894ECT22.93
1629DBT2.92
116969ART52.90
3227HOXC112.88
116064LRRC583.47
29899GPSM23.47
135114HINT33.45
27333GOLIM43.43
55839CENPN3.43
23213SULF13.41
81671VMP13.39
9889ZBED43.36
3092HIP13.34
51512GTSE13.34
92797HELB3.34
51426POLK3.30
5611DNAJC33.30
6596HLTF3.28
9910RABGAP1L3.25
528ATP6V1C13.23
3833KIFC13.23
197131UBR13.20
29923HILPDA3.20
28998MRPL133.19
58527C6orf1153.19
79000C1orf1353.19
9857CEP3503.18
84296GINS43.18
81034SLC25A323.15
55723ASF1B3.14
7110TMF13.14
84081NSRP13.14
23075SWAP703.12
6726SRP92.69
55215FANCI2.68
57590WDFY12.67
55142HAUS22.66
23047PDS5B2.66
5373PMM22.66
11065UBE2C2.66
23085ERC12.66
389197C4orf502.65
11260XPOT2.65
29980DONSON2.65
64399HHIP2.64
6453ITSN12.63
29108PYCARD2.63
9877ZC3H11A2.62
3149HMGB32.87
10437IFI302.87
57489ODF2L2.87
2151F2RL22.86
23215PRRC2C2.85
128710C20orf942.85
23594ORC62.84
5205ATP8B12.83
51430C1orf92.80
57405SPC252.80
112401BIRC82.80
3606IL182.80
115362GBP52.80
50515CHST112.79
83461CDCA32.79
10744PTTG22.78
51765MST42.77
10926DBF42.76
27125AFF42.75
10615SPAG52.75
55143CDCA82.74
51602NOP582.74
51478HSD17B72.73
2209FCGR1A2.73
9958USP152.72
5469MED12.72
8813DPM12.70
6731SRP722.70
9991PTBP32.70
79866BORA2.41
7072TIA12.40
55632G2E32.40
2213FCGR2B2.40
3987LIMS12.39
829CAPZA12.39
26973CHORDC12.38
435ASL2.38
29979UBQLN12.38
8548BLZF12.37
9694TTC352.37
55055ZWILCH2.36
4481MSR12.36
10213PSMD142.35
9966TNFSF152.35
81624DIAPH32.62
79723SUV39H22.61
55789DEPDC1B2.61
10097ACTR22.59
23036ZNF2922.58
22936ELL22.57
8477GPR652.57
23397NCAPH2.57
3015H2AFZ2.54
55749CCAR12.53
25937WWTR12.52
360023ZBTB412.51
5080PAX62.51
4193MDM22.51
24137KIF4A2.51
9212AURKB2.51
168850ZNF8002.50
55109AGGF12.49
23185LARP4B2.49
51571FAM49B2.49
51077FCF12.49
23167EFR3A2.49
23468CBX52.48
5396PRRX12.48
10096ACTR32.47
10308ZNF2672.47
6782HSPA132.47
3832KIF112.47
917CD3G2.47
80821DDHD12.46
52ACP12.46
4179CD462.46
10499NCOA22.44
60558GUF12.44
55676SLC30A62.43
6646SOAT12.43
5440POLR2K2.43
84955NUDCD12.42
54739XAF12.42
84295PHF62.23
7295TXN2.23
2710GK2.23
10905MAN1A22.22
6780STAU12.22
51582AZIN12.35
54843SYTL22.34
9039UBA32.33
933CD222.33
5685PSMA42.33
9885OSBPL22.33
9262STK17B2.33
56942C16orf612.32
10767HBS1L2.32
87178PNPT12.32
6303SAT12.32
7316UBC2.32
4205MEF2A2.32
85465EPT12.31
84640USP382.31
5810RAD12.30
64397ZFP1062.29
5706PSMC62.29
22948CCT52.29
10672GNA132.29
339344MYPOP2.28
7292TNFSF42.28
57103C12orf52.28
388403YPEL22.28
54876DCAF162.27
113235SLC46A12.27
11177BAZ1A2.27
339175METTL2A2.26
26586CKAP22.26
55785FGD62.26
24145PANX12.25
253461ZBTB382.25
23232TBC1D122.25
995CDC25C2.25
55974SLC50A12.25
472ATM2.25
23008KLHDC102.24
10024TROAP2.24
9521EEF1E12.24
7402UTRN2.09
55589BMP2K2.08
158747MOSPD22.08
56886UGGT12.07
203100HTRA42.07
10282BET12.22
134430WDR362.21
4299AFF12.21
6747SSR32.21
7334UBE2N2.21
5965RECQL2.21
4605MYBL22.2
6093ROCK12.19
161725OTUD7A2.19
23518R3HDM12.18
2239GPC42.18
28977MRPL422.18
64859OBFC2A2.18
3845KRAS2.18
51388NIP72.18
7586ZKSCAN12.18
10762NUP502.17
7328UBE2H2.17
10730YME1L12.17
23093TTLL52.17
6790AURKA2.17
22889KIAA09072.17
10875FGL22.17
23161SNX132.17
9169SCAF112.16
1788DNMT3A2.15
9088PKMYT12.15
23033DOPEY12.13
89882TPD52L32.13
6556SLC11A12.13
64216TFB2M2.13
3071NCKAP1L2.13
51068NMD32.13
509ATP5C12.13
953ENTPD12.13
51105PHF20L12.13
5062PAK22.13
9205ZMYM52.12
55157DARS22.12
8520HAT12.11
79739TTLL72.11
9495AKAP52.10
3181HNRNPA2B12.10
55279ZNF6542.07
54499TMCO12.07
81930KIF18A2.07
142686ASB142.06
55209SETD52.06
9736USP342.04
116285ACSM12.04
2201FBN22.04
963CD532.04
55159RFWD32.03
9871SEC24D2.03
9887SMG72.02
23376UFL12.02
79646PANK32.01
50613UBQLN32.00
201595STT3B2.00
59345GNB41.99
5876RABGGTB1.99
79820CATSPERB1.99
6637SNRPG1.99
51330TNFRSF12A1.99
9928KIF141.99
286097EFHA21.98
9131AIFM11.98
488ATP2A21.98
23042PDXDC11.98
7114TMSB4X1.98
9123SLC16A31.98
54454ATAD2B1.97
23143LRCH11.97
4212MEIS21.97
1457CSNK2A11.97
80012PHC31.97
128497SPATA251.96
186AGTR21.96
53981CPSF21.96
56996SLC12A91.96
1584CYP11B11.96
133619PRRC11.96
4288MKI671.96
9014TAF1B1.96
55858TMEM1651.96
2212FCGR2A1.96
389898UBE2NL2.10
29850TRPM52.10
3070HELLS2.10
331XIAP2.09
55751TMEM184C2.09
2146EZH22.09
26057ANKRD171.95
128061C1orf1311.95
64090GAL3ST21.94
130507UBR31.93
2298FOXD41.93
123169LEO11.93
57187THOC21.93
148789B3GALNT21.93
58508MLL31.92
5701PSMC21.92
148066ZNRF41.92
6670SP31.92
10075HUWE11.96
220988HNRNPA31.96
80146UXS11.95
122011CSNK1A1L1.95
150468CKAP2L1.95
84624FNDC11.95
7332UBE2L31.92
3336HSPE11.92
54800KLHL241.92
2290FOXG11.91
50848F11R1.91
10627MYL12A1.91
5074PAWR1.91
6476SI1.91
1009CDH111.90
29066ZC3H7A1.90
51319RSRC11.90
Table S4

Result of ROC curve analysis on our miRNA array data. ROC analysis was done to validate the diagnostic value of the miRNA in the miRNA-regulated PINs.

miRBase AccessionmiRNA nameAUCp-value
MIMAT0002856hsa-miR-520d-3p0.490.549112
MIMAT0000265hsa-miR-204-5p0.986.47 × 10−10 ***
MIMAT0000272hsa-miR-2150.210.999782
MIMAT0000271hsa-miR-214-3p0.680.010387 *
MIMAT0002820hsa-miR-497-5p0.992.75 × 10−10 ***
MIMAT0000076hsa-miR-21-5p0.780.000184 ***
MIMAT0000738hsa-miR-3830.600.106284
MIMAT0000423hsa-miR-125b-5p0.992.48 × 10−10 ***
MIMAT0000064hsa-let-7c0.933.79 × 10−8 ***
MIMAT0000089hsa-miR-31-5p0.808.63 × 10−5 ***
MIMAT0000077hsa-miR-22-3p0.270.99749
MIMAT0000098hsa-miR-100-5p0.985.55 × 10−10 ***
MIMAT0000097hsa-miR-99a-5p0.992.55 × 10−10 ***
MIMAT0000443hsa-miR-125a-5p0.310.990694
MIMAT0002819hsa-miR-193b-3p0.410.86128
MIMAT0000250hsa-miR-139-5p0.992.42 × 10−10 ***
MIMAT0000437hsa-miR-145-5p0.963.14 × 10−9 ***
MIMAT0000421hsa-miR-122-5p0.480.597483

AUC: area under (ROC) curve;

p-value < 0.05;

p-value < 0.001.

Table S5

Summary of constructed miRNA-regulated networks. L0 gene: genes connected directly to the miRNA (i.e., direct target of miRNA); L1 gene: genes not connected directly to the miRNA.

miRBase AccessionmiR nameTotal gene countL0 countL1 count
MIMAT0002819hsa-miR-193b-3p16115
MIMAT0000250hsa-miR-139-5p281018
MIMAT0000437hsa-miR-145-5p862264
MIMAT0000423hsa-miR-125b-5p21116195
MIMAT0000443hsa-miR-125a-5p20614192
MIMAT0000097hsa-miR-99a-5p14113
MIMAT0000265hsa-miR-204-5p641846
MIMAT0000076hsa-miR-21-5p911675
MIMAT0000064hsa-let-7c962076
MIMAT0000421hsa-miR-122-5p532
MIMAT0000098hsa-miR-100-5p14113
MIMAT0000272hsa-miR-215330
MIMAT0000271hsa-miR-214-3p1486
MIMAT0000738hsa-miR-38334331
MIMAT0002856hsa-miR-520d-3p14623123
MIMAT0000077hsa-miR-22-3p461135
MIMAT0002820hsa-miR-497-5p26732235
MIMAT0000089hsa-miR-31-5p34331
Table S6

Specific enriched GO terms of each miRNA-regulated PINs. Genes annotated with the specific GO term in the PIN were also listed in this table. Adj. p-value: multiple-test adjusted p-value calculated by the method described in the work of Benjamini and Yekutieli [48].

MIMAT0002856(hsa-miR-520d-3p)
GO termGenesAdj. p-value
GO:0007169, Transmembrane receptor protein tyrosine kinase signaling pathwaySH3KBP1, HDAC2, RET, ABI1, LYN, GRB2, SORBS1, CLTC, CLTA, CDC42, CASP9, RAF1, SRC, AP2A1, AP2B1, MAPK3, ARHGEF7, PRKCA, RPS6, PRKAR2B, MAPK1, ARHGEF6, CDK1, SH3GL2, EIF4G1, HDAC1, ECT2, MKNK1, CASP3, PRKACA, ADRB2, PRKAR2A, EIF4B, SHC1, RAC12.77 × 10−29
GO:0048011, Nerve growth factor receptor signaling pathwayHDAC2, GRB2, CLTC, CLTA, CASP9, RAF1, SRC, AP2A1, AP2B1, MAPK3, ARHGEF7, PRKCA, PRKAR2B, MAPK1, ARHGEF6, CDK1, SH3GL2, HDAC1, ECT2, CASP3, PRKACA, PRKAR2A, SHC1, RAC15.09 × 10−24
GO:0007173, Epidermal growth factor receptor signaling pathwaySH3KBP1, GRB2, CLTC, CLTA, CDC42, CASP9, RAF1, SRC, AP2A1, AP2B1, MAPK3, ARHGEF7, PRKCA, PRKAR2B, MAPK1, CDK1, SH3GL2, PRKACA, PRKAR2A, SHC12.96 × 10−22
GO:0043067, Regulation of programmed cell deathHDAC2, STK17B, ESR1, ABL1, LYN, TP53, GABRB3, PAK2, LCK, CASP9, RAF1, PLK1, ARHGEF7, PRKCA, RPS6, SH3RF1, MAPK1, IFT57, ARHGAP10, ARHGEF6, CDK1, APAF1, HDAC1, ECT2, CASP3, SOX10, EP300, ARAF, TFAP2A, ADRB2, HCK, KLHL20, CASP8, HIP1, RAC14.76 × 10−19
GO:0042058, Regulation of epidermal growth factor receptor signaling pathwaySH3KBP1, ESR1, GRB2, CLTC, CLTA, CDC42, AP2A1, AP2B1, ARHGEF7, SH3GL2, SHC12.36 × 10−12
GO:0008543, Fibroblast growth factor receptor signaling pathwayGRB2, CASP9, RAF1, SRC, MAPK3, PRKCA, PRKAR2B, MAPK1, CDK1, MKNK1, PRKACA, PRKAR2A, SHC12.39 × 10−12
GO:0043068, Positive regulation of programmed cell deathSTK17B, ABL1, LYN, TP53, LCK, CASP9, ARHGEF7, PRKCA, RPS6, SH3RF1, MAPK1, ARHGEF6, APAF1, ECT2, CASP3, EP300, TFAP2A, ADRB2, CASP8, HIP1, RAC13.09 × 10−12
GO:0010942, Positive regulation of cell deathSTK17B, ABL1, LYN, TP53, LCK, CASP9, ARHGEF7, PRKCA, RPS6, SH3RF1, MAPK1, ARHGEF6, APAF1, ECT2, CASP3, EP300, TFAP2A, ADRB2, CASP8, HIP1, RAC14.49 × 10−12
GO:0006917, Induction of apoptosisSTK17B, ABL1, TP53, LCK, CASP9, ARHGEF7, PRKCA, SH3RF1, MAPK1, ARHGEF6, APAF1, ECT2, CASP3, EP300, CASP8, HIP1, RAC16.91 × 10−11
GO:0012502, Induction of programmed cell deathSTK17B, ABL1, TP53, LCK, CASP9, ARHGEF7, PRKCA, SH3RF1, MAPK1, ARHGEF6, APAF1, ECT2, CASP3, EP300, CASP8, HIP1, RAC17.42 × 10−11
GO:0042059, Negative regulation of epidermal growth factor receptor signaling pathwaySH3KBP1, GRB2, CLTC, CLTA, CDC42, AP2A1, AP2B1, ARHGEF7, SH3GL28.63 × 10−11
GO:0015630, Microtubule cytoskeletonSTMN1, SORBS1, SMAD4, CLTC, CDC42, LCK, RACGAP1, PLK1, PRKAR2B, YES1, MAPK1, IFT57, CDK1, ECT2, PRKACA, RB1, EP300, CCNB1, CHAF1B, TFAP2A, CASP8, PRKAR2A4.75 × 10−10
GO:0060548, Negative regulation of cell deathHDAC2, ESR1, TP53, SMAD4, RAF1, PLK1, PRKCA, RPS6, SH3RF1, CDK1, HDAC1, CASP3, SOX10, ARAF, TFAP2A, HCK, KLHL206.27 × 10−8
GO:0008286, Insulin receptor signaling pathwayGRB2, SORBS1, RAF1, MAPK3, RPS6, MAPK1, CDK1, EIF4G1, EIF4B, SHC12.15 × 10−7
GO:0043069, Negative regulation of programmed cell deathHDAC2, ESR1, TP53, RAF1, PLK1, PRKCA, RPS6, SH3RF1, CDK1, HDAC1, CASP3, SOX10, ARAF, TFAP2A, HCK, KLHL203.13 × 10−7
GO:0008284, Positive regulation of cell proliferationHDAC2, ESR1, LYN, CDC42, E2F1, PRKCA, MAPK1, CDK1, RHOG, HDAC1, NCK1, SOX10, CCNB1, ADRB2, HCK, SHC18.34 × 10−7
GO:0051988, Regulation of attachment of spindle microtubules to kinetochoreCDC42, RACGAP1, ECT2, CCNB13.27 × 10−5
GO:0008629, Induction of apoptosis by intracellular signalsABL1, TP53, CASP9, APAF1, CASP3, EP300, CASP85.83 × 10−5
MIMAT0002820(hsa-miR-497-5p)
GO termGenesAdj.p-value
GO:0043067, Regulation of programmed cell deathESR1, MEN1, ABL1, HIPK3, PPARGC1A, SIAH1, SH3RF1, PAK2, LCK, MED1, PPARG, CBX4, ARHGEF7, YWHAB, RXRA, ACVR1, MAPK1, CASP3, CASP6, AR, PTPRF, MDM2, BRCA1, MLH1, RAB27A, PIAS4, FAF1, RAC1, VHL, SKI, NR4A1, LYN, TP53, PSMC2, GATA1, GATA6, GATA3, RAF1, CDKN1B, PLK1, PSMD11, HOXA13, RPS6, ESR2, ARHGAP10, ARHGEF6, SMAD3, SKIL, RYR2, PSEN1, HCK, TRAF22.67 × 10−25
GO:0043068, Positive regulation of programmed cell deathMEN1, ABL1, SIAH1, SH3RF1, LCK, PPARG, ARHGEF7, YWHAB, RXRA, MAPK1, CASP3, CASP6, PTPRF, BRCA1, MLH1, RAB27A, PIAS4, FAF1, RAC1, NR4A1, LYN, TP53, GATA6, CDKN1B, HOXA13, RPS6, ESR2, ARHGEF6, SMAD3, RYR2, PSEN1, TRAF21.74 × 10−17
GO:0010942, Positive regulation of cell deathMEN1, ABL1, SIAH1, SH3RF1, LCK, PPARG, ARHGEF7, YWHAB, RXRA, MAPK1, CASP3, CASP6, PTPRF, BRCA1, MLH1, RAB27A, PIAS4, FAF1, RAC1, NR4A1, LYN, TP53, GATA6, CDKN1B, HOXA13, RPS6, ESR2, ARHGEF6, SMAD3, RYR2, PSEN1, TRAF23.08 × 10−17
GO:0008285, Negative regulation of cell proliferationMEN1, MED1, PPARG, RXRA, CASP3, AR, PTPRF, VDR, VHL, SKI, LYN, TP53, TOB1, GATA1, GATA3, RAF1, HNF4A, CDKN1B, BRD7, MED25, ESR2, ABI1, SMAD1, SMAD2, SMAD3, SMAD4, SOX73.85 × 10−14
GO:0015629, Actin cytoskeletonABL1, SORBS1, FLNA, SEPT7, ANLN, MACF1, HAP1, SH3PXD2A, IQGAP2, BRCA1, ACTC1, ACTA1, MYL2, MYLK, SORBS2, ARPC4, ARPC5, ACTR2, ACTR3, ARPC1B, WASF1, WASF2, HCK2.52 × 10−13
GO:0006917, Induction of apoptosisABL1, SH3RF1, LCK, PPARG, ARHGEF7, YWHAB, MAPK1, CASP3, CASP6, BRCA1, MLH1, RAB27A, RAC1, NR4A1, TP53, CDKN1B, ARHGEF6, SMAD3, RYR2, PSEN1, TRAF29.69 × 10−11
GO:0012502, Induction of programmed cell deathABL1, SH3RF1, LCK, PPARG, ARHGEF7, YWHAB, MAPK1, CASP3, CASP6, BRCA1, MLH1, RAB27A, RAC1, NR4A1, TP53, CDKN1B, ARHGEF6, SMAD3, RYR2, PSEN1, TRAF21.06 × 10−10
GO:0007178, Transmembrane receptor protein serine/threonine kinase signaling pathwayACVR1, SMURF2, SKI, GDF6, BMP6, ZNF8, GATA4, HNF4A, SMAD1, SMAD2, SMAD3, SMAD4, SMAD5, RYR21.22 × 10−10
GO:0007169, Transmembrane receptor protein tyrosine kinase signaling pathwaySORBS1, CDC42, SRC, MAPK3, ARHGEF7, YWHAB, MAPK1, SH3GL2, CASP3, MDM2, EIF4G1, RAC1, SH3KBP1, NR4A1, LYN, GRB2, RAF1, CDKN1B, RPS6, ABI1, ARHGEF6, MKNK1, PSEN1, EIF4B1.67 × 10−10
GO:0090092, Regulation of transmembrane receptor protein serine/threonine kinase signaling pathwayMEN1, ACVR1, SMURF2, SKI, GDF6, TP53, BMP6, GATA4, GATA6, HOXA13, SMAD2, SMAD3, SMAD4, SKIL7.51 × 10−10
GO:0030509, BMP signaling pathwayACVR1, SMURF2, SKI, GDF6, BMP6, ZNF8, SMAD1, SMAD4, SMAD5, RYR23.25 × 10−9
GO:0060548, Negative regulation of cell deathESR1, HIPK3, PPARGC1A, SH3RF1, MED1, CBX4, ACVR1, CASP3, AR, MDM2, VHL, TP53, GATA1, GATA6, GATA3, RAF1, CDKN1B, PLK1, RPS6, SMAD3, SMAD4, PSEN1, HCK7.88 × 10−9
GO:0007173, Epidermal growth factor receptor signaling pathwayCDC42, SRC, MAPK3, ARHGEF7, YWHAB, MAPK1, SH3GL2, MDM2, SH3KBP1, NR4A1, GRB2, RAF1, CDKN1B9.22 × 10−9
GO:0030521, Androgen receptor signaling pathwayPPARGC1A, MED14, MED1, AR, BRCA1, MED12, PIAS1, RAN, NR1I31.42 × 10−8
GO:0043069, Negative regulation of programmed cell deathESR1, HIPK3, PPARGC1A, SH3RF1, MED1, CBX4, ACVR1, CASP3, AR, MDM2, VHL, TP53, GATA1, GATA6, GATA3, RAF1, CDKN1B, PLK1, RPS6, SMAD3, PSEN1, HCK2.44 × 10−8
GO:0048011, Nerve growth factor receptor signaling pathwaySRC, MAPK3, ARHGEF7, YWHAB, MAPK1, SH3GL2, CASP3, MDM2, RAC1, NR4A1, GRB2, RAF1, CDKN1B, ARHGEF6, PSEN12.64 × 10−8
GO:0032956, Regulation of actin cytoskeleton organizationABL1, LRP1, ARPC4, ARPC5, ACTR3, ARPC1B, SMAD3, NCK1, SORBS3, HCK, LIMK16.42 × 10−6
GO:0008543, Fibroblast growth factor receptor signaling pathwaySRC, MAPK3, YWHAB, MAPK1, MDM2, NR4A1, GRB2, RAF1, CDKN1B, MKNK19.06 × 10−6
GO:0042059, Negative regulation of epidermal growth factor receptor signaling pathwayCDC42, ARHGEF7, SH3GL2, PTPRF, SH3KBP1, GRB2, PSEN11.11 × 10−5
GO:0042058, Regulation of epidermal growth factor receptor signaling pathwayESR1, CDC42, ARHGEF7, SH3GL2, PTPRF, SH3KBP1, GRB2, PSEN11.17 × 10−5
GO:0007179, Transforming growth factor beta receptor signaling pathwayACVR1, SMURF2, SKI, SMAD1, SMAD2, SMAD3, SMAD4, SMAD51.67 × 10−5
GO:0015630, Microtubule cytoskeletonSTMN1, RIF1, SORBS1, CDC42, LCK, RACGAP1, YES1, YWHAB, MAPK1, SEPT7, KIF23, CDC16, MACF1, BRCA1, FEZ1, NCOR1, PLK1, CHD3, SMAD4, CEP350, CDC27, PSEN12.14 × 10−5
GO:0017015, Regulation of transforming growth factor beta receptor signaling pathwayMEN1, SMURF2, SKI, TP53, SMAD2, SMAD3, SMAD4, SKIL2.30 × 10−5
GO:0070302, Regulation of stress-activated protein kinase signaling cascadeMEN1, ZEB2, HIPK3, SH3RF1, CDC42, MAPK3, MAPK1, LYN, NCOR1, TRAF22.74 × 10−5
GO:0001959, Regulation of cytokine-mediated signaling pathwayHSP90AB1, MED1, PPARG, PTPRF, NR1H2, PIAS1, IL36RN, HIPK16.35 × 10−5
GO:0008284, Positive regulation of cell proliferationESR1, CDC42, MED1, RARA, MAPK1, AR, MDM2, NR4A1, LYN, FZR1, BMP6, GATA1, GATA4, GATA6, CDKN1B, NCK1, HCLS1, HCK7.29 × 10−5
MIMAT0000423(hsa-miR-125b-5p)
GO termGenesAdj.p-value
GO:0043067, Regulation of programmed cell deathHMGA2, PML, PRNP, FGF2, XRCC4, BRCA1, IGFBP3, HDAC3, CTNNB1, CD5, CDK1, NKX2-5, MEF2C, PRKCI, CASP2, PSMA4, PSMA3, CFDP1, CAV1, FAF1, YWHAB, HIF1A, RELA, TCF7L2, TNFSF12, PSEN2, TP53, TOP2A, TNFRSF4, BID, MYC, JUN, OGT, CDKN1A, HOXA13, RNF7, PPP2R4, HDAC2, HDAC1, SNCA, PTEN, NFKBIA, IFI16, NOL3, TRAF2, HSP90B14.56 × 10−24
GO:0060548, Negative regulation of cell deathHMGA2, PRNP, FGF2, XRCC4, HDAC3, CTNNB1, CDK1, NKX2-5, MEF2C, PRKCI, CFDP1, HIF1A, RELA, TCF7L2, PSEN2, TP53, TNFRSF4, MYC, JUN, CDKN1A, RNF7, HDAC2, HDAC1, SNCA, PTEN, MGMT, NFKBIA, NOL3, HSP90B18.04 × 10−17
GO:0008284, Positive regulation of cell proliferationHMGA2, FGF2, XRCC4, CDC25B, CTNNB1, EGR1, AGGF1, CDK1, NKX2-5, MEF2C, PRKCI, IRS1, HIF1A, RELA, HCLS1, TNFSF12, ARNT, PTPRC, TNFSF4, TNFRSF4, MYC, JUN, FGF1, CDKN1A, HDAC2, HDAC1, NOLC1, PTEN2.20 × 10−15
GO:0043069, Negative regulation of programmed cell deathHMGA2, PRNP, XRCC4, HDAC3, CTNNB1, CDK1, NKX2-5, MEF2C, PRKCI, CFDP1, HIF1A, RELA, TCF7L2, PSEN2, TP53, TNFRSF4, MYC, JUN, CDKN1A, RNF7, HDAC2, HDAC1, SNCA, PTEN, NFKBIA, NOL3, HSP90B13.62 × 10−15
GO:0043068, Positive regulation of programmed cell deathHMGA2, PML, BRCA1, IGFBP3, CTNNB1, CD5, MEF2C, PRKCI, CASP2, CAV1, FAF1, YWHAB, TNFSF12, PSEN2, TP53, TOP2A, BID, JUN, OGT, CDKN1A, HOXA13, RNF7, PPP2R4, PTEN, IFI16, TRAF24.51 × 10−14
GO:0010942, Positive regulation of cell deathHMGA2, PML, BRCA1, IGFBP3, CTNNB1, CD5, MEF2C, PRKCI, CASP2, CAV1, FAF1, YWHAB, TNFSF12, PSEN2, TP53, TOP2A, BID, JUN, OGT, CDKN1A, HOXA13, RNF7, PPP2R4, PTEN, IFI16, TRAF27.15 × 10−14
GO:0006916, Anti-apoptosisPRNP, HDAC3, CDK1, NKX2-5, MEF2C, PRKCI, CFDP1, RELA, TCF7L2, PSEN2, RNF7, HDAC1, SNCA, NFKBIA, NOL3, HSP90B11.25 × 10−10
GO:0008285, Negative regulation of cell proliferationSERPINF1, SRF, PML, PRNP, FGF2, CSNK2B, IGFBP3, CTNNB1, CAV1, HMGA1, VDR, CDH5, HSF1, COL18A1, TP53, MYC, JUN, CDKN1A, PAK1, PTEN2.08 × 10−9
GO:0015630, Microtubule cytoskeletonSTMN1, KIF1C, RANGAP1, CDC25B, BRCA1, HDAC3, CTNNB1, PIN4, HSPH1, RANBP9, CDK1, SPTAN1, YWHAQ, DVL1, FKBP4, YWHAB, CCDC85B, MAPT, PSEN2, TOP2A, SPIB, MYC, OGT, APEX1, PAFAH1B12.24 × 10−9
GO:0048011, Nerve growth factor receptor signaling pathwayHDAC3, CDK1, MEF2C, PRKCI, CASP2, IRS1, YWHAB, RELA, PSEN2, HDAC2, HDAC1, PTEN, ATF1, NFKBIA1.99 × 10−8
GO:0050678, Regulation of epithelial cell proliferationSERPINF1, PGR, FGF2, CTNNB1, AGGF1, CAV1, HIF1A, TNFSF12, ARNT, MYC, JUN, FGF1, PTEN3.41 × 10−8
GO:0007169, Transmembrane receptor protein tyrosine kinase signaling pathwayFGF2, HDAC3, CDK1, MEF2C, PRKCI, CASP2, IRS1, FIBP, PTPN1, YWHAB, RELA, PSEN2, FGF1, HDAC2, HDAC1, PTEN, ATF1, NFKBIA, EIF4EBP16.39 × 10−8
GO:0006917, Induction of apoptosisPML, BRCA1, CD5, CASP2, CAV1, YWHAB, TNFSF12, PSEN2, TP53, BID, OGT, CDKN1A, RNF7, PTEN, IFI16, TRAF21.68 × 10−7
GO:0012502, Induction of programmed cell deathPML, BRCA1, CD5, CASP2, CAV1, YWHAB, TNFSF12, PSEN2, TP53, BID, OGT, CDKN1A, RNF7, PTEN, IFI16, TRAF21.81 × 10−7
GO:0035666, TRIF-dependent toll-like receptor signaling pathwayATF2, CDK1, MEF2C, FOS, RELA, JUN, ATF1, NFKBIA2.40 × 10−6
GO:0034138, Toll-like receptor 3 signaling pathwayATF2, CDK1, MEF2C, FOS, RELA, JUN, ATF1, NFKBIA2.71 × 10−6
GO:0051693, Actin filament cappingSPTB, SPTBN1, SPTAN1, SPTA1, ADD1, EPB493.43 × 10−6
GO:0002756, MyD88- independent toll-like receptor signaling pathwayATF2, CDK1, MEF2C, FOS, RELA, JUN, ATF1, NFKBIA3.82 × 10−6
GO:0034134, Toll-like receptor 2 signaling pathwayATF2, CDK1, MEF2C, FOS, RELA, JUN, ATF1, NFKBIA5.33 × 10−6
GO:0034130, Toll-like receptor 1 signaling pathwayATF2, CDK1, MEF2C, FOS, RELA, JUN, ATF1, NFKBIA5.33 × 10−6
GO:0030835, Negative regulation of actin filament depolymerizationSPTB, SPTBN1, SPTAN1, SPTA1, ADD1, EPB495.58 × 10−6
GO:0015629, Actin cytoskeletonWAS, CDH1, BRCA1, SPTB, SPTBN1, SPTAN1, CTDP1, STX1A, SPTA1, PAK1, SNCA, ADD1, EPB41, EPB495.58 × 10−6
GO:0002755, MyD88-dependent toll-like receptor signaling pathwayATF2, CDK1, MEF2C, FOS, RELA, JUN, ATF1, NFKBIA7.66 × 10−6
GO:0034142, Toll-like receptor 4 signaling pathwayATF2, CDK1, MEF2C, FOS, RELA, JUN, ATF1, NFKBIA1.14 × 10−5
GO:0050679, Positive regulation of epithelial cell proliferationFGF2, CTNNB1, AGGF1, HIF1A, TNFSF12, ARNT, MYC, JUN, FGF11.14 × 10−5
MIMAT0000076(hsa-miR-21-5p)
GO termGenesAdj.p-value
GO:0030834, Regulation of actin filament depolymerizationSPTB, SPTBN1, SPTAN1, SPTA1, ADD1, EPB491.27 × 10−5
GO:0030837, Negative regulation of actin filament polymerizationSPTB, SPTBN1, SPTAN1, SPTA1, ADD1, EPB492.71 × 10−5
GO:0002224, Toll-like receptor signaling pathwayATF2, CDK1, MEF2C, FOS, RELA, JUN, ATF1, NFKBIA2.96 × 10−5
GO:0008629, Induction of apoptosis by intracellular signalsPML, BRCA1, YWHAB, TP53, BID, CDKN1A, RNF7, IFI163.52 × 10−5
GO:0043067, Regulation of programmed cell deathSPRY2, TP53, ADAMTSL4, ETS1, TDGF1, RAF1, HOXA5, HOXA13, MSX1, MSX2, NKX2-5, CBL, INHBB, COL4A3, ACVR1C, TRAF21.92 × 10−5
GO:0007173, Epidermal growth factor receptor signaling pathwaySPRY2, SPRY1, GRB2, PTPN11, TDGF1, RAF1, CBL9.44 × 10−5
MIMAT0000250(hsa-miR-139-5p)
GO termGenesAdj.p-value
GO:0035583, Negative regulation of transforming growth factor beta receptor signaling pathway by extracellular sequestering of TGFbetaLTBP1, FBN1, FBN22.61 × 10−5
MIMAT0000089(hsa-miR-31-5p)
GO termGenesAdj.p-value
GO:0007187, G-protein signaling, coupled to cyclic nucleotide second messengerGNA12, GNA13, DRD5, MTNR1A, S1PR3, TSHR, S1PR48.74 × 10−7
GO:0019935, Cyclic-nucleotidemediated signalingGNA12, GNA13, DRD5, MTNR1A, S1PR3, TSHR, S1PR41.60 × 10−6
GO:0007188, G-protein signaling, coupled to cAMP nucleotide second messengerGNA12, GNA13, DRD5, S1PR3, TSHR, S1PR46.82 × 10−6
GO:0048011, Nerve growth factor receptor signaling pathwayARHGEF1, PRKCD, ARHGEF12, PRKACA, PRKCE, MCF2, ARHGEF116.93 × 10−6
GO:0019933, CAMP-mediated signalingGNA12, GNA13, DRD5, S1PR3, TSHR, S1PR41.05 × 10−5
GO:0043067, Regulation of programmed cell deathPTK2B, PRKCD, TGFBR1, ARHGEF12, CTNNB1, PRKCE, TIA1, MCF2, FASTK, ARHGEF11, F2R1.25 × 10−5
GO:0003376, Sphingosine-1- phosphate signaling pathwayS1PR3, S1PR2, S1PR43.22 × 10−5
GO:0007169, Transmembrane receptor protein tyrosine kinase signaling pathwayPTK2B, ARHGEF1, PRKCD, ARHGEF12, PRKACA, PRKCE, MCF2, ARHGEF115.20 × 10−5
GO:0043068, Positive regulation of programmed cell deathTGFBR1, ARHGEF12, CTNNB1, PRKCE, TIA1, MCF2, FASTK, ARHGEF117.67 × 10−5
GO:0010942, Positive regulation of cell deathTGFBR1, ARHGEF12, CTNNB1, PRKCE, TIA1, MCF2, FASTK, ARHGEF118.78 × 10−5
GO:0006917, Induction of apoptosisTGFBR1, ARHGEF12, PRKCE, TIA1, MCF2, FASTK, ARHGEF119.32 × 10−5
GO:0012502, Induction of programmed cell deathTGFBR1, ARHGEF12, PRKCE, TIA1, MCF2, FASTK, ARHGEF119.51 × 10−5
MIMAT0000437(hsa-miR-145-5p)
GO termGenesAdj.p-value
GO:0030509, BMP signaling pathwayBMP6, ZNF8, ACVR1, SMAD1, SMAD4, RYR2, SMAD5, SMURF2, GDF61.37 × 10−11
GO:0007178, Transmembrane receptor protein serine/threonine kinase signaling pathwayBMP6, ZNF8, ACVR1, SMAD1, SMAD4, RYR2, SMAD5, SMURF2, GDF62.56 × 10−8
GO:0090092, Regulation of transmembrane receptor protein serine/threonine kinase signaling pathwayMEN1, TP53, BMP6, HOXA13, ACVR1, SMAD4, SULF1, SMURF2, GDF68.22 × 10−8
MIMAT0000064(hsa-let-7c)
GO termGenesAdj.p-value
GO:0043067, Regulation of programmed cell deathRRM2B, ACVR1, TP53, RASA1, TGFBR1, PSMA3, BIRC5, ACTN2, HOXA13, IRS2, FASTK, VAV1, PSMB6, BCL2, CDK1, HDAC1, SOX10, TIA1, AKT1, AURKB3.43 × 10−8
GO:0043069, Negative regulation of programmed cell deathRRM2B, ACVR1, TP53, RASA1, TGFBR1, BIRC5, IRS2, BCL2, CDK1, HDAC1, SOX10, AKT1, AURKB6.58 × 10−6
GO:0060548, Negative regulation of cell deathRRM2B, ACVR1, TP53, RASA1, TGFBR1, BIRC5, IRS2, BCL2, CDK1, HDAC1, SOX10, AKT1, AURKB8.92 × 10−6
GO:0015630, Microtubule cytoskeletonINCENP, SNTB2, SEPT1, TACC1, BIRC5, RACGAP1, PIN4, CDCA8, CDK1, PHF1, AKT1, AURKB, NINL, CCDC85B5.15 × 10−5
GO:0043067, Regulation of programmed cell deathHMGA2, PML, PRNP, FGF2, XRCC4, BRCA1, IGFBP3, HDAC3, CTNNB1, CD5, CDK1, NKX2-5, MEF2C, PRKCI, CASP2, PSMA4, PSMA3, CFDP1, CAV1, FAF1, YWHAB, HIF1A, RELA, TCF7L2, TNFSF12, PSEN2, TP53, TOP2A, TNFRSF4, BID, MYC, JUN, OGT, CDKN1A, RNF7, PPP2R4, HDAC2, HDAC1, SNCA, PTEN, NFKBIA, IFI16, NOL3, TRAF2, HSP90B11.62 × 10−23
GO:0060548, Negative regulation of cell deathHMGA2, PRNP, FGF2, XRCC4, HDAC3, CTNNB1, CDK1, NKX2-5, MEF2C, PRKCI, CFDP1, HIF1A, RELA, TCF7L2, PSEN2, TP53, TNFRSF4, MYC, JUN, CDKN1A, RNF7, HDAC2, HDAC1, SNCA, PTEN, MGMT, NFKBIA, NOL3, HSP90B14.53 × 10−17
GO:0008284, Positive regulation of cell proliferationHMGA2, FGF2, XRCC4, CDC25B, CTNNB1, EGR1, AGGF1, CDK1, NKX2-5, MEF2C, PRKCI, IRS1, HIF1A, RELA, HCLS1, TNFSF12, ARNT, PTPRC, TNFSF4, TNFRSF4, MYC, JUN, FGF1, CDKN1A, HDAC2, HDAC1, NOLC1, PTEN1.23 × 10−15
MIMAT0000443(hsa-miR-125a-5p)
GO termGenesAdj.p-value
GO:0043069, Negative regulation of programmed cell deathHMGA2, PRNP, XRCC4, HDAC3, CTNNB1, CDK1, NKX2-5, MEF2C, PRKCI, CFDP1, HIF1A, RELA, TCF7L2, PSEN2, TP53, TNFRSF4, MYC, JUN, CDKN1A, RNF7, HDAC2, HDAC1, SNCA, PTEN, NFKBIA, NOL3, HSP90B12.02 × 10−15
GO:0043068, Positive regulation of programmed cell deathHMGA2, PML, BRCA1, IGFBP3, CTNNB1, CD5, MEF2C, PRKCI, CASP2, CAV1, FAF1, YWHAB, TNFSF12, PSEN2, TP53, TOP2A, BID, JUN, OGT, CDKN1A, RNF7, PPP2R4, PTEN, IFI16, TRAF22.80 × 10−13
GO:0010942, Positive regulation of cell deathHMGA2, PML, BRCA1, IGFBP3, CTNNB1, CD5, MEF2C, PRKCI, CASP2, CAV1, FAF1, YWHAB, TNFSF12, PSEN2, TP53, TOP2A, BID, JUN, OGT, CDKN1A, RNF7, PPP2R4, PTEN, IFI16, TRAF24.35 × 10−13
GO:0006916, Anti-apoptosisPRNP, HDAC3, CDK1, NKX2-5, MEF2C, PRKCI, CFDP1, RELA, TCF7L2, PSEN2, RNF7, HDAC1, SNCA, NFKBIA, NOL3, HSP90B19.04 × 10−11
GO:0008285, Negative regulation of cell proliferationSERPINF1, SRF, PML, PRNP, FGF2, CSNK2B, IGFBP3, CTNNB1, CAV1, HMGA1, VDR, CDH5, HSF1, COL18A1, TP53, MYC, JUN, CDKN1A, PAK1, PTEN1.32 × 10−9
GO:0015630, Microtubule cytoskeletonSTMN1, KIF1C, RANGAP1, CDC25B, BRCA1, HDAC3, CTNNB1, PIN4, HSPH1, RANBP9, CDK1, SPTAN1, YWHAQ, DVL1, FKBP4, YWHAB, CCDC85B, MAPT, PSEN2, TOP2A, SPIB, MYC, OGT, APEX1, PAFAH1B11.32 × 10−9
GO:0048011, Nerve growth factor receptor signaling pathwayHDAC3, CDK1, MEF2C, PRKCI, CASP2, IRS1, YWHAB, RELA, PSEN2, HDAC2, HDAC1, PTEN, ATF1, NFKBIA1.50 × 10−8
GO:0050678, Regulation of epithelial cell proliferationSERPINF1, PGR, FGF2, CTNNB1, AGGF1, CAV1, HIF1A, TNFSF12, ARNT, MYC, JUN, FGF1, PTEN2.66 × 10−8
GO:0007169, Transmembrane receptor protein tyrosine kinase signaling pathwayFGF2, HDAC3, CDK1, MEF2C, PRKCI, CASP2, IRS1, FIBP, PTPN1, YWHAB, RELA, PSEN2, FGF1, HDAC2, HDAC1, PTEN, ATF1, NFKBIA, EIF4EBP14.37 × 10−8
GO:0006917, Induction of apoptosisPML, BRCA1, CD5, CASP2, CAV1, YWHAB, TNFSF12, PSEN2, TP53, BID, OGT, CDKN1A, RNF7, PTEN, IFI16, TRAF21.22 × 10−7
GO:0012502, Induction of programmed cell deathPML, BRCA1, CD5, CASP2, CAV1, YWHAB, TNFSF12, PSEN2, TP53, BID, OGT, CDKN1A, RNF7, PTEN, IFI16, TRAF21.31 × 10−7
GO:0035666, TRIF-dependent toll-like receptor signaling pathwayATF2, CDK1, MEF2C, FOS, RELA, JUN, ATF1, NFKBIA2.01 × 10−6
GO:0034138, Toll-like receptor 3 signaling pathwayATF2, CDK1, MEF2C, FOS, RELA, JUN, ATF1, NFKBIA2.27 × 10−6
GO:0051693, Actin filament cappingSPTB, SPTBN1, SPTAN1, SPTA1, ADD1, EPB492.97 × 10−6
GO:0002756, MyD88- independent toll-like receptor signaling pathwayATF2, CDK1, MEF2C, FOS, RELA, JUN, ATF1, NFKBIA3.20 × 10−6
GO:0015629, Actin cytoskeletonWAS, CDH1, BRCA1, SPTB, SPTBN1, SPTAN1, CTDP1, STX1A, SPTA1, PAK1, SNCA, ADD1, EPB41, EPB494.25 × 10−6
GO:0034134, Toll-like receptor 2 signaling pathwayATF2, CDK1, MEF2C, FOS, RELA, JUN, ATF1, NFKBIA4.43 × 10−6
GO:0034130, Toll-like receptor 1 signaling pathwayATF2, CDK1, MEF2C, FOS, RELA, JUN, ATF1, NFKBIA4.43 × 10−6
GO:0030835, Negative regulation of actin filament depolymerizationSPTB, SPTBN1, SPTAN1, SPTA1, ADD1, EPB494.72 × 10−6
GO:0002755, MyD88-dependent toll-like receptor signaling pathwayATF2, CDK1, MEF2C, FOS, RELA, JUN, ATF1, NFKBIA6.41 × 10−6
GO:0050679, Positive regulation of epithelial cell proliferationFGF2, CTNNB1, AGGF1, HIF1A, TNFSF12, ARNT, MYC, JUN, FGF19.31 × 10−6
GO:0034142, Toll-like receptor 4 signaling pathwayATF2, CDK1, MEF2C, FOS, RELA, JUN, ATF1, NFKBIA9.31 × 10−6
GO:0030834, Regulation of actin filament depolymerizationSPTB, SPTBN1, SPTAN1, SPTA1, ADD1, EPB491.09 × 10−5
GO:0030837, Negative regulation of actin filament polymerizationSPTB, SPTBN1, SPTAN1, SPTA1, ADD1, EPB492.37 × 10−5
GO:0002224, Toll-like receptor signaling pathwayATF2, CDK1, MEF2C, FOS, RELA, JUN, ATF1, NFKBIA2.48 × 10−5
GO:0008629, Induction of apoptosis by intracellular signalsPML, BRCA1, YWHAB, TP53, BID, CDKN1A, RNF7, IFI162.92 × 10−5
GO:0050851, Antigen receptor-mediated signaling pathwayWAS, MEF2C, RELA, PSEN2, PTPRC, PAK1, PTEN, NFKBIA8.55 × 10−5
GO:0002221, Pattern recognition receptor signaling pathwayATF2, CDK1, MEF2C, FOS, RELA, JUN, ATF1, NFKBIA9.15 × 10−5
GO:0001936, Regulation of endothelial cell proliferationFGF2, AGGF1, CAV1, HIF1A, TNFSF12, ARNT, JUN9.47 × 10−5
MIMAT0000077(hsa-miR-22-3p)
GO termGenesAdj.p-value
GO:0035583, Negative regulation of transforming growth factor beta receptor signaling pathway by extracellular sequestering of TGFbetaFBN2, FBN1, LTBP11.45 × 10−5
MIMAT0000265(hsa-miR-204-5p)
GO termGenesAdj.p-value
GO:0035583, Negative regulation of transforming growth factor beta receptor signaling pathway by extracellular sequestering of TGFbetaFBN1, FBN2, LTBP16.31 × 10−5
Table S7

GOBO survival analysis results. Genes which was annotated with the specified GO term of proteins in the PIN would be used as input gene set for GOBO analysis.

miRNAGO termp-value
MIMAT0000064(hsa-let-7c)GO:0015630, Microtubule cytoskeleton9.97 × 10−6***
GO:0043067, Regulation of programmed cell death0.268067
GO:0043069, Negative regulation of programmed cell death0.0390439*
GO:0060548, Negative regulation of cell death0.0390439*
MIMAT0000076(hsa-miR-21-5p)GO:0007173, Epidermal growth factor receptor signaling pathway0.721139
GO:0043067, Regulation of programmed cell death0.266312
MIMAT0000077(hsa-miR-22-3p)GO:0035583, Negative regulation of transforming growth factor beta receptor signaling pathway by extracellular sequestering of TGFbeta0.940727
MIMAT0000089(hsa-miR-31-5p)GO:0003376, Sphingosine-1-phosphate signaling pathway0.062202
GO:0006917, Induction of apoptosis0.048584*
GO:0007169, Transmembrane receptor protein tyrosine kinase signaling pathway0.050408
GO:0007187, G-protein signaling, coupled to cyclic nucleotide second messenger0.289466
GO:0007188, G-protein signaling, coupled to cAMP nucleotide second messenger0.687572
GO:0010942, Positive regulation of cell death0.356228
GO:0012502, Induction of programmed cell death0.048584*
GO:0019933, CAMP-mediated signaling0.687572
GO:0019935, Cyclic-nucleotide-mediated signaling0.289466
GO:0043067, Regulation of programmed cell death0.694486
GO:0043068, Positive regulation of programmed cell death0.356228
GO:0048011, Nerve growth factor receptor signaling pathway0.154543
MIMAT0000250(hsa-miR-139-5p)GO:0035583, Negative regulation of transforming growth factor beta receptor signaling pathway by extracellular sequestering of TGFbeta0.940727
MIMAT0000265(hsa-miR-204-5p)GO:0035583, Negative regulation of transforming growth factor beta receptor signaling pathway by extracellular sequestering of TGFbeta0.940727
MIMAT0000423(hsa-miR-125b-5p)GO:0002224, Toll-like receptor signaling pathway0.380928
GO:0002755, MyD88-dependent toll-like receptor signaling pathway0.380928
GO:0002756, MyD88-independent toll-like receptor signaling pathway0.380928
GO:0006916, Anti-apoptosis0.0593
GO:0006917, Induction of apoptosis0.618064
GO:0007169, Transmembrane receptor protein tyrosine kinase signaling pathway0.776269
GO:0008284, Positive regulation of cell proliferation0.882324
GO:0008285, Negative regulation of cell proliferation0.883393
GO:0008629, Induction of apoptosis by intracellular signals0.073118
GO:0010942, Positive regulation of cell death0.972892
GO:0012502, Induction of programmed cell death0.618064
GO:0015629, Actin cytoskeleton0.596528
GO:0015630, Microtubule cytoskeleton0.028245*
GO:0030834, Regulation of actin filament depolymerization0.654383
GO:0030835, Negative regulation of actin filament depolymerization0.654383
GO:0030837, Negative regulation of actin filament polymerization0.654383
GO:0034130, Toll-like receptor 1 signaling pathway0.380928
GO:0034134, Toll-like receptor 2 signaling pathway0.380928
GO:0034138, Toll-like receptor 3 signaling pathway0.380928
GO:0034142, Toll-like receptor 4 signaling pathway0.380928
GO:0035666, TRIF-dependent toll-like receptor signaling pathway0.380928
GO:0043067, Regulation of programmed cell death0.643418
GO:0043068, Positive regulation of programmed cell death0.972892
GO:0043069, Negative regulation of programmed cell death0.492576
GO:0048011, Nerve growth factor receptor signaling pathway0.171634
GO:0050678, Regulation of epithelial cell proliferation0.002205**
GO:0050679, Positive regulation of epithelial cell proliferation0.205483
GO:0051693, Actin filament capping0.654383
GO:0060548, Negative regulation of cell death0.413665
MIMAT0000437(hsa-miR-145-5p)GO:0007178, Transmembrane receptor protein serine/threonine kinase signaling pathway0.196953
GO:0030509, BMP signaling pathway0.196953
GO:0090092, Regulation of transmembrane receptor protein serine/threonine kinase signaling pathway0.843529
MIMAT0000443(hsa-miR-125a-5p)GO:0001936, Regulation of endothelial cell proliferation0.115146
GO:0002221, Pattern recognition receptor signaling pathway0.380928
GO:0002224, Toll-like receptor signaling pathway0.380928
GO:0002755, MyD88-dependent toll-like receptor signaling pathway0.380928
GO:0002756, MyD88-independent toll-like receptor signaling pathway0.380928
GO:0006916, Anti-apoptosis0.0593
GO:0006917, Induction of apoptosis0.618064
GO:0007169, Transmembrane receptor protein tyrosine kinase signaling pathway0.776269
GO:0008284, Positive regulation of cell proliferation0.882324
GO:0008285, Negative regulation of cell proliferation0.883393
GO:0008629, Induction of apoptosis by intracellular signals0.073118
GO:0010942, Positive regulation of cell death0.972892
GO:0012502, Induction of programmed cell death0.618064
GO:0015629, Actin cytoskeleton0.596528
GO:0015630, Microtubule cytoskeleton0.028245*
GO:0030834, Regulation of actin filament depolymerization0.654383
GO:0030835, Negative regulation of actin filament depolymerization0.654383
GO:0030837, Negative regulation of actin filament polymerization0.654383
GO:0034130, Toll-like receptor 1 signaling pathway0.380928
GO:0034134, Toll-like receptor 2 signaling pathway0.380928
GO:0034138, Toll-like receptor 3 signaling pathway0.380928
GO:0034142, Toll-like receptor 4 signaling pathway0.380928
GO:0035666, TRIF-dependent toll-like receptor signaling pathway0.380928
GO:0043067, Regulation of programmed cell death0.643418
GO:0043068, Positive regulation of programmed cell death0.972892
GO:0043069, Negative regulation of programmed cell death0.492576
GO:0048011, Nerve growth factor receptor signaling pathway0.171634
GO:0050678, Regulation of epithelial cell proliferation0.002205**
GO:0050679, Positive regulation of epithelial cell proliferation0.205483
GO:0050851, Antigen receptor-mediated signaling pathway0.103325
GO:0051693, Actin filament capping0.654383
GO:0060548, Negative regulation of cell death0.413665
MIMAT0002820(hsa-miR-497-5p)GO:0001959, Regulation of cytokine-mediated signaling pathway0.06699
GO:0006917, Induction of apoptosis0.142401
GO:0007169, Transmembrane receptor protein tyrosine kinase signaling pathway0.837635
GO:0007173, Epidermal growth factor receptor signaling pathway0.387447
GO:0007178, Transmembrane receptor protein serine/threonine kinase signaling pathway0.240167
GO:0007179, Transforming growth factor beta receptor signaling pathway0.017876*
GO:0008284, Positive regulation of cell proliferation0.430255
GO:0008285, Negative regulation of cell proliferation0.149994
GO:0008543, Fibroblast growth factor receptor signaling pathway0.521978
GO:0010942, Positive regulation of cell death0.237692
GO:0012502, Induction of programmed cell death0.142401
GO:0015629, Actin cytoskeleton0.228804
GO:0015630, Microtubule cytoskeleton0.18331
GO:0017015, Regulation of transforming growth factor beta receptor signaling pathway0.128773
GO:0030509, BMP signaling pathway0.39837
GO:0030521, Androgen receptor signaling pathway0.383811
GO:0032956, Regulation of actin cytoskeleton organization0.91762
GO:0042058, Regulation of epidermal growth factor receptor signaling pathway0.934045
GO:0042059, Negative regulation of epidermal growth factor receptor signaling pathway0.789492
GO:0043067, Regulation of programmed cell death0.111544
GO:0043068, Positive regulation of programmed cell death0.237692
GO:0043069, Negative regulation of programmed cell death0.856892
GO:0048011, Nerve growth factor receptor signaling pathway0.471986
GO:0060548, Negative regulation of cell death0.667437
GO:0070302, Regulation of stress-activated protein kinase signaling cascade0.561032
GO:0090092, Regulation of transmembrane receptor protein serine/threonine kinase signaling pathway0.182314
MIMAT0002856(hsa-miR-520d-3p)GO:0006917, Induction of apoptosis0.489781
GO:0007169, Transmembrane receptor protein tyrosine kinase signaling pathway0.171689
GO:0007173, Epidermal growth factor receptor signaling pathway0.449696
GO:0008284, Positive regulation of cell proliferation0.05916
GO:0008286, Insulin receptor signaling pathway0.237933
GO:0008543, Fibroblast growth factor receptor signaling pathway0.159318
GO:0008629, Induction of apoptosis by intracellular signals0.502822
GO:0010942, Positive regulation of cell death0.076906
GO:0012502, Induction of programmed cell death0.489781
GO:0015630, Microtubule cytoskeleton0.000292***
GO:0042058, Regulation of epidermal growth factor receptor signaling pathway0.762633
GO:0042059, Negative regulation of epidermal growth factor receptor signaling pathway0.826854
GO:0043067, Regulation of programmed cell death0.740092
GO:0043068, Positive regulation of programmed cell death0.076906
GO:0043069, Negative regulation of programmed cell death0.067499
GO:0048011, Nerve growth factor receptor signaling pathway0.014926*
GO:0051988, Regulation of attachment of spindle microtubules to kinetochore7.48 × 10−6***
GO:0060548, Negative regulation of cell death0.213035

p < 0.05,

p < 0.01;

p < 0.001.

Table S8

Pathophysiogical characteristics of miRNA array data used in ROC curve analysis.

Sample NameERPRHERTNMStageGrade
S621T1000pT1N0M0I2
S434T1101T2N1M0IIB3
S403T1010T2N1M0IIB2
S459T1100T4N0M1IV1
S455N1100T3N3M1IV3
S545T1000pT2N0M0IIA3
S173N1001T2N3M0IIIC3
S363T1110T2N1M0IIB1
S909T1111pT3N3aM0IIIC(Unknown)
S645T1011pT1bN0M0I3
S898T1100pT2N0(i-)M0IIA(Unknown)
S201T1100T1N1M0IIA2
S631T1111T2N3aM1IV2
S303T1001T2N0M0IIA2
S502T1000pT3N0M0IIB3
S498N1111pT1cN1aM0IIA2
S536T1101T1cN1miM0IIA2
S660T1001T1N0M0I3
S358N1001T2pN2M0IIIA2
S665T1000T2N3M0IIIC3
S475T1001pT1cN0M0I3
S423T1110T2N3M0IIIC1
S507T1000pT1cN1aM0IIA3
S891T1100pT2NxM0IIA2
S422T1110T2N0M0IIA1
S961T1001T2N2aM0IIIA2
S622T1000pT2N0M0IIA2
S454T1000T1cN0M0I2
S433T1100T2N0M0IIA1
S673T1000pT2N0M0IIA2
S450T1001T2N1M0IIB3
S430T1110T2N3M0IIIC2
S437T1100T3N1M0IIIA3
S574T1000T2N0M0IIA2
S401T1110T1N0M0I1
S427N1000T3N1M0IIIA2
S894T1000pT1cN0M0I2
S929T1100pT2N0M0IIA2
S173T1001T2N3M0IIIC3
S622N1000pT2N0M0IIA2
S562T1111pT1aN0M0I3
S602T1000pT1cN0M0I3
S490T1111pT2N0M0IIA2
S677T1000pT2N1M0IIB3
S881T1000pT2N1aM0IIB3
S619T1000pT2N0M0IIA3
S446T1000T2N2M0IIIA3
S446T2000T2N2M0IIIA3
S453T1111T2N1M0IIB2
S562N1111pT1aN0M0I3
S557T1000pT2N0M0IIA3
S594T1111pT2N3aM0IIIC3
S582T1000pT2N0M0IIA3
S358T1001T2pN2M0IIIA2
S368T1001T3N3M0IIIC2
S175T1110T3N3MxIIIC3
S357T1000T2pN1M0IIIC3
S653T1000pT3N3aM0IIIC2
S722T1111pT1N0M0I3
S593T1000pT1N0M0I3
S543T1111pT2N1M0IIB2
S498T1111pT1cN1aM0IIA2
S389T1100T2N1M0IIB3
S614T1011TxN0M1IIIA(Unknown)
S536N1101T1cN1miM0IIA2
S462T1110T3N3M0IIIC2
S477T1000pT1cN0M0I3
S917T1000T2N0M0IIA(Unknown)
S213T1001T1cN1M0IIA3
S628T1000T1N0M0I2
S291T1000T3pN2M0IIIA3
S593N1000pT1N0M0I3
S418T1110T2N3M0IIIC2
S420T1110T1N0M0I2
S363N1110T2N1M0IIB1
S629T1101pT1N0M0I2
S586T1111pT2N3aM0IIIC2
S415T1010T2N2M0IIIA2
S439T1110T2N2M0IIIA2
S941N1000pT1cN1aM0IIA3
S893T1000pT2N1micM0IIB3
S380T1100T2N1M0IIB2
S906N1111pT3N3aM0IIIC2
S918T1000pT2N0M0IIA3
S400T1001T2N0M0IIA2
S328T1000T1cpN0M0I3
S367T1100T1N1M0IIIA1
S420N1110T1N0M0I2
S922T1000pT1cN0(i-)M0I3
S896T1001pT2N1aM0IIB2
S410T1011T1N0M0I3
S572T1001T4N1aM0IIIC3
S448T1000T1N0M0I3
S207T1001T2N0M0IIA3
S604T1001pT2N1M0IIB3
S379T1001T2N2M0IIIA2
S906T1111pT3N3aM0IIIC2
S941T2000pT1cN1aM0IIA3
S941T1000pT1cN1aM0IIA3
S375T1110T1N0M0I2
S427T1000T3N1M0IIIA2
S417T1000pT2N0M0IIA3
S180T1100T4NxM0IIIC
S455T1100T3N3M1IV
S887T1000pT2N0M0IIA
S445T1101T2N1M0IIB
S469T1110T2N0M0IIA
S469T2110T2N0M0IIA
S483T1110T2N3M0IIIC
S444T1110T1NxM0(Unknown)
S909N1111pT3N3aM0IIIC
S464T1110T1N0M0I
S894N1000pT1cN0M0I
S698T1111pT1cN0M0I
S452T1000T1N0M0I
S474T1(Unknown)(Unknown)(Unknown)(Unknown)(Unknown)
  50 in total

1.  Significance analysis of microarrays applied to the ionizing radiation response.

Authors:  V G Tusher; R Tibshirani; G Chu
Journal:  Proc Natl Acad Sci U S A       Date:  2001-04-17       Impact factor: 11.205

2.  Using expression profiling data to identify human microRNA targets.

Authors:  Jim C Huang; Tomas Babak; Timothy W Corson; Gordon Chua; Sofia Khan; Brenda L Gallie; Timothy R Hughes; Benjamin J Blencowe; Brendan J Frey; Quaid D Morris
Journal:  Nat Methods       Date:  2007-11-18       Impact factor: 28.547

3.  miRNPs: a novel class of ribonucleoproteins containing numerous microRNAs.

Authors:  Zissimos Mourelatos; Josée Dostie; Sergey Paushkin; Anup Sharma; Bernard Charroux; Linda Abel; Juri Rappsilber; Matthias Mann; Gideon Dreyfuss
Journal:  Genes Dev       Date:  2002-03-15       Impact factor: 11.361

Review 4.  microRNAs as oncogenes and tumor suppressors.

Authors:  Baohong Zhang; Xiaoping Pan; George P Cobb; Todd A Anderson
Journal:  Dev Biol       Date:  2006-08-16       Impact factor: 3.582

Review 5.  Androgens and androgen receptors in breast cancer.

Authors:  B Nicolás Díaz-Chico; F Germán Rodríguez; Ana González; Raquel Ramírez; Cristina Bilbao; A Cabrera de León; A Aguirre Jaime; Ricardo Chirino; Domingo Navarro; Juan C Díaz-Chico
Journal:  J Steroid Biochem Mol Biol       Date:  2007-05-17       Impact factor: 4.292

6.  A microRNA in a multiple-turnover RNAi enzyme complex.

Authors:  György Hutvágner; Phillip D Zamore
Journal:  Science       Date:  2002-08-01       Impact factor: 47.728

7.  Altered MicroRNA expression confined to specific epithelial cell subpopulations in breast cancer.

Authors:  Lorenzo F Sempere; Mette Christensen; Asli Silahtaroglu; Mads Bak; Catherine V Heath; Gary Schwartz; Wendy Wells; Sakari Kauppinen; Charles N Cole
Journal:  Cancer Res       Date:  2007-12-15       Impact factor: 12.701

8.  An evaluation of human protein-protein interaction data in the public domain.

Authors:  Suresh Mathivanan; Balamurugan Periaswamy; T K B Gandhi; Kumaran Kandasamy; Shubha Suresh; Riaz Mohmood; Y L Ramachandra; Akhilesh Pandey
Journal:  BMC Bioinformatics       Date:  2006-12-18       Impact factor: 3.169

9.  Human Protein Reference Database--2009 update.

Authors:  T S Keshava Prasad; Renu Goel; Kumaran Kandasamy; Shivakumar Keerthikumar; Sameer Kumar; Suresh Mathivanan; Deepthi Telikicherla; Rajesh Raju; Beema Shafreen; Abhilash Venugopal; Lavanya Balakrishnan; Arivusudar Marimuthu; Sutopa Banerjee; Devi S Somanathan; Aimy Sebastian; Sandhya Rani; Somak Ray; C J Harrys Kishore; Sashi Kanth; Mukhtar Ahmed; Manoj K Kashyap; Riaz Mohmood; Y L Ramachandra; V Krishna; B Abdul Rahiman; Sujatha Mohan; Prathibha Ranganathan; Subhashri Ramabadran; Raghothama Chaerkady; Akhilesh Pandey
Journal:  Nucleic Acids Res       Date:  2008-11-06       Impact factor: 16.971

10.  The microRNA.org resource: targets and expression.

Authors:  Doron Betel; Manda Wilson; Aaron Gabow; Debora S Marks; Chris Sander
Journal:  Nucleic Acids Res       Date:  2007-12-23       Impact factor: 16.971
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  24 in total

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Authors:  Lijun Shen; Zhihong Wan; Yuming Ma; Libing Wu; Fangfang Liu; Hong Zang; Shaojie Xin
Journal:  Tumour Biol       Date:  2014-11-28

Review 2.  MiR-139-5p: promising biomarker for cancer.

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Journal:  Tumour Biol       Date:  2015-02-19

3.  Genes interconnecting AMPK and TREM-1 and associated microRNAs in rotator cuff tendon injury.

Authors:  Finosh G Thankam; Chandra S Boosani; Matthew F Dilisio; R Michael Gross; Devendra K Agrawal
Journal:  Mol Cell Biochem       Date:  2018-10-10       Impact factor: 3.396

4.  Low expression of miR-195 is associated with cell proliferation, glycolysis and poor survival in estrogen receptor (ER)-positive but not in triple negative breast cancer.

Authors:  Yoshihisa Tokumaru; Masanori Oshi; Ankit Patel; Eriko Katsuta; Li Yan; Fernando A Angarita; Subhamoy Dasgupta; Masayuki Nagahashi; Nobuhisa Matsuhashi; Manabu Futamura; Kazuhiro Yoshida; Kazuaki Takabe
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5.  miR-139-5p is a regulator of metastatic pathways in breast cancer.

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Journal:  RNA       Date:  2013-10-24       Impact factor: 4.942

6.  Editorial on the Special Issue: Regulation by non-coding RNAs.

Authors:  Nicholas Delihas
Journal:  Int J Mol Sci       Date:  2013-11-06       Impact factor: 5.923

7.  MicroRNA-21 as a potential diagnostic biomarker for breast cancer patients: a pooled analysis of individual studies.

Authors:  Ying Gao; Qiliang Cai; Yubei Huang; Shu Li; Hongxi Yang; Li Sun; Kexin Chen; Yaogang Wang
Journal:  Oncotarget       Date:  2016-06-07

8.  Involvement of miR-30c in resistance to doxorubicin by regulating YWHAZ in breast cancer cells.

Authors:  Y Fang; H Shen; Y Cao; H Li; R Qin; Q Chen; L Long; X L Zhu; C J Xie; W L Xu
Journal:  Braz J Med Biol Res       Date:  2014-01-10       Impact factor: 2.590

9.  Kruppel-like factor 4 signals through microRNA-206 to promote tumor initiation and cell survival.

Authors:  C-C Lin; S B Sharma; M K Farrugia; S L McLaughlin; R J Ice; Y V Loskutov; E N Pugacheva; K M Brundage; D Chen; J M Ruppert
Journal:  Oncogenesis       Date:  2015-06-08       Impact factor: 7.485

10.  The Potential Diagnostic Accuracy of Let-7 Family for Cancer: A Meta-Analysis.

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