Abstract The expression of microRNA (miR)‐140‐5p is known to be reduced in both pulmonary arterial hypertension (PAH) patients and monocrotaline‐induced PAH models in rat. Identification of target genes for miR‐140‐5p with bioinformatics analysis may reveal new pathways and connections in PAH. This study aimed to explore downstream target genes and relevant signaling pathways regulated by miR‐140‐5p to provide theoretical evidences for further researches on role of miR‐140‐5p in PAH. Multiple downstream target genes and upstream transcription factors (TFs) of miR‐140‐5p were predicted in the analysis. Gene ontology (GO) enrichment analysis indicated that downstream target genes of miR‐140‐5p were enriched in many biological processes, such as biological regulation, signal transduction, response to chemical stimulus, stem cell proliferation, cell surface receptor signaling pathways. Kyoto Encyclopedia of Genes and Genome (KEGG) pathway analysis found that downstream target genes were mainly located in Notch, TGF‐beta, PI3K/Akt, and Hippo signaling pathway. According to TF–miRNA–mRNA network, the important downstream target genes of miR‐140‐5p were PPI, TGF‐betaR1, smad4, JAG1, ADAM10, FGF9, PDGFRA, VEGFA, LAMC1, TLR4, and CREB. After thoroughly reviewing published literature, we found that 23 target genes and seven signaling pathways were truly inhibited by miR‐140‐5p in various tissues or cells; most of these verified targets were in accordance with our present prediction. Other predicted targets still need further verification in vivo and in vitro. Keywords: GO, KEGG, miR‐140‐5p, target gene, transcription factor __________________________________________________________________ Abbreviations GO gene ontology KEGG kyoto encyclopedia of genes and genome PAH pulmonary arterial hypertension PASMC pulmonary arterial smooth muscle cell TF transcription factor Pulmonary arterial hypertension (PAH) is a chronic progressive disease of pulmonary vasculature characterized by sustained elevation of pulmonary vascular resistance and pulmonary arterial pressure, consequently leading to right heart failure and eventual death [42]1. The pathogenesis of PAH is associated with genetic predisposition, inflammation, increase in vascular tone, elevation in pulmonary artery cell proliferation and resistance to apoptosis, and the presence of in situ thrombosis [43]2, [44]3, [45]4, [46]5. Effect of current treatment on PAH remains poor and available therapies to improve long‐term prognosis are limited [47]6, so exploring novel molecular mechanisms and generating therapeutic approaches are urgently needed.MicroRNAs (miRNAs) are small noncoding RNA molecules around 22 nucleotides long that bind the 3′‐untranslated region (UTR) of mRNA to degrade mRNA and therefore to negatively regulate relevant genes expression [48]7. miRNAs have the ability to target numerous genes mRNA, therefore potentially controlling a host of genes expression and the activity of multiple signaling pathways [49]8, [50]9, [51]10. Recent studies have shown that reduction in microRNA (miR)‐140‐5p is found in both patients with PAH and monocrotaline‐induced PAH models in rat, which is involved in the development of PAH [52]11, [53]12. Therefore, it is important to identify comprehensive downstream targets of miR‐140‐5p with bioinformatics analysis in PAH, and this might provide some critical information for the development and treatment of PAH. In this study, downstream target genes regulated by miR‐140‐5p and upstream transcription factors (TFs) regulating miR‐140‐5p expression were predicted, and the downstream target genes were analyzed for gene ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genome (KEGG) pathway. Next, the upstream TFs and downstream targets of miR‐140‐5p were determined according to the TF–miRNA–mRNA network. Finally, the direct downstream targets and relevant signaling pathways regulated by miR‐140‐5p were obtained in published literature and were compared with the predicted results of this study. Materials and methods Mature sequences of miR‐140‐5p in various species Mature sequences of miR‐140‐5p in various species were obtained in the miRBase database ([54]http://mirbase.org/index.shtml). Target gene prediction of miR‐140‐5p Identification of target genes is critical for characterizing the functions of miRNAs. In this study, miRanda ([55]http://www.microrna.org/), TargetScan ([56]http://www.targetscan.org/), RNAhybrid ([57]https://bibiserv.cebitec.uni-bielefeld.de/rnahybrid/submission.htm l), and miRDB ([58]http://www.mirdb.org/) databases were used to predict the target genes of miR‐140‐5p. To make our predicted target genes more convincible, only the target genes predicted by at least three databases were selected for further analyses. Database‐based GO and KEGG pathway enrichment analysis Target mRNA of miR‐140‐5p supported by at least three databases were used for GO analysis to predict gene functions. Integration Discovery (DAVID) software, version 6.7 ([59]http://david.abcC.ncifcrf.gov), was used to perform GO analysis to identify biological processes, cellular components, and molecular functions of these target genes. At the same time, the probable signaling pathways in which these target genes were enriched were analyzed by KEGG database ([60]http://www.genome.jp/kegg/). The P‐value <0.05 was considered significant. Upstream TFs prediction of miR‐140‐5p Human miR‐140‐5p precursor was obtained in the miRBase database and its 5000 bp upstream was defined as the miR‐140‐5p promoter. The TFs of miR‐140‐5p were predicted using MOODS‐python software (version 1.9.3) in JASPAR database ([61]http://jaspar.binf.ku.dk/), which includes various vertebrate TFs. The P‐value <0.0001 was considered significant. Construction of the network for TF–miR‐140‐5p–mRNA By merging the regulatory relationships between TFs and miR‐140‐5p, miR‐140‐5p and target genes, genes and genes (TF→miRNA, miRNA→gene and gene→gene), we constructed a comprehensive TF–miR‐140‐5p–mRNA regulatory network using Gephi software (release 0.8.1‐β, [62]http://gephi.github.io/). Screening target genes and signaling pathways inhibited by miR‐140‐5p in published studies To obtain downstream target genes and signaling pathways modulated by miR‐140‐5p in published studies, a comprehensive electronic search of Web of Science and PubMed databases was performed until April 20, 2017. The keyword ‘miR‐140‐5p’ in the titles or abstracts was used, and then, studies exploring the targets of miR‐140‐5p were collected. Results Mature sequences of miR‐140‐5p in various species Mature sequences of miR‐140‐5p in various species were obtained in the miRBase database. The pre‐miR‐140‐5p was located at position 69933081 ~ 69933180 of chromosome 16, and the gene ID of human miR‐140‐5p was MIMAT0000431. As shown in Table [63]1, mature sequences of miR‐140‐5p were highly conserved in various species and human miR‐140‐5p was chosen for further analyses. Table 1. Mature sequences of miR‐140‐5p in various species ID Mature name Sequence MIMAT0000151 mmu‐miR‐140‐5p CAGUGGUUUUACCCUAUGGUAG MIMAT0000431 hsa‐miR‐140‐5p CAGUGGUUUUACCCUAUGGUAG MIMAT0000573 rno‐miR‐140‐5p CAGUGGUUUUACCCUAUGGUAG MIMAT0001159 gga‐miR‐140‐5p AGUGGUUUUACCCUAUGGUAG MIMAT0001836 dre‐miR‐140‐5p CAGUGGUUUUACCCUAUGGUAG MIMAT0002143 ssc‐miR‐140‐5p AGUGGUUUUACCCUAUGGUAG MIMAT0006812 oan‐miR‐140‐5p CAGUGGUUUUACCCUAUGGU MIMAT0006197 mml‐miR‐140‐5p CAGUGGUUUUACCCUAUGGUAG MIMAT0012745 mdo‐miR‐140‐5p CAGUGGUUUUACCCUAUGGUAG MIMAT0012926 eca‐miR‐140‐5p CAGUGGUUUUACCCUAUGGUAG MIMAT0014557 tgu‐miR‐140‐5p CAGUGGUUUUACCCUAUGGUAG MIMAT0015763 ppy‐miR‐140‐5p CAGUGGUUUUACCCUAUGGUAG MIMAT0021765 aca‐miR‐140‐5p CAGUGGUUUUACCCUAUGGU MIMAT0022552 ola‐miR‐140‐5p CAGUGGUUUUACCCUAUGGUAG MIMAT0023767 cgr‐miR‐140‐5p CAGUGGUUUUACCCUAUGGUAG MIMAT0025434 pol‐miR‐140‐5p CAGUGGUUUUACCCUAUGGUAG MIMAT0026220 ccr‐miR‐140‐5p CAGUGGUUUUACCCUAUGGUAG MIMAT0032359 ssa‐miR‐140‐5p CAGUGGUUUUACCCUAUGGUAG MIMAT0035960 chi‐miR‐140‐5p CAGUGGUUUUACCCUAUGGUAG MIMAT0036560 tch‐miR‐140‐5p CAGUGGUUUUACCCUAUGGUA MIMAT0036719 oha‐miR‐140‐5p CAGUGGUUUUACCCUAUGGUAG [64]Open in a new tab Prediction of target genes for miR‐140‐5p As shown in Fig. [65]1, the number of predicted target genes of miR‐140‐5p in miRanda, TargetScan, RNAhybrid, and miRDB databases was 2370, 428, 1017, and 262, respectively. There were 482 target genes supported by at least two databases, 123 target genes predicted by at least three databases and five target genes supported by all four databases. The target genes of miR‐140‐5p predicted by at least three databases are listed in Table [66]2 and were used for further analyses. Figure 1. Figure 1 [67]Open in a new tab The number of predicted target genes of miR‐140‐5p. Table 2. The target genes of miR‐140‐5p predicted by at least three databases ABCA1 ACSL6 ADAM10 ADAMTS5 ADCY6 ANKFY1 ANKIB1 AP2B1 BACH1 BAZ2B BCL9 BMP2 C1R CADM3 CAND1 CAPN1 CCNYL1 CELF1 CORO2A CREB CTCF CYTH2 DNM3 DOK4 DPP10 DPYSL2 EGR2 EIF4G2 ELAVL2 ENTPD5 EPB41L2 ERC2 FAM175B FBN1 FCHO2 FES FGF9 FLRT2 FOXP2 FYCO1 GNG5 GIT1 HAND2 HDAC4 HDAC7 HDGFRP3 HNRNPH3 HS2ST1 HSPA13 IGSF3 IPO7 JAG1 KAT2B KBTBD2 KIF1B KLF6 KLF9 KLK10 LAMC1 LHFPL2 LMNB1 LPHN2 LRAT LRP4 LSM14B LYSMD3 MARK1 MED13 MMD MYCBP2 MYO10 NAA20 NAALADL2 NCKAP1 NCOA1 NCSTN NFE2L2 NLK NPL NUCKS1 OSBPL6 PPPICC PAFAH1B2 PDGFRA PPTC7 PDE7A PPP1R12A PALM2‐AKAP2 RBM39 RFX7 RNF19A RALA RAB10 SEPT2 STRADB SYS1 SLAIN1 SAMD4 SMOC2 SNX2 SRCAP SHROOM3 SIAH1 SLC30A5 SLC38A2 TTYH3 ST5 TLR4 TTK TJP1 TSSK2 TSPAN12 TSC22D2 TTYH2 TGFBR1 UBR5 UBR5 VEZF1 VEGFA WNT1 WDFY3 YOD1 ZBTB10 ZNF800 [68]Open in a new tab GO enrichment analysis for predicted target genes of miR‐140‐5p GO enrichment analysis was conducted for the target genes of miR‐140‐5p predicted by at least three databases. As shown in Table [69]3, the target genes of miR‐140‐5p were mainly located in basement membrane (P < 0.05) and participated in the molecular functions of protein binding, activating transcription factor binding, ion binding, lipid binding, and so on (P < 0.05). In addition, the target genes of miR‐140‐5p were involved in various biological processes, including biological regulation, metabolic process, cell communication, signal transduction, response to chemical stimulus, stem cell proliferation, cell surface receptor signaling pathway (P < 0.05). Fig. [70]2 presents the number of target genes corresponding to each GO term. Table 3. Gene ontology (GO) analysis for predicted target genes of miR‐140‐5p ID Term P‐value Genes annotated to the term Biological processes GO:0050794 Regulation of cellular process 5.39E‐06 VEGFA| FGF9| PPP1CC|Pin1|HDAC7|PDGFRA|TGFBR1|ADAM10… GO:0050789 Regulation of biological process 9.05E‐06 FGF9|BMP2|LAMC1|NUMBL|PDGFRA|PPP1CC||ADAM10|TLR4|TGFBR1… GO:0007154 Cell communication 5.69E‐05 WNT1|PPP1CC|PDGFRA|TLR4|HDAC7|ADAM10| BMP2|TGFBR1… GO:0023052 Signaling 6.14E‐05 PDGFRA|PPP1CC|FGF9|WNT1|TGFBR1|BMP2|ADAM10|JAG1|TLR4… GO:0044763 Single‐organism cellular process 8.73E‐05 VEGFA|FGF9|LAMC1|BMP2|TLR4|WNT1|TGFBR1|PDGFRA|PPP1CC… GO:0065007 Biological regulation 9.89E‐05 VEGFA|BMP2|TLR4|CREB|PPP1CC|PDGFRA|ADAM10|TGFBR1… GO:0007165 Signal transduction 0.00011 PPP1CC|PDGFRA|WNT1|TGFBR1|FGF9|VEGFA|NCSTN|TLR4|ADAM10… GO:0042221 Response to chemical stimulus 0.00048 NUMBL|PPP1CC|PDGFRA|VEGFA|LAMC1|TGFBR1|FGF9|BMP2|ADAM10|TLR4… GO:0072089 Stem cell proliferation 0.00087 ACSL6|NUMBL|RAB10|HAND2|WNT1|BMP2… GO:0007166 Cell surface receptor signaling pathway 0.00370 TLR4|WNT1|BMP2|ADAM10|NCSTN|JAG1|PPP1CC|PDGFRA|FGF9… GO:0050896 Response to stimulus 0.01555 PPP1CC|PDGFRA|WNT1|CREB|TGFBR1|VEGFA||FGF9|BMP2|ADAM10|TLR4… GO:0019538 Protein metabolic process 0.02054 CREB|PPP1CC|PDGFRA|NUMBL|TLR4|ADAM10|BMP2|KAT2B|NCSTN| TGFBR1… GO:0006464 Cellular protein modification process 0.03073 HDAC4|CREB|ADAM10|TLR4|TGFBR1|PPP1CC|PDGFRA… Molecular functions GO:0005515 Protein binding 2.53E‐07 TLR4|ADAM10|PDGFRA|WNT1|HDAC7|VEGFA|CREB|PPP1CC|TGFBR1|FGF9… GO:0005488 Binding 0.00048 HDAC7|JAG|LMNB1|PDGFRA|ADAM10| TLR4|FGF9|KAT2B|TGFBR1… GO:0033613 Activating transcription factor binding 0.00320 EGR2|NFE2L2|HDAC4|HDAC7|HAND2… GO:0043167 Ion binding 0.00724 VEGFA|PPP1CC|ADAM10|PDGFRA|TGFBR1|HDAC4|FGF9|HDAC7… GO:0008289 Lipid binding 0.04471 LAMC1|OSBPL6|FES|DNM3|MYO10|TLR4… Cellular components GO:0005604 Basement membrane 0.04119 FGF9|PDGFRA|TLR4|VEGFA|SMOC2… [71]Open in a new tab Figure 2. Figure 2 [72]Open in a new tab Gene ontology (GO) enrichment analysis for predicted target genes of miR‐140‐5p. KEGG pathway analysis for predicted target genes of miR‐140‐5p Enriched signaling pathways for the target genes of miR‐140‐5p identified by KEGG pathway analysis were ranked according to the P‐values. As shown in Table [73]4, the top rankings were related to Notch, cancer‐associated pathway, TGF‐beta, PI3K/Akt, HTLV infection, Hippo, HIF‐1, alcoholism signaling pathways, and so on (P < 0.05); among them, Notch, TGF‐beta, PI3K/Akt, and Hippo signaling pathways were well known to be associated with the pathogenesis of PAH. Fig. [74]3 presents the rich factor, Q value, and gene number corresponding to each pathway term. Table 4. Kyoto Encyclopedia of Genes and Genome (KEGG) pathway analysis for predicted target genes of miR‐140‐5p Term ID Sample number Background number P‐value Genes Notch signaling pathway hsa04330 4 52 0.006408 JAG1|ADAM10|KAT2B|NCSTN Pathways in cancer hsa05200 9 337 0.016384 FGF9|TGFBR1|VEGFA|SLC2A1|WNT1|BMP2|PDGFRA|LAMC1 Endocrine and other factor‐regulated calcium reabsorption hsa04961 3 48 0.022347 AP2B1|ADCY6|DNM3 HTLV‐I infection hsa05166 7 268 0.031935 TGFBR1|KAT2B|SLC2A1|EGR2|WNT1|PDGFRA|ADCY6 Regulation of actin cytoskeleton hsa04810 6 221 0.031935 PPP1R12A|NCKAP1|FGF9|GIT1|PDGFRA|PPP1CC Pancreatic cancer hsa05212 3 66 0.031935 RALA|TGFBR1|VEGFA Epithelial cell signaling in Helicobacter pylori infection hsa05120 3 66 0.031935 TJP1|GIT1|ADAM10 Proteoglycans in cancer hsa05205 6 231 0.033735 PPP1R12A|FGF9|VEGFA|WNT1|TLR4|PPP1CC Adherence junction hsa04520 3 74 0.037848 NLK|TJP1|TGFBR1 Alcoholism hsa05034 5 183 0.038681 HDAC7|HDAC4|CREB3L1|GNG5|PPP1CC PI3K‐Akt signaling pathway hsa04151 7 358 0.045545 FGF9|VEGFA|PDGFRA|LAMC1|TLR4|CREB|GNG5 Focal adhesion hsa04510 5 214 0.045545 PPP1R12A|VEGFA|PDGFRA|LAMC1|PPP1CC Endocytosis hsa04144 5 212 0.045545 AP2B1|TGFBR1|GIT1|PDGFRA|DNM3 Viral carcinogenesis hsa05203 5 213 0.045545 HDAC7|HDAC4|KAT2B|EGR2|CREB3L1 Hepatitis B hsa05161 4 151 0.045545 TGFBR1|EGR2|TLR4|CREB3L1 Insulin secretion hsa04911 3 92 0.045545 SLC2A1|CREB3L1|ADCY6 GABAergic synapse hsa04727 3 89 0.045545 SLC38A2|GNG5|ADCY6 TGF‐beta signaling pathway hsa04350 3 83 0.045545 TGFBR1|SMAD4|BMP2 Gap junction hsa04540 3 96 0.045545 TJP1|PDGFRA|ADCY6 Hippo signaling pathway hsa04390 4 156 0.045565 TGFBR1|WNT1|BMP2|PPP1CC [75]Open in a new tab Figure 3. Figure 3 [76]Open in a new tab Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis for predicted target genes of miR‐140‐5p. Prediction of upstream TFs for miR‐140‐5p and construction of TF–miR‐140‐5p–mRNA network The number of predicted TFs for miR‐140‐5p with P‐value <0.0001 was 393. To reduce false‐positive results, TFs with a quality score (Q‐score) less than 10 were filtered. As shown in Table [77]5, the remaining TFs, including PAX5, FOXI1, IRF1, FOSL1, RUNX2, were chosen for further analyses. Finally, by merging the regulatory relationships between TFs and miR‐140‐5p, miR‐140‐5p and target genes, as well as genes and genes, we built a comprehensive TF–miR‐140‐5p–mRNA regulatory network, as shown in Fig. [78]4. Table 5. Prediction of transcription factors and binding sites of miR‐140‐5p Model ID Model name Hit position Strand Score Predicted site sequence MA0014.2 PAX5 95 − 10.5663 gtctcactctgttgcccat MA0014.2 PAX5 3874 − 11.6915 gtcttgctctgttgcccag MA0025.1 NFIL3 722 − 10.0393 TTCTTACATAA MA0035.3 Gata1 3391 − 10.0718 acagataaaaa MA0036.2 GATA2 3391 − 10.4087 acagataaaaattt MA0041.1 Foxd3 4529 + 10.4011 ttttgtttgttt MA0042.1 FOXI1 984 + 11.5926 GGATGTTTGTTT MA0042.1 FOXI1 4529 + 10.3990 ttttgtttgttt MA0046.1 HNF1A 4949 + 10.3282 agttaataatttta MA0050.2 IRF1 3825 + 11.0065 tttttctttttcttttctttc MA0050.2 IRF1 3840 + 12.4803 tctttctttcttttttttttt MA0050.2 IRF1 3844 + 10.0776 tctttcttttttttttttttt MA0062.2 GABPA 1506 + 10.0387 ccggaagtcga MA0073.1 RREB1 1164 − 10.9028 TTTTGGTTGTTGTTTTGTTT MA0073.1 RREB1 3734 + 10.2056 caacaaaacaaaacaaaaca MA0471.1 E2F6 143 − 10.6410 tcttcccgcct MA0477.1 FOSL1 4238 − 11.2229 cctgagtcacc MA0478.1 FOSL2 4239 − 10.3145 ctgagtcacct MA0481.1 FOXP1 3756 + 10.2195 acaaaaaaaacacaa MA0481.1 FOXP1 4018 − 10.3465 ttttgtttttttagt MA0490.1 JUNB 4239 − 10.6046 ctgagtcacct MA0491.1 JUND 2362 + 10.0256 GAAAATGATATCACA MA0493.1 Klf1 4812 + 10.548 caccacaccca MA0511.1 RUNX2 3813 + 11.453 tgtgtatgtggtttt MA0515.1 Sox6 3772 − 10.2529 gaaacaatgg MA0595.1 SREBF1 2000 − 10.1772 gtggcgtgat [79]Open in a new tab Figure 4. Figure 4 [80]Open in a new tab Regulatory network of TF–miR‐140‐5p–mRNA. Screening target genes and signaling pathways modulated by miR‐140‐5p in published studies A comprehensive electronic search of Web of Science and PubMed databases was performed until April 20, 2017, to obtain target genes and signaling pathways modulated by miR‐140‐5p in published studies. Finally, a total of 26 papers including 23 target genes and seven signaling pathways inhibited by miR‐140‐5p were obtained; most of them focus on the functions of miR‐140‐5p suppressing tumor growth, migration, and invasion in various tumor tissues and cells. Two recent studies have found that SMURF1 and Dumt1 are direct target genes of miR‐140‐5p in pulmonary arterial smooth muscle cells (PASMCs) and are involved in the pathogenesis of PAH. The details are shown in Table [81]6. Table 6. Target genes and signaling pathways modulated by miR‐140‐5p in published studies. NA, not available; HCC, hepatocellular carcinoma; T‐ALL, T‐cell acute lymphoblastic leukemia; Th1, T helper type 1; HSCC, hypopharyngeal squamous cell carcinoma; EPCs, endothelial progenitor cells; PH, pulmonary hypertension; HUVECs, human umbilical vein endothelial cells; BTC, biliary tract cancer; TSPCs, tendon stem/progenitor cells; LLC, Lewis lung cancer cells; MSCs, mesenchymal stem cells; TSCC, tongue squamous cell carcinoma Author (Year) Target genes Inhibited pathways Associated functions Cell or tissue types Hu (2017) VEGFA NA Inhibit cell proliferation and invasion, promote apoptosis Glioma tissues and cells Meng (2017) HMGN5 NA Decrease cell resistance to chemotherapy Osteosarcoma tissues and cells Yan (2017) Pin1 Pin1‐dependent cancer pathway Suppress tumor growth HCC tissues and cells Correia (2016) TAL1 NA Suppress tumor growth T‐ALL cells Guan (2016) STAT1 NA Suppress Th1 cell differentiation Th1 cells Jing (2016) ADAM10 Notch1 signaling pathway Suppress tumor migration and invasion HSCC tissues and cells Liu (2016) HDAC7 NA Protect EPCs EPCs Lv (2016) Slug NA Inhibit cell migration and invasion HCC tissues Rothman (2016) SMURF1 BMP signaling pathway Inhibit cell proliferation, migration, and PH development PASMCs, rat PH models Su (2016) IGF2BP1 NA Decrease cell proliferation, migration, and invasion Cervical cancer cells and tissues SUN (2016) VEGFA NA Decrease cell proliferation, migration, and tube formation HUVECs Wei (2016) IP3k2 IP3 signaling pathway Promote chemotherapy‐induced autophagy Human osteosarcoma cells Yu (2016) Septin 2 NA Suppress cell proliferation and colony formation BTC tissues and cells Zhang (2016) Dnmt1 NA Inhibit cell proliferation, promote cell apoptosis Human PH tissues, human PASMCs Barter (2015) FZD6 Wnt signaling pathway Promote chondrogenic differentiation Mesenchymal stem cells Chen (2015) Pin1 NA Promote cell senescence TSPCs Lan (2015) PDGFRA NA Inhibit cancer growth Human ovarian cancer tissues and cells Zhai (2015) Smad2 TGF‐β signaling pathway Decrease cell invasion and proliferation Colorectal cancer stem cells Zhang (2015) VEGFA NA Inhibit tumor progression Colorectal cancer tissues and cells Zhang (2015) TGFBR1 TGF‐β signaling pathway Regulate adipocyte differentiation Bone marrow stromal cells Li (2014) MMD ERK signaling pathway Inhibit cell proliferation LLCs Hwang (2014) BMP2 BMP signaling pathway Suppress osteogenesis Human MSCs Karlsen (2014) RALA NA Stimulate chondrogenesis MSCs Yang (2014) ADAM10, LAMC1, HDAC7 NA Suppress migration and invasion TSCC tissues and cells Shi (2013) FoxP2 NA Impair dendritic development and vocal learning Zebra finch brain tissues Yang (2013) TGFBR1, FGF9 TGF‐β and ERK signaling pathway Suppress cell proliferation and tumor metastasis HCC tissues and cells [82]Open in a new tab Discussion Pulmonary arterial hypertension is a chronic life‐threatening condition requiring long‐term management [83]13, and its available therapies are limited [84]6. There is a clear and urgent need for new therapeutic options based on deeply exploring the pathogenesis of PAH. Previous studies have indicated that miR‐140‐5p is dramatically downregulated, which in turn causes the development of a variety of cancers by the loss of suppressing tumor cell migration and growth [85]14, [86]15, [87]16, [88]17. miR‐140‐5p has been recently found to be reduced in both PAH patients and MCT‐induced PAH models in rat [89]11, [90]12. However, the downstream targets regulated by miR‐140‐5p contributing to the development of PAH remain largely unknown. In this study, we found that the target genes of miR‐140‐5p were enriched in many biological processes, such as biological regulation, metabolic process, cell communication, signal transduction, response to chemical stimulus, stem cell proliferation, cell surface receptor signaling pathway. In KEGG pathway analysis, the target genes of miR‐140‐5p were mainly located in Notch, TGF‐beta, PI3K/Akt, and Hippo signaling pathways. According to the TF–miRNA–mRNA network, the important genes potentially regulated by miR‐140‐5p included PPI, TGF‐betaR1, smad4, JAG1, ADAM10, FGF9, PDGFRA, VEGFA, TLR4, LAMC1, CREB, and the upstream TFs, which might regulate miR‐140‐5p expression including TAX5, FOXI, IRF1, GATA6, RUNX2. After thoroughly reviewing published literature, we found that 23 target genes and seven signaling pathways were truly inhibited by miR‐140‐5p in various tissues or cells; most of these downstream targets were in accordance with our present prediction. Several studies have shown that activation of Notch3 pathway is involved in the pathogenesis of PAH [91]18, [92]19. We have previously shown that activation of Notch3 promotes PASMC proliferation and inhibition of Notch3 pathway prevents monocrotaline‐induced development of PAH in rat [93]20, [94]21. JAG1 and ADAM10 are indispensable components of Notch signaling pathway, which were predicted as downstream targets of miR‐140‐5p in our analysis, suggesting that lack of miR‐140‐5p might promote the development of PAH by upregulation of JAG1 and ADAM10 genes and therefore activation of Notch3 cascade. In addition, activation of TGF‐beta1/Smad4 signaling promotes a proliferative PASMC phenotype and induces PAH in rat [95]22, [96]23. We found that TGF‐betaR1 and smad4 were possible downstream targets of miR‐140‐5p, reduction in miR‐140‐5p in PAH might stimulate TGF‐beta1/Smad4 pathway by upregulating TGF‐betaR1 and smad4. Previous studies have demonstrated that PDGF, TLR4, VEGFA, and FGF contribute to the pathogenesis of PAH via activating various signaling pathways, especially PI3K/Akt cascade [97]24, [98]25, [99]26, [100]27, [101]28. CREB, an important transcription factor lying downstream of PI3K/Akt pathway, mediates the partial functions of PI3K/Akt [102]29. In our analysis, PDGF, TLR4, VEGFA, FGF, and CREB were positively predicted as downstream targets of miR‐140‐5p, implying that miR‐140‐5p negatively regulates the functions of PI3K/Akt cascade by targeting FGF9, PDGFRA, VEGFA, TLR4, or CREB gene. Recent studies have also shown that Hippo signaling is associated with the development of PAH, which can be activated by PPI [103]30, [104]31. Our present results suggested that PPI was a direct target gene of miR‐140‐5p and might mediate miR‐140‐5p regulation of Hippo signaling. Our predicted network provided potential target genes and relevant signaling pathways that might be modulated by miR‐140‐5p contribution to the development of PAH. Several targets and pathways predicted in our analysis, such as TGF‐betaR1, ADAM10, FGF9, PDGFRA, VEGFA and Notch, PI3K/Akt, TGF‐beta cascades, have been demonstrated to mediate the effects of miR‐140‐5p on antiproliferation and prodifferentiation in several cell types in published studies [105]16, [106]17, [107]32, [108]33. While the other targets predicted in our study, including PPI, smad4, JAG1, LAMC1, TLR4, and CREB as well as Hippo signaling pathway, have not been confirmed in the published literature, they still need further verification in vivo and in vitro. Author contributions ML and FL designed the study; WS, YW, LC, and QW analyzed and interpreted the data; WF, XY, QZ, and JW organized the results; FL wrote the manuscript. Acknowledgement