OR Oncology Reports 1021-335X 1791-2431 D.A. Spandidos 10.3892/or.2016.4560 or-35-04-2257 Articles A transcriptional miRNA-gene network associated with lung adenocarcinoma metastasis based on the TCGA database WANGYUBO HANRUI CHENZHAOJUN FUMING KANGJUN LIKUNLIN LILI CHENHENGYI HEYONG Department of Respiratory Medicine, Daping Hospital, Third Military Medical University, Chongqing 400042, P.R. China Correspondence to: Dr Yong He, Department of Respiratory Medicine, Daping Hospital, Third Military Medical University, Chongqing 400042, P.R. China, E-mail: heyong@tmmu.edu.cn 04 2016 14 01 2016 35 4 2257 2269 29 08 2015 20 11 2015 Copyright © 2016, Spandidos Publications 2016

Lung adenocarcinoma is the most common subtype of non-small cell lung cancer (NSCLC), leading to the largest number of cancer-related deaths worldwide. The high mortality rate may be attributed to the delay of detection. Therefore, it is of great importance to explore the mechanism of lung adenocarcinoma metastasis and the strategy to block metastasis of the disease. We searched and downloaded mRNA and miRNA expression data and clinical data from The Cancer Genome Atlas (TCGA) database to identify differences in mRNA and miRNA expression of primary tumor tissues from lung adenocarcinoma that did and did not metastasize. In addition, combined with bioinformatic prediction, we constructed an miRNA-target gene regulatory network. Finally, we employed RT-qPCR to validate the bioinformatic approach by determining the expression of 10 significantly differentially expressed genes which were also putative targets of several dysregulated miRNAs. RT-qPCR results indicated that the bioinformatic approach in our study was acceptable. Our data suggested that some of the genes including PKM2, STRAP and FLT3, may participate in the pathology of lung adenocarcinoma metastasis and could be applied as potential markers or therapeutic targets for lung adenocarcinoma.

 bioinformatic analysis differentially expressed genes differentially expressed miRNAs TCGA lung adenocarcinoma Introduction

Lung adenocarcinoma, the most common subtype of non-small cell lung cancer (NSCLC), leads to one million deaths each year, affecting an increasing percentage of the population over the past few years (1). Despite advances in suitable therapies, the 5-year survival of lung adenocarcinoma patients remains low. The high mortality rate may be attributed to the delay of detection, since patients are asymptomatic in early stages. Local and distant metastases occur in most cases by the time symptoms are obvious, resulting in treatment failure in advanced NSCLC (2). Therefore, it is of great importance to explore the mechanism of NSCLC metastasis and strategies to block metastasis of the disease.

As post-transcriptional modulators, microRNAs (miRNAs) are a class of endogenous, non-coding, single-stranded RNAs with 21–24 nucleotides (3). Dysregulation of miRNAs contributes to many pathological conditions, such as the initiation and progression of lung cancer. A number of studies have assessed the potential role of miRNA signatures to classify histological subtypes (4) or to predict diagnosis (5), metastasis, recurrence or survival of NSCLC patients (6–9). Wang et al revealed a set of 24 differentially expressed miRNAs between NSCLC metastatic primary loci and non-metastatic primary loci in a mouse model (9). Larzabal et al showed that miR-205 overexpression inhibited metastasis of NSCLC by targeting integrin α5 (a pro-invasive protein) upon TMPRSS4 blockade, which was confirmed by in vivo and in vitro experiments (10).

High throughput data, such as gene expression data from RNA sequencing or microarrays, could be widely used in the exploration of molecular mechanisms that drive tumor behavior. The Cancer Genome Atlas (TCGA) database, a publically available database, offers a multilayered view of genomic and epigenomic data of approximately 10,000 patient samples together with clinicopathological information across more than 30 human cancer types, which is a rich resource for data mining and biological discovery.

Based on the fact that the collection of lung adenocarcinoma specimens that have metastasized to other organs is difficult, investigation of the early molecular events underlying lung adenocarcinoma metastasis is difficult. Toward this end, we analyzed mRNA and miRNA expression data and clinical data derived from TCGA to identify differences in mRNA and miRNA expression in primary tumor tissues from lung adenocarcinoma that did and did not metastasize. In addition, combined with bioinformatic prediction, we constructed an miRNA-target gene regulatory network, suggesting the regulation of the beginning of lung adenocarcinoma metastasis.

 Materials and methods TCGA gene expression profiles

Lung adenocarcinoma level 3 mRNA and miRNA expression data, and the corresponding clinical information, were downloaded from the TCGA data portal (https://tcga-data.nci.nih.gov/tcga/). The tumor staging information for all patient samples was derived from TCGA clinical information. Gene expression data were available for 291 lung adenocarcinoma samples without metastases, and 248 lung adenocarcinoma samples with lymph node metastasis or distant metastases. The miRNA expression data were available for 186 lung adenocarcinoma samples without metastases, and 144 lung adenocarcinoma with metastases.

 Ranking of differentially expressed genes and miRNAs

The raw expression data of all lung adenocarcinoma patients was downloaded, and transformed into log2 scale. Z-score normalization was also employed. The Limma (Linear Models for Microarray Data) package in R was used to identify the differentially expressed probe sets between the metastatic primary loci and non-metastatic primary loci by two-tailed Student's t-test, and p-values from the same platform were obtained. MetaMA package in R was used to combine p-values from different platforms, and the false discovery rate (FDR) was calculated for multiple comparisons using the Benjamini and Hochberg method. We selected differentially expressed mRNAs and miRNAs with criterion of FDR <0.05. Hierarchical clustering of differentially expressed genes was performed using the 'heatmap.2' function of the R/Bioconductor package 'gplots' (11).

 Target gene prediction of differentially expressed miRNAs

To understand the potential association between differentially expressed mRNAs and miRNAs obtained in the study, we predicted the transcriptional targets of the identified miRNAs using the online tools of miRWalk (http://www.umm.uni-heidelberg.de/apps/zmf/mirwalk/) (12) based on six bioinformatic algorithms (DIANAmT, miRanda, miRDB, miRWalk, PicTar and TargetScan). Target genes that were commonly predicted by ≥4 algorithms or experimentally validated based on miRWalk database, were considered as putative targets.

 Construction of the regulatory network of miRNA-target genes

Given that miRNAs tend to decrease the expression of their target genes, we matched putative target genes with the list of differentially expressed genes between the metastatic primary loci and non-metastatic primary loci to increase the accuracy of target prediction. We selected miRNA-target pairs whose expression was inversely correlated, to subject to further investigation (13–15). We conducted miRNA-target gene interaction networks with miRNA-target gene interacting pairs, whose expression levels are inversely correlated, and the miRNA regulation networks were visualized by Cytoscape (16).

 Functional annotation

Functional enrichment analysis is essential to uncover biological functions of miRNA target genes. To gain insight into the biological function of the miRNA target genes, we performed Gene Ontology (GO) classification and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis based on the online software GENECODIS (17). GO which includes three categories such as biological process, molecular function and cellular component, provides a common descriptive framework of gene annotation and classification for analyzing gene set data. The KEGG pathway enrichment analysis was also performed to detect the potential pathway of miRNA target genes based on the KEGG pathway database, which is a recognized and comprehensive database including all types of biochemistry pathways (18). FDR <0.05 was set as the cut-off for selecting significantly enriched functional GO terms and KEGG pathways.

 Validating the expression of lung adenocarcinoma metastasis-associated miRNA target genes

Based on the published studies, hsa-mir-133a-1 and hsa-let-7d were closely linked to the metastatic ability of lung adenocarcinoma (19,20). In our study, SurvMicro (http://bioinformatica.mty.itesm.mx:8080/Biomatec/Survmicro.jsp), an online validation tool, was used to evaluate the association of miRNA expression with survival (21) in human cancer datasets.

Furthermore, the expression of putative targets of hsa-mir-133a-1 (PKM2, PTBP1, FSCN1 and STRAP) and hsa-let-7d (KIT, BCL2, CNTN3, CISH, FLT3 and ESR2) was detected in primary tumor tissues from 10 lung adenocarcinoma patients by RT-qPCR, to validate the differential expression observed in the bioinformatic analysis. The 2−ΔΔCt method was used to analyze the data of RT-qPCR. SPSS version 13.0 was used to perform all statistical analyses. The sequences of the primers used are provided in Table I.

Among the 10 patients included, 5 presented with metastasis and 5 were without metastasis. The clinical specimens were provided by Daping Hospital. Written informed consent forms were received from the patients or legal guardians of the patients. All protocols and documents were approved by the Medical Ethics Committee of Daping Hospital. Frozen sections were prepared from the tumor tissues for the cytological or histological diagnosis. The resected tumor tissues were stored in liquid nitrogen until RNA extraction.

 Results Differences in miRNA and mRNA expression in lung adenocarcinoma with or without metastasis

Based on clinical information 'AJCC pathologic tumor stage' in the TCGA lung adenocarcinoma, we classified these data into lung adenocarcinoma with or without metastasis. After the gene expression data were downloaded, we performed differential expression analysis of the genes and miRNAs between the metastatic primary loci and non-metastatic primary loci. Genes (1,257) were identified to be differentially expressed under a threshold of FDR <0.05, with 585 upregulated and 672 downregulated genes in the lung adenocarcinoma with metastasis. The hierarchical clustering of differentially expressed genes is shown in Fig. 1. Fifty-eight miRNAs were identified as differentially expressed under the threshold of FDR <0.05, with 44 upregulated and 14 downregulated miRNAs in the lung adenocarcinoma samples with metastasis (Table II).

 The regulatory network of differentially expressed mRNAs and miRNAs associated with lung adenocarcinoma metastasis

To find the potential link between the differentially expressed mRNAs and miRNAs observed in the bioinformatic analysis, we predicted the putative targets of the identified miRNAs in the miRWalk database. Matching predicted putative targets with those found to be disregulated genes in metastatic primary tumors, miRNA-target gene pairs whose expression levels were inversely correlated were selected. As a result, we identified 1,154 miRNA-target gene pairs for the upregulated miRNAs with 61 pairs validated by previous experiments, and 474 miRNA-target gene pairs for the downregulated miRNAs with 57 pairs validated by previous experiments (Table III).

Using the 770 miRNA-target gene pairs, an miRNA-target gene regulatory network was constructed (Fig. 2). In the miRNA-target gene regulatory network, we identified the top 10 miRNAs which regulated the most target genes, such as hsa-miR-7, hsa-miR-182, hsa-miR-324-3p, hsa-miR-139-5p, hsa-miR-130b, hsa-let-7f, hsa-miR-18a, hsa-miR-188-5p, hsa-let-7d, and hsa-miR-590-5p, and the target genes such as RPS6KA3, TSC1, AIM1, GAS7, GFOD1, GGA2, IGF1, IL28RA, and INSR were regulated by the most miRNAs.

 GO classification and KEGG pathways of the miRNA target genes

We performed the GO classification and KEGG pathway enrichment analysis for miRNA target genes whose expression was differentially expressed. We found that lymphoid progenitor cell differentiation (GO:0002320, FDR=4.75E-04) and hematopoietic progenitor cell differentiation (GO:0002244, FDR=2.08E-03) were significantly enriched for biological processes. While for molecular functions, signaling receptor activity (GO:0038023, FDR=9.24E-04) and signal transducer activity (GO:0004871, FDR=8.77E-04) were significantly enriched (Table IV). The most significant pathway in our KEGG analysis was DNA replication (FDR=1.78E-04). Furthermore, T cell receptor signaling pathway (FDR=1.12E-03) and endocytosis (FDR=1.17E-03) were also highly enriched (Table V).

 Validating the expression of lung adenocarcinoma metastasis-associated miRNA target genes

By the online tool of SurvMicro, survival analysis was performed to evaluate the correlation between miRNA expression level (hsa-miR-133a-1 and hsa-let-7d) and the overall survival time of the lung adenocarcinoma patients. With the data from TCGA database, Kaplan-Meier curves indicated that the expression of hsa-mir-133a-1 was significantly correlated with the overall survival time of lung adenocarcinoma patients (P=0.03654), while the expression of hsa-let-7d was not significantly correlated with the overall survival time of the lung adenocarcinoma patients (P=0.114) (Fig. 3).

We performed RT-qPCR for the putative target genes of hsa-mir-133a-1 and hsa-let-7d, which exhibited significantly differential expression between the primary tumor tissues from lung adenocarcinoma with metastasis and that from the lung adenocarcinoma patiemts without metastasis in the bioinformatic analysis. In the RT-qPCR assay, most of the target genes showed similar expression patterns to what was observed in the bioinformatic analysis. The expression levels of the selected upregulated and downregulated genes are shown in Fig. 4. Among the upregulated genes in the lung adenocarcinoma with metastasis, PKM2 and STRAP mRNA expression was significantly increased (P=0.04, P=0.05), and PTBP1 mRNA expression was also increased, but not obviously, while FSCN1 mRNA expression was decreased. Among the downregulated genes in the lung adenocarcinoma with metastasis, FLT3 mRNA expression was significantly decreased (P=0.023), and BCL2, ESR2, and KIT mRNA expression was also mildly decreased. In contrast, CISH mRNA expression was significantly increased (P=0.027). CNTN3 mRNA expression appeared unchanged between the metastatic primary loci and the non-metastatic primary loci.

 Discussion

Due to a substantially lower cost, high-throughput gene expression data are becoming widely available. A key question in the field is how to use the data to identify both biological drivers and strong metastatic markers. In this study, we compared miRNA and mRNA expression data differences between lung adenocarcinoma with metastasis and lung adenocarcinoma without metastasis from the TCGA data portal with the aim to screen the miRNAs and genes associated with lung adenocarcinoma metastasis.

In line with previous findings, some of the identified differentially expressed miRNAs were found to be implicated in lung adenocarcinoma metastasis, such as hsa-mir-98, hsa-let-7d, hsa-mir-134, hsa-mir-196b, hsa-mir-381, hsa-mir-133a, hsa-mir-552, hsa-mir-944, hsa-mir-550, and hsa-mir-655. miR-98 was also found to be overexpressed in lung cancer cell lines, and it binds the 3′UTR of Fus1, a tumor suppressor, to inhibit protein expression (22). Despite no reports related to lung adenocarcinoma metastasis, a recent study uncovered the regulatory roles of miR-98 in melanoma metastasis (23). miR-134 and miR-655, belonging to the same cluster on chromosome 14q32, were demonstrated to be involved in TGF-β1-induced EMT which is recognized as a key element of cell invasion, migration, and metastasis, by directly targeting MAGI2, a scaffold protein required for PTEN (24). Directly targeting oncogenic receptors, such as IGF-1R, TGFBR1, and EGFR, miR-133a inhibits cell invasiveness and cell growth in lung cancer cells. Furthermore, an in vivo animal model showed that miR-133a markedly suppressed the metastatic ability of lung cancer cells (19). An miRNA microarray revealed that the expression level of miR-552 in colorectal cancer metastases in the lung was 39 times higher than that of primary lung adenocarcinomas, suggesting its possible roles in cancer metastases (25). miR-944 affected NSCLC cell growth, proliferation, and invasion by targeting a tumor suppressor, SOCS4. miR-944 was found to be associated with lymph node metastasis by determining its expression in 52 formalin-fixed paraffin-embedded SCC tissues (26).

More importantly, two miRNAs, hsa-mir-133a and hsa-let-7d, were found to be involved in lung adenocarcinoma metastasis by previous studies. Consequently, we performed survival analysis to evaluate the correlation between miRNA expression level and the overall survival time of lung adenocarcinoma patients, and we found that the expression of hsa-mir-133a was significantly correlated with the overall survival of lung adenocarcinoma, while the expression of hsa-let-7d was not significantly correlated with the survival of lung adenocarcinoma.

miR-133a was identified to function as a tumor suppressor in NSCLCs directly targeting several membrane receptors including IGF-1R, TGFBR1 and EGFR. In addition, by using the in vivo animal model, ectopically expressing miR-133a markedly suppressed the metastatic ability of the lung cancer cells, which may be applied in the clinical therapy of lung cancer in the future. Accordingly, NSCLC patients with higher expression levels of miR-133a had longer survival rates compared with those with lower miR-133a expression levels (19).

Let-7d, one of the members of the let-7 family, is deregulated in many types of cancers (27–30). Let-7d is involved in cellular differentiation, epithelial-to-mesenchymal transition (EMT) as a switch between EMT and MET (mesenchymal-epithelial transition), and regulates tumor-initiating cell (TIC) formation (31). Mairinger et al showed that let-7d expression was significantly associated with overall survival in pulmonary neuroendocrine tumors, suggesting its important role in metastasis and tumor progression (20).

The putative targets of hsa-mir-133a and hsa-let-7d were subjected to RT-qPCR validation in primary tumor tissues from 5 lung adenocarcinoma patients with metastasis and 5 lung adenocarcinoma patients without metastasis. The result of the RT-qPCR assay revealed that most of the genes showed similar expression patterns to what were observed in the bioinformatic analysis, suggesting that the findings in the bioinformatic analysis of the differential gene expression data were credible. PKM2, as a common putative target of both hsa-mir-133a and hsa-mir-552, was significantly upregulated at the mRNA level in the lung adenocarcinoma with metastasis. Interesting, the protein level of PKM2 was upregulated in the lung adenocarcinoma tissues compared with the paired surrounding normal tissue, which was also correlated with chemotherapy resistance, the severity of epithelial dysplasia, as well as a relatively poor prognosis (32,33), indicating that PKM2 could be used as a tumor marker for diagnosis and, in particular, as a potential target for cancer therapy.

STRAP, a putative target of hsa-mir-133a, was significantly increased at the mRNA level in lung adenocarcinoma with metastasis. As a WD domain-containing protein, STRAP inhibits TGF-β signaling through interaction with receptors and Smad7, and promotes growth and enhances tumorigenicity. Similarly, strong upregulation of STRAP was also observed in lung tumors by immunoblot analyses (34).

FLT3, a common putative target of hsa-let-7d, hsa-mir-196b and hsa-mir-376c, was significantly upregulated at the mRNA level among the downregulated genes in the lung adenocarcinoma with metastasis. It was found that in a mouse model of lung metastases treatment with the Flt3 ligand significantly reduced the number of lung metastases after laparotomy or radiation therapy, and thus prolonged survival (35,36). Unlike the findings in the bioinformatic analysis, CISH mRNA expression was significantly increased. As negative feedback regulators of JAK-STAT and several other signalling pathways, CISH, a putative common target of hsa-let-7d, hsa-mir-382, hsa-mir-9-3, hsa-mir-9-2 and hsa-mir-9-1, was recently found to be involved in the development of many solid organ and haematological malignancies (37). Prostate cancer LNCaP-S17 cells were resistant to exogenous IL-6-induced neuroendocrine differentiation due to increased levels of CISH and SOCS7 that blocked activation of the JAK2-STAT3 pathways (38).

Functional enrichment analysis of miRNA target genes showed that those genes were highly correlated with carcinogenesis. The most significantly enriched pathway based on the KEGG database was DNA replication. Based on the fact that cancer cells are characterized by uncontrolled cell proliferation properties, deregulation of DNA replication may promote the process of carcinogenesis. In addition, in cancer cells several DNA replication-initiation proteins, such as CDC6 and minichromosome maintenance (MCM) proteins, were overexpressed (39,40).

There are two limitations in this study. Firstly, the expression levels of the miRNAs were not determined in the metastatic primary loci compared with non-metastatic primary loci of lung adenocarcinomas. Secondly, the small size of the clinical samples for RT-qPCR validation was also a limitation of our study, which may be the reason that the RT-qPCR results were not significant. Given that surgery is not recommended for the traditional stage IIIB-IV patients with lung adenocarcinoma classified according to the taxonomy of cancer staging (TNM) system, it is difficult to obtain specimens of metastatic lung adenocarcinoma. Thus, detailed further studies are needed to investigate the selected miRNAs and the corresponding target genes in lung adenocarcinomas metastasis.

In summary, by comparing the difference in the miRNA and mRNA expression profiling in lung adenocarcinomas with and without metastases, an miRNA-regulated network involved in lung adenocarcinoma metastasis was identified combined with the bioinformatic analysis. RT-qPCR results indicated that the bioinformatic approach in our study was acceptable. Our results may contribute to the identification of markers which could be used to detect lung adenocarcinoma metastasis and assess prognosis.

 References AlbergAJFordJGSametJMAmerican College of Chest PhysiciansEpidemiology of lung cancer: ACCP evidence-based clinical practice guidelines (2nd edition)Chest132Suppl 329S55S200710.1378/chest.07-134717873159 XieLYangZLiGShenLXiangXLiuXXuDXuLChenYTianZGenome-wide identification of bone metastasis-related microRNAs in lung adenocarcinoma by high-throughput sequencingPLoS One8e61212201310.1371/journal.pone.0061212235934343620207 BartelDPMicroRNAs: Genomics, biogenesis, mechanism, and functionCell116281297200410.1016/S0092-8674(04)00045-514744438 ZhangYKZhuWYHeJYChenDDHuangYYLeHBLiuXGmiRNAs expression profiling to distinguish lung squamous-cell carcinoma from adenocarcinoma subtypesJ Cancer Res Clin Oncol13816411650201210.1007/s00432-012-1240-022618509 ZhengDHaddadinSWangYGuLQPerryMCFreterCEWangMXPlasma microRNAs as novel biomarkers for early detection of lung cancerInt J Clin Exp Pathol45755862011219046333160609 LandiMTZhaoYRotunnoMKoshiolJLiuHBergenAWRubagottiMGoldsteinAMLinnoilaIMarincolaFMMicroRNA expression differentiates histology and predicts survival of lung cancerClin Cancer Res16430441201010.1158/1078-0432.CCR-09-1736200680763163170 YanaiharaNCaplenNBowmanESeikeMKumamotoKYiMStephensRMOkamotoAYokotaJTanakaTUnique microRNA molecular profiles in lung cancer diagnosis and prognosisCancer Cell9189198200610.1016/j.ccr.2006.01.02516530703 YuSLChenHYChangGCChenCYChenHWSinghSChengCLYuCJLeeYCChenHSMicroRNA signature predicts survival and relapse in lung cancerCancer Cell134857200810.1016/j.ccr.2007.12.00818167339 WangRChenXFShuYQPrediction of non-small cell lung cancer metastasis-associated microRNAs using bioinformaticsAm J Cancer Res532512015256289194300719 LarzabalLde AberasturiALRedradoMRuedaPRodriguezMJBodegasMEMontuengaLMCalvoATMPRSS4 regulates levels of integrin α5 in NSCLC through miR-205 activity to promote metastasisBr J Cancer110764774201410.1038/bjc.2013.761244344353915125 ReimersMCareyVJBioconductor: An open source framework for bioinformatics and computational biologyMethods Enzymol411119134200610.1016/S0076-6879(06)11008-316939789 DweepHStichtCPandeyPGretzNmiRWalk - database: Prediction of possible miRNA binding sites by 'walking' the genes of three genomesJ Biomed Inform44839847201110.1016/j.jbi.2011.05.00221605702 LuoZZhangLLiZLiXLiGYuHJiangCDaiYGuoXXiangJAn in silico analysis of dynamic changes in microRNA expression profiles in stepwise development of naso-pharyngeal carcinomaBMC Med Genomics53201210.1186/1755-8794-5-3 LionettiMBiasioloMAgnelliLTodoertiKMoscaLFabrisSSalesGDeliliersGLBicciatoSLombardiLIdentification of microRNA expression patterns and definition of a microRNA/mRNA regulatory network in distinct molecular groups of multiple myelomaBlood114e20e26200910.1182/blood-2009-08-23749519846888 EnerlyESteinfeldIKleiviKLeivonenSKAureMRRussnesHGRønnebergJAJohnsenHNavonRRødlandEmiRNA-mRNA integrated analysis reveals roles for miRNAs in primary breast tumorsPLoS One6e16915201110.1371/journal.pone.0016915213649383043070 ShannonPMarkielAOzierOBaligaNSWangJTRamageDAminNSchwikowskiBIdekerTCytoscape: A software environment for integrated models of biomolecular interaction networksGenome Res1324982504200310.1101/gr.123930314597658403769 Tabas-MadridDNogales-CadenasRPascual-MontanoAGeneCodis3: A non-redundant and modular enrichment analysis tool for functional genomicsNucleic Acids Res40W478W483201210.1093/nar/gks402225731753394297 AltermannEKlaenhammerTRPathwayVoyager: Pathway mapping using the Kyoto Encyclopedia of Genes and Genomes (KEGG) databaseBMC Genomics660200510.1186/1471-2164-6-60158697101112592 WangLKHsiaoTHHongTMChenHYKaoSHWangWLYuSLLinCWYangPCMicroRNA-133a suppresses multiple oncogenic membrane receptors and cell invasion in non-small cell lung carcinomaPLoS One9e96765201410.1371/journal.pone.0096765248168134016005 MairingerFDTingSWernerRWalterRFHagerTVollbrechtCChristophDWormKMairingerTSheu-GrabellusSYDifferent micro-RNA expression profiles distinguish subtypes of neuroendocrine tumors of the lung: results of a profiling studyMod Pathol2716321640201410.1038/modpathol.2014.7424875640 Aguirre-GamboaRGomez-RuedaHMartínez-LedesmaEMartínez-TorteyaAChacolla-HuaringaRRodriguez-BarrientosATamez-PeñaJGTreviñoVSurvExpress: An online biomarker validation tool and database for cancer gene expression data using survival analysisPLoS One8e74250201310.1371/journal.pone.0074250240661263774754 DuLSchagemanJJSubausteMCSaberBHammondSMPrudkinLWistubaIIJiLRothJAMinnaJDmiR-93, miR-98, and miR-197 regulate expression of tumor suppressor gene FUS1Mol Cancer Res712341243200910.1158/1541-7786.MCR-08-0507196716782741087 LiFLiXJQiaoLShiFLiuWLiYDangYPGuWJWangXGLiuWmiR-98 suppresses melanoma metastasis through a negative feedback loop with its target gene IL-6Exp Mol Med46e116201410.1038/emm.2014.63252772114221693 KitamuraKSeikeMOkanoTMatsudaKMiyanagaAMizutaniHNoroRMinegishiYKubotaKGemmaAmiR-134/487b/655 cluster regulates TGF-β-induced epithelial-mesenchymal transition and drug resistance to gefitinib by targeting MAGI2 in lung adenocarcinoma cellsMol Cancer Ther13444453201410.1158/1535-7163.MCT-13-0448 KimJLimNJJangSGKimHKLeeGKmiR-592 and miR-552 can distinguish between primary lung adenocarcinoma and colorectal cancer metastases in the lungAnticancer Res3422972302201424778034 MaJMannoorKGaoLTanAGuarneraMAZhanMShettyAStassSAXingLJiangFCharacterization of microRNA transcriptome in lung cancer by next-generation deep sequencingMol Oncol812081219201410.1016/j.molonc.2014.03.019247851864198444 BoyerinasBParkSMHauAMurmannAEPeterMEThe role of let-7 in cell differentiation and cancerEndocr Relat Cancer17F19F36201010.1677/ERC-09-0184 AliSSalehHSethiSSarkarFHPhilipPAMicroRNA profiling of diagnostic needle aspirates from patients with pancreatic cancerBr J Cancer10713541360201210.1038/bjc.2012.383229298863494446 NagadiaRPanditPComanWBCooper-WhiteJPunyadeeraCmiRNAs in head and neck cancer revisitedCell Oncol (Dordr)3617201310.1007/s13402-012-0122-4 LiuXChenZYuJXiaJZhouXMicroRNA profiling and head and neck cancerComp Funct Genomics2009837514200910.1155/2009/8375142688814 KolendaTPrzybyłaWTeresiakAMackiewiczALamperskaKMThe mystery of let-7d - a small RNA with great powerContemp Oncol (Pozn)182933012014 PengXCGongFMZhaoYWZhouLXXieYWLiaoHLLinHJLiZYTangMHTongAPComparative proteomic approach identifies PKM2 and cofilin-1 as potential diagnostic, prognostic and therapeutic targets for pulmonary adenocarcinomaPLoS One6e27309201110.1371/journal.pone.0027309220872863210781 ShiHSLiDZhangJWangYSYangLZhangHLWangXHMuBWangWMaYSilencing of pkm2 increases the efficacy of docetaxel in human lung cancer xenografts in miceCancer Sci10114471453201010.1111/j.1349-7006.2010.01562.x20507318 AnumanthanGHalderSKFriedmanDBDattaPKOncogenic serine-threonine kinase receptor-associated protein modulates the function of Ewing sarcoma protein through a novel mechanismCancer Res661082410832200610.1158/0008-5472.CAN-06-159917108118 CarterJJFeingoldDLOhAKirmanIWildbrettPStapletonGAsiZFowlerRBhagatGHuangEHPerioperative immunomodulation with Flt3 kinase ligand or a whole tumor cell vaccine is associated with a reduction in lung metastasis formation after laparotomy in miceSurg Innov134147200610.1177/15533506060130010716708154 ChakravartyPKAlfieriAThomasEKBeriVTanakaKEVikramBGuhaCFlt3-ligand administration after radiation therapy prolongs survival in a murine model of metastatic lung cancerCancer Res59602860321999 SasiWSharmaAKMokbelKThe role of suppressors of cytokine signalling in human neoplasmsMol Biol Int2014630797201410.1155/2014/630797247575653976820 GeDGaoACZhangQLiuSXueYYouZLNCaP prostate cancer cells with autocrine interleukin-6 expression are resistant to IL-6-induced neuroendocrine differentiation due to increased expression of suppressors of cytokine signalingProstate7213061316201210.1002/pros.22479222130963665156 KarakaidosPTaravirasSVassiliouLVZacharatosPKastrinakisNGKougiouDKouloukoussaMNishitaniHPapavassiliouAGLygerouZOverexpression of the replication licensing regulators hCdt1 and hCdc6 characterizes a subset of non-small-cell lung carcinomas: Synergistic effect with mutant p53 on tumor growth and chromosomal instability - evidence of E2F-1 transcriptional control over hCdt1Am J Pathol16513511365200410.1016/S0002-9440(10)63393-7154663991618634 LiJNFengCJLuYJLiHJTuZLiaoGQLiangCmRNA expression of the DNA replication-initiation proteins in epithelial dysplasia and squamous cell carcinoma of the tongueBMC Cancer8395200810.1186/1471-2407-8-395

The hierarchical clustering of the top 100 differentially expressed genes between the metastatic primary loci and non-metastatic primary loci.

The regulatory network between miRNAs and target genes associated with lung adenocarcinoma metastasis. The diamonds and ellipses represent the miRNAs and genes, respectively. The red and green colors represent relatively high and low expression, respectively.

Kaplan-Meier survival curves show the correlation between the miRNA expression levels (hsa-mir-133a-1 and hsa-let-7d) and the overall survival time of lung adenocarcinoma patients.

RT-qPCR validation of 10 significantly (A) upregulated or (B) downregulated differentially expressed genes (DEGs) in primary tumor tissues from 5 lung adenocarcinoma patients with metastasis and 5 lung adenocarcinoma patients without metastasis. *P<0.05.

List of primers designed for the RT-qPCR experiments.

Primer Primer sequence (5′ to 3′) size (bp) Product
PKM2
 Forward AAGTCTGGCAGGTCTGCTCAC 241
 Reverse TCAGCACAATGACCACATCTCC
PTBP1
 Forward TCCTTCTCCAAGTCCACCATCT 128
 Reverse AAAATCTCTGGTCTGCTAAGGTCAC
FSCN1
 Forward CGTCCAATGGCAAGTTTGTG 241
 Reverse GTGGAGTCTTTGATGTTGTAGGC
STRAP
 Forward GCAAACTGTGGTAGGAAAAACG 203
 Reverse ACTAACTGCAACATATGATTGACGC
CISH
 Forward TATTGGGGTTCCATTACGGC 235
 Reverse GCACAAGGCTGACCACATCC
ESR2
 Forward ATCACATCTGTATGCGGAACCTC 158
 Reverse AGTGAGCATCCCTCTTTGAACCT
FLT3
 Forward TATGTGACTTTGGATTGGCTCG 175
 Reverse CCAAGTGAGAAGATTTCCCACAGTA
KIT
 Forward TGAAGTGGATGGCACCTGAAAG 82
 Reverse CAAAGAAAAATCCCATAGGACCAGAC
CNTN3
 Forward TGAGCAATGGACATTTACTGGG 219
 Reverse GAGGCGTTTTCTTGGTGGTT
BCL2
 Forward GCCTTCTTTGAGTTCGGTGGG 107
 Reverse TGCCGGTTCAGGTACTCAGTCATC
ACTIN
 Forward ACTTAGTTGCGTTACACCCTT 156
 Reverse GTCACCTTCACCGTTCCA

Summary of the differentially expressed miRNAs.

miRNAs P-value miRNAs P-value miRNAs P-value
Upregulated miRNAs
hsa-mir-1293 6.62E-04 hsa-mir-381 1.17E-02 hsa-mir-147b 3.00E-02
hsa-mir-105-2 1.87E-03 hsa-mir-365-1 1.50E-02 hsa-mir-1285-1 3.13E-02
hsa-mir-365-2 2.82E-03 hsa-mir-550a-1 1.52E-02 hsa-mir-105-1 3.25E-02
hsa-mir-193a 3.22E-03 hsa-mir-539 1.70E-02 hsa-mir-432 3.43E-02
hsa-mir-196b 3.64E-03 hsa-mir-433 1.76E-02 hsa-mir-9-3 3.68E-02
hsa-mir-1185-1 3.86E-03 hsa-mir-509-2 1.88E-02 hsa-mir-376c 3.92E-02
hsa-mir-944 8.07E-03 hsa-mir-3191 2.09E-02 hsa-mir-2116 4.35E-02
hsa-mir-767 8.36E-03 hsa-mir-382 2.15E-02 hsa-mir-379 4.40E-02
hsa-mir-193b 8.48E-03 hsa-mir-665 2.27E-02 hsa-mir-513a-1 4.53E-02
hsa-mir-3942 8.49E-03 hsa-mir-582 2.31E-02 hsa-mir-1276 4.76E-02
hsa-mir-9-1 8.65E-03 hsa-mir-889 2.40E-02 hsa-mir-212 4.89E-02
hsa-mir-9-2 9.43E-03 hsa-mir-421 2.48E-02 hsa-mir-516a-2 4.92E-02
hsa-mir-655 1.14E-02 hsa-mir-744 2.72E-02 hsa-mir-98 4.96E-02
hsa-mir-409 1.15E-02 hsa-mir-653 2.86E-02 hsa-let-7d 4.98E-02
hsa-mir-496 1.16E-02 hsa-mir-134 2.96E-02
Downregulated miRNAs
hsa-mir-133a-1 1.07E-02 hsa-mir-518a-1 2.77E-02 hsa-mir-338 3.21E-02
hsa-mir-3065 1.11E-02 hsa-mir-552 2.94E-02 hsa-mir-577 3.24E-02
hsa-mir-3186 1.42E-02 hsa-mir-30b 3.02E-02 hsa-mir-622 3.95E-02
hsa-mir-135a-2 2.08E-02 hsa-mir-660 3.03E-02 hsa-mir-3202-2 4.16E-02
hsa-mir-1250 2.35E-02 hsa-mir-302a 3.17E-02

miRNA-target gene pairs whose expression levels are inversely correlated.

miRNA Status (miRNAs) No. of miRNA target genes Target mRNAs
hsa-mir-133a-1 Downregulated 7 PTBP1, PKM2, NKX2-3, STRAP, FSCN1, TGFBI, PA2G4
hsa-mir-135a-2 Downregulated 2 SLC27A4, FOXM1
hsa-mir-302a Downregulated 67 MCM7, SLC27A4, ELOVL6, RPE, CKAP4, KPNA2, SEMA3C, POLQ, KCND2, SLC45A2, MCM10, ASF1B, PBK, ST8SIA2, USP42, E2F7, CCT7, ERLIN1, BLCAP, PDAP1, CD109, KDELC2, ESCO2, DDX10, DHCR7, DSG3, E2F2, TMEM184A, C11orf82, GGA2, PSD4, NUP62, PANX1, PGM2L1, RACGAP1, HOXA11, CYP27C1, ACADVL, KRT14, MCM4, MCM6, NEDD4, MRPS16, NIP7, CYCS, PON2, RNF216, CDCA8, CEP55, OGFOD1, SYT13, KIAA1609, GNPNAT1, BRCA1, UPK1B, C1orf135, SHCBP1, FBXL18, MFAP5, CDT1, C10orf58, BARX2, STC2, EXO1, NCAPD2, CDC25A, ECT2
hsa-mir-30b Downregulated 100 MRPS24, DPP7, NKX2-3, F2, TRIP13, AP2A1, MYO1E, GARS, PLK1, UTP15, ZNF384, KDELC2, DSG2, DSP, AURKB, ABCF2, POLD2, TXNRD1, NCL, MYBL2, CARHSP1, SFXN1, CHORDC1, NEDD4, PTBP1, RARS, GAPDH, PTGFRN, IQGAP3, GNPNAT1, LMNB2, POLA2, NT5C3, DLGAP4, GALNT2, CYP24A1, LASS3, MESDC2, DPY19L1, GRM5, INCENP, KIF11, PELO, ERRFI1, PON2, AVEN, HECW2, RRM2, YWHAG, SLC7A5, ARHGAP11A, LPGAT1, CHST1, PSME3, CIB2, CCT4, ERLIN1, C1QL1, PDAP1, CBX3, SLC5A11, CPD, TICAM1, ESCO2, DLX1, TTLL12, RNASEH1, C16orf73, ABCA12, GPR110, PGM2L1, HDLBP, HNRNPA2B1, HOXA11, KCND2, MID1, MTHFD1, NEFM, ORC2L, CYCS, ANLN, CDCA8, PGM2, DEPDC1B, KIF15, SH3GL1, CENPH, STC1, TFAP2A, UPK1B, ZIC2, C1orf135, CALB2, MLF1IP, CALU, FAM136A, PRC1, HN1L, KIAA0101, E2F7
hsa-mir-338 Downregulated 1 CCT4
hsa-mir-552 Downregulated 52 CYP24A1, FOXC1, CYCS, UBFD1, C10orf58, EIF2S2, ABCF2, CARM1, CENPF, YKT6, CDCA5, WBSCR22, CD109, CPD, AP2A2, ENO1, TMEM184A, EXTL3, TTLL12, NCAPH, FOSL2, GNG4, PGM2L1, HOXA10, BIRC5, CYP27C1, CHST6, MCM4, MKI67, MRPS16, PKM2, ERRFI1, PNPLA2, PTBP1, SYT13, NKX3-2, RFC2, BCAN, GNPNAT1, FSCN1, BRCA1, TGM4, TULP3, GLT25D1, MAFK, USP5, ST8SIA2, CDT1, LMNB2, CDK5R2, HN1L, DDX21
hsa-mir-1276 Upregulated 15 CNOT6L, CHIC2, PIK3CG, C1orf190, BDH2, BCL2, SLC14A1, HSDL2, PSTPIP2, CD5, PKHD1L1, ITM2A, LPIN2, MTSS1, EPM2AIP1
hsa-mir-1293 Upregulated 5 NMUR1, ITIH4, ROBO2, DNALI1, FCRLA
hsa-mir-134 Upregulated 74 KIT, KLHDC1, SEC14L3, IL16, PCMTD2, SLC14A1, FCRL5, RCL1, ATP8A1, RRH, CHKA, WIF1, ERMAP, FMNL2, CYP2R1, CNR1, CSF3R, PRICKLE2, ZNF25, LNX2, DENND3, MMRN1, P2RX2, PDZD2, GPD1L, ISCU, CBX7, CNOT6L, SLC41A1, GCET2, PDE7B, ZNF776, ZNF615, SLC25A42, S100A7A, FAM19A2, ID2, RSPO2, C3orf62, KCNJ8, CLEC12B, SMAD6, MOCS1, UNC13C, MTHFR, GIMAP6, CDON, CECR1, PLCB2, GNG2, RSBN1, TNFRSF19, PRKCQ, MOSPD1, SLC24A3, BCL2, SLAMF1, UBP1, C18orf1, ZFP161, C21orf2, ZNF20, DNALI1, BTG2, ZNF552, C1orf21, PIGY, MPDZ, LIMD1, ZNF468, IL33, RPS6KA5, TP53INP1, LPIN2
hsa-mir-147b Upregulated 3 MACROD2, LY9, NFIX
hsa-mir-193b Upregulated 42 BCL2, KIT, UPRT, CAMTA1, CBX7, MMP19, UBP1, RAMP3, UNC13B, MGAT4A, UNC45B, PDIK1L, PRICKLE2, TAPT1, FAIM2, CNOT6L, PPP1R16B, GATA6, CRB2, C3orf62, RHOH, MAP3K3, CD244, PLAG1, C21orf29, POU2AF1, SNRK, TRIM68, NXF3, WDR48, SLC4A5, C18orf1, EVI5, C1orf21, CAMKK1, KBTBD8, ATOH8, VAMP8, NMT2, TP53INP1, AATK, SPNS3
hsa-mir-196b Upregulated 53 FLT3, BCL2, GATA6, AQP4, ZMYND11, EPS15, WDR37, CAPN7, ATRNL1, PDE7B, SMAD6, MGAT4A, CYP2U1, UNC45B, PRICKLE2, KLHDC8B, GPD1L, SLC41A1, PPP1R16B, SERP1, HLF, SNAI3, RSPO2, C3orf62, MAOA, MTHFR, CDON, PLAG1, PLCB2, BCL11A, BEST2, TNFRSF19, MRPS25, COL14A1, WNT2B, TMEM50B, SLC25A20, EEA1, ZNF577, ATOH8, PRSS12, FAM125B, LIMD1, CREB3L1, ACVR2A, VAPA, MS4A1, TP53INP1, FHL5, AATK, LPIN2, WSCD2, LPPR4
hsa-mir-212 Upregulated 92 RPS6KA5, REM1, ANKRD29, PRICKLE2, SOSTDC1, PDE7B, SERP1, MAOA, SLC24A3, TMEM50B, BTG2, SLC25A20, USP38, KLRG1, CXCR6, WASF3, FGL2, MGAT4A, CYP2U1, PIK3IP1, FMNL2, CNR1, C1orf88, MAP3K8, CPT1B, UPRT, GAB3, GPR155, BTLA, ZNF483, TAPT1, ENPP4, KLRK1, CLASP2, GLT25D2, TBC1D9, GPD1L, CAMTA1, FOSB, CNOT6L, PPP1R16B, NAP1L5, GPR160, SESN1, ZNF615, FAM19A2, IKBKB, IL12B, IL16, IMPG1, B3GNT8, MAP3K3, MLLT3, UNC13C, AK3, CDON, EMCN, PLAG1, C21orf29, GFOD1, LAX1, PCMTD2, FBXW7, PNRC2, MOSPD1, LHX9, GALNTL1, WDR48, STIM2, PTPN4, SLC4A5, RSU1, SDF2, C18orf1, ZFP161, ZNF708, ZNF80, C1orf21, TCF7L1, SYT15, HSDL2, KBTBD8, PDE8B, LIMD1, LARGE, VAPA, NMT2, TP53INP1, CHST10, TOX, ELMO1, SEMA4A
hsa-mir-365-1 Upregulated 1 BCL2
hsa-mir-365-2 Upregulated 1 BCL2
hsa-mir-376c Upregulated 51 PDIK1L, SMYD1, ALCAM, GNG2, KLHL9, ARID4A, VLDLR, ACYP2, TESK2, FBLN5, RCBTB2, SLAMF6, KLHDC1, UNC45B, ZNF483, NLRC3, DAPK2, PLA2G4F, C1orf101, GCET2, D4S234E, SERP1, SNAI3, FAM19A2, FAM19A1, MAP3K3, GIMAP6, AK3, C3orf18, PLAG1, P2RY13, RCOR3, MOSPD1, ST6GAL1, ZNF649, C18orf1, ZFP161, ZNF10, ZNF708, ZNF614, NDFIP1, C1orf21, IL33, CABLES1, GPR55, MS4A1, WSCD2, TOX, MTSS1, FLT3, RORB
hsa-mir-379 Upregulated 52 ATP8A2, CYP2U1, KLHL9, C7, KLRG1, NMUR1, UNC13B, ZNF211, INMT, SCGB3A2, C1orf88, GAB3, ANKRD29, PRICKLE2, ALCAM, DENND3, FAIM2, SLC41A1, C1orf101, HSPB7, ZNF776, C13orf15, GZMK, HLF, S100A7A, ID4, SLCO4C1, IL16, KIT, ARRB1, UNC13C, GIMAP6, CDON, C9orf68, SLC25A36, SPTLC3, BEX4, WNT2B, C18orf1, ZNF10, DNALI1, C1orf21, ACSS1, IL18RAP, CD5, LARGE, VAPA, GPR55, MS4A1, CD40LG, WSCD2, LPPR4
hsa-mir-381 Upregulated 102 KIT, DLC1, WDR37, MMRN1, ID2, CNTN3, SLC25A36, SLC24A3, PCDH20, ACVR2A, MERTK, FBLN5, CXCR6, WASF3, MGAT4A, CYP2U1, FMNL2, KLHDC1, CNR1, CNTFR, C1orf88, CD200R1, MAP3K8, UPRT, PDIK1L, SMYD1, BTLA, ZNF483, PRICKLE2, SUSD3, F11, ENPP4, SACM1L, SORCS3, SWAP70, CLASP2, TBC1D9, GPD1L, ANKRD12, CAMTA1, CAPN7, CNOT6L, SLC41A1, GCET2, NAP1L5, PDE7B, SERP1, ZNF776, ZNF615, GPR34, GRIA1, C13orf15, ZC3H7A, GZMK, HTR2A, ID4, RSPO2, KCNT2, RBPJ, SLCO4C1, IKBKB, IL12B, KCNA4, RHOH, MLLT3, NINJ1, PLAG1, BCL11A, PMM1, P2RY13, SETD4, FAM105A, SNRK, LAX1, TRIM68, PCMTD2, TNFRSF19, KLHL9, BEX4, WDR48, SLC4A5, ARID4A, PRDM16, SLAMF1, BTG1, VLDLR, C18orf1, ZNF10, ZNF136, DNALI1, LRRC27, FAM117A, EEA1, HSDL2, KBTBD8, CD1D, VAPA, RCSD1, TP53INP1, KIAA0408, MTSS1, NFIB
hsa-mir-382 Upregulated 59 DLC1, ATP8A1, MAP3K8, CRHBP, CAPN7, GPR160, ZNF615, ID4, SLCO4C1, CABC1, ROBO2, CD96, MGAT4A, CISH, CNR1, PPM1M, MACROD2, GPR155, BTLA, PRICKLE2, TAPT1, EPS15, ALCAM, SORCS3, CAMTA1, CNOT6L, PPP1R16B, RSPO2, IL10RA, IL12B, ITGAD, B3GNT8, MAOA, LRP2BP, KLHL9, MOSPD1, SLC24A3, SLC4A5, PRDM16, PCDH20, C18orf1, ZNF708, TMEM50B, CA3, ZNF614, C1orf21, SYT15, CLDN2, IL33, CD1D, RCSD1, HMGN3, GRAP2, ITM2A, AKAP7, CHST10, TOX, NFIB, PPP3CC
hsa-mir-421 Upregulated 79 CBX7, TESK2, CLASP2, SOSTDC1, RHOH, SLC24A3, NDFIP1, KBTBD8, B3GALT2, VAPA, TSPAN32, CXCR6, WASF3, FRS3, FGL2, MGAT4A, ERMAP, CNR1, GAB3, PDIK1L, SMYD1, DBH, ZNF540, PRICKLE2, NLRC3, ZNF25, ENPP4, ANKRD12, CAMTA1, CNOT6L, SLC41A1, PDE7B, SERP1, SLC46A3, NKIRAS1, ZC3H7A, FAM19A2, RSPO2, SLCO4C1, IL10RA, IL12B, AQP4, C3orf62, NR3C2, MTHFR, GIMAP6, CDON, BCL11A, FBXO42, RSBN1, LAX1, SLC25A36, PCMTD2, LRP2BP, GALNTL1, WDR48, STIM2, PTPRB, RSU1, GZF1, MRPS25, C18orf1, BTG2, ZNF614, LRRC27, FCRL5, SYT15, ZNF577, LIMD1, CLDN2, ACVR2A, GPR55, NMT2, AKAP7, USP6NL, TOX, LPPR4, RORB, ACTN2
hsa-mir-432 Upregulated 79 CXCR6, SLCO4C1, GNG2, CA3, C1orf21, HSDL2, PPAP2A, RCL1, CD96, DLC1, WASF3, CHKA, MGAT4A, ADH1B, CNR1, MAP3K8, CPT1B, MACROD2, UNC45B, ANKRD29, GPR155, NLRC3, TAPT1, F11, TMEM130, KLRK1, SORCS3, GPD1L, CAMTA1, PPP1R13B, FOS, SLC41A1, MYRIP, SETBP1, NAP1L5, GPR160, HSPB7, GNG7, SNAI3, ID4, RSPO2, IL16, LY9, MAP3K3, MLLT3, GIMAP6, P2RX1, C3orf18, REV1, HMGCLL1, RSBN1, PPP1R1A, SLC25A36, TMEM57, FBXW7, SPTLC3, GIMAP5, LRP2BP, C1orf183, BCL2, PRDM16, GZF1, C5, UBP1, C18orf1, ZNF80, EVI5, DENND1C, ATOH8, FAM125B, BZRAP1, GPR55, MS4A1, ENTPD3, CD40LG, WSCD2, USP6NL, TOX, PDZD2
hsa-mir-433 Upregulated 55 NR0B2, ZNF211, RAB40B, TAGAP, NLRC3, ZNF615, NINJ1, RSBN1, PTPN4, PRDM16, MRPS25, ZNF649, ZNF91, BTG2, ACVR2A, RAMP3, ATP8A1, UNC13B, FGL2, FCRL3, CNR1, CD200R1, GAB3, PDIK1L, BTLA, DMP1, GPC5, SACM1L, SWAP70, ANKRD12, CAMTA1, CNOT6L, NAP1L5, ZNF776, IL16, UNC13C, C9orf68, TRIM68, FBXW7, TNFRSF19, LRP2BP, SLC4A5, PTPRB, BCL2, ZNF708, EVI5, LRRC27, C1orf21, KBTBD8, ZNF439, VAPA, NMT2, AKAP7, KIAA0408, LPPR4
hsa-mir-496 Upregulated 49 KIT, ANKRD12, FAM55D, ZNF20, ZNF136, MGAT4A, CNR1, ADHFE1, CR2, CRHBP, GPR155, ZNF781, P2RX2, TBC1D9, C20orf194, SETBP1, GATA6, CLUL1, HSPB7, ANGPT1, ZNF776, GPR34, S100A7A, SLCO4C1, C3orf62, CDON, EMCN, P2RY13, HMGCLL1, LAX1, SUSD2, BEX4, BDH2, BCL2, MRPS25, SPATA20, SLC14A1, ZNF708, ZNF614, C1orf21, EEA1, ZNF160, IL33, VAPA, BZRAP1, ITM2A, LPPR4, NFIB, PAQR8
hsa-mir-513a-1 Upregulated 1 DNAH8
hsa-mir-539 Upregulated 101 KIT, KLRG1, WASF3, PIK3IP1, MACROD2, BTLA, CLASP2, PDE7B, ZNF776, ID4, RSPO2, PNRC2, C7, NDFIP1, FCRL5, SYT15, PRSS12, ZNF266, RCBTB2, ZPBP2, CNR1, CNTFR, MAP3K8, UNC45B, SMYD1, GPR155, ZNF483, DLG4, DMP1, FAIM2, PDZD2, GLT25D2, ANKRD12, PPP1R13B, FOS, PIK3R5, FOSB, CCNDBP1, SLC41A1, GCET2, ATRNL1, SEC14L3, ANGPT1, S100A7A, SLCO4C1, C3orf62, CLEC12B, MAOA, NR3C2, MTHFR, GIMAP6, P2RX1, CNTN3, CDON, PHF7, CECR1, PIK3CG, BCL11A, PNOC, C21orf29, C1orf190, GNG2, PALMD, SLC25A36, SPTLC3, GIMAP5, TNFRSF19, MOSPD1, CPA6, TNFRSF17, RSU1, NAPB, USP4, C18orf1, DNALI1, EVI5, BTG2, DENND1C, ZNF614, LRRC27, C1orf21, CDADC1, EEA1, C15orf48, KBTBD8, EMR3, ST3GAL5, FAM125B, LIMD1, ZNF439, CLDN2, CD1D, C22orf32, CD5, VAPA, MS4A1, NMT2, TP53INP1, AKAP7, CD69, USP6NL
hsa-mir-582 Upregulated 1 SMAD6
hsa-mir-9-1 Upregulated 9 CD19, CEBPA, BCL2, RASSF1, CBX7, ATP8A2, CISH, GRIA1, IGFALS
hsa-mir-9-2 Upregulated 8 ATP8A2, CD19, RASSF1, BCL2, GRIA1, CEBPA, IGFALS, CISH
hsa-mir-9-3 Upregulated 7 ATP8A2, CD19, BCL2, IGFALS, GRIA1, CEBPA, CISH
hsa-mir-98 Upregulated 3 ESR2, CISH, CNTN3
hsa-let-7d Upregulated 61 ACVR2A, BCL2, FOSB, FLT3, PGC, KIT, CNTN3, ESR2, ATP8A2, CXCR4, CEBPA, FBXW7, CACNA2D2, CISH, MTSS1, RASSF1, MGAT4A, USP38, ADAMTS8, WDR37, RUFY3, RSPO2, COL14A1, WASF3, CYP2U1, SLAMF6, CNR1, CD200R1, GAB3, GPR155, DAPK1, ZNF540, NLRC3, KLHDC8B, EPS15, ENPP4, CLASP2, DAPK2, PPP1R16B, HSPB7, CRB2, HLF, SNAI3, TMEM110, P2RX1, PLCB2, GFOD1, BEST2, SFTPB, MRPS25, C18orf1, ZNF10, ZNF614, C1orf21, TCF7L1, SYT15, EEA1, ZNF577, MPDZ, FAM125B, TP53INP1

GO functional annotation of the predicted miRNA target genes.

GO ID GO term FDR
Biological process
 GO:0002320 Lymphoid progenitor cell differentiation 4.75E-04
 GO:0002244 Hematopoietic progenitor cell differentiation 2.08E-03
 GO:0070661 Leukocyte proliferation 1.44E-02
 GO:0002521 Leukocyte differentiation 2.32E-02
 GO:0046425 Regulation of JAK-STAT cascade 1.94E-02
 GO:0048639 Positive regulation of developmental growth 1.65E-02
 GO:0007166 Cell surface receptor signaling pathway 1.88E-02
 GO:0043552 Positive regulation of phosphatidylinositol 3-kinase activity 2.20E-02
 GO:0002318 Myeloid progenitor cell differentiation 1.96E-02
 GO:0048070 Regulation of developmental pigmentation 1.76E-02
 GO:0030318 Melanocyte differentiation 1.60E-02
 GO:0050931 Pigment cell differentiation 1.47E-02
 GO:0090218 Positive regulation of lipid kinase activity 1.36E-02
 GO:0060563 Neuroepithelial cell differentiation 1.26E-02
 GO:0048534 Hematopoietic or lymphoid organ development 1.23E-02
 GO:0001932 Regulation of protein phosphorylation 1.20E-02
 GO:0042113 B cell activation 1.53E-02
 GO:0031399 Regulation of protein modification process 1.67E-02
 GO:0002065 Columnar/cuboidal epithelial cell differentiation 1.73E-02
 GO:0042509 Regulation of tyrosine phosphorylation of STAT protein 1.64E-02
 GO:0042531 Positive regulation of tyrosine phosphorylation of STAT protein 1.57E-02
 GO:1903727 Positive regulation of phospholipid metabolic process 1.50E-02
 GO:0046427 Positive regulation of JAK-STAT cascade 1.43E-02
 GO:0071310 Cellular response to organic substance 1.38E-02
 GO:0050853 B cell receptor signaling pathway 1.41E-02
 GO:0050769 Positive regulation of neurogenesis 1.64E-02
 GO:0051094 Positive regulation of developmental process 1.67E-02
Molecular function
 GO:0038023 Signaling receptor activity 9.24E-04
 GO:0004871 Signal transducer activity 8.77E-04
 GO:0004888 Transmembrane signaling receptor activity 8.24E-04
 GO:0004872 Receptor activity 1.40E-03
 GO:0060089 Molecular transducer activity 1.57E-03
 GO:0005057 Receptor signaling protein activity 8.40E-03
 GO:0002020 Protease binding 1.02E-02
 GO:0004714 Transmembrane receptor protein tyrosine kinase activity 8.93E-03
 GO:0042803 Protein homodimerization activity 1.36E-02
 GO:0019199 Transmembrane receptor protein kinase activity 3.21E-02
 GO:0042802 Identical protein binding 3.54E-02
 GO:0046983 Protein dimerization activity 3.64E-02
 GO:0044389 Ubiquitin-like protein ligase binding 4.93E-02
 GO:0031625 Ubiquitin protein ligase binding 4.58E-02
 GO:0004713 Protein tyrosine kinase activity 4.60E-02

KEGG pathway enrichment analysis of the predicted miRNA target genes (top 15).

ID Items Count FDR Genes
hsa03030 DNA replication 7 1.78E-04 MCM4, RNASEH1, MCM7, POLD2, RFC2, MCM6, POLA2
hsa04660 T cell receptor signaling pathway 9 1.12E-03 IKBKB, FOS, PIK3R5, PPP3CC, PRKCQ, CD40LG, GRAP2, PIK3CG, MAP3K8
hsa04144 Endocytosis 12 1.17E-03 CXCR4, KIT, AP2A2, NEDD4, SH3GL1, PSD4, SMAD6, ARRB1, EEA1, EPS15, FAM125B, AP2A1
hsa04380 Osteoclast differentiation 5 1.19E-03 IKBKB, FOS, PIK3R5, PPP3CC, PIK3CG
hsa05145 Toxoplasmosis 5 1.23E-03 IKBKB, CYCS, PIK3R5, BCL2, PIK3CG
hsa04210 Apoptosis 5 1.23E-03 IKBKB, CYCS, PIK3R5, BCL2, PIK3CG
hsa04110 Cell cycle 3 1.24E-03 MCM4, MCM7, MCM6
hsa05222 Small cell lung cancer 5 1.26E-03 IKBKB, PIK3R5, E2F2, BCL2, PIK3CG
hsa05210 Colorectal cancer 7 1.27E-03 CYCS, TCF7L1, FOS, PIK3R5, BIRC5, BCL2, PIK3CG
hsa04620 Toll-like receptor signaling pathway 5 1.27E-03 IKBKB, FOS, PIK3R5, PIK3CG, MAP3K8
hsa05142 Chagas disease (American trypanosomiasis) 6 1.28E-03 IKBKB, FOS, TICAM1, PIK3R5, IL12B, PIK3CG
hsa04722 Neurotrophin signaling pathway 4 1.29E-03 IKBKB, PIK3R5, BCL2, PIK3CG
hsa05200 Pathways in cancer 17 1.31E-03 IKBKB, CYCS, TCF7L1, FOS, PIK3R5, KIT, BIRC5, WNT2B, E2F2,CSF3R, FLT3, RASSF1, BCL2, DAPK1, CEBPA, PIK3CG, DAPK2
hsa05221 Acute myeloid leukemia 7 1.39E-03 IKBKB, TCF7L1, PIK3R5, KIT, FLT3, CEBPA, PIK3CG
hsa04640 Hematopoietic cell lineage 8 1.43E-03 KIT, CSF3R, CD1D, FLT3, CR2, CD19, CD5, MS4A1