Analysis of the function of microRNA-375 in humans using bioinformatics
- Authors:
- Published online on: April 10, 2017 https://doi.org/10.3892/br.2017.889
- Pages: 561-566
Abstract
Introduction
The mature microRNAs (miRs) are approximately 20–22 nucleotide molecules and regulate approximately one-third of human genes. These miRs are termed non-coding RNAs. miRs function by complete or incomplete complementary combination to their target genes, which subsequently affects degradation, stability and translation (1). Subsequently, the target genes influence signaling pathways or other molecules to function (2). The primary (pri)-miRs (length, 300–1,000 mucleotides) transform into precursor (pre)-miRs (length, 70–90 mucleotides) and the pre-miRs transform into mature miRs (length, 20–22 mucleotides) via processing with Dicer enzyme (3).
miR-375 is expressed at low levels in many types of solid tumor, such as esophageal carcinoma (4,5), and gastric (6), liver (7), colon (8) and pancreatic (9) cancers, and has been recorded to be associated with the development and progression of cancer. In addition, it was significant in other diseases, including diabetes (10,11), and autoimmune thyroid (12) and allergic (13) diseases. As miR-375 performs pivotal roles in the above-mentioned diseases and, to the best of our knowledge, there are no detailed studies regarding the regulatory role of its target genes, miRWalk was used in the present study to predict the target genes and further analyze the gene functions using gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways and protein-protein interaction (PPI) analysis. The aim of the study was to obtain further information regarding the function of miR-375 and provide candidate target genes for diagnosis and treatment of certain human diseases.
Materials and methods
Target gene predictions
The target genes were predicted using the miRWalk 1.0 online tool (http://zmf.umm.uni-heidelberg.de/apps/zmf/mirwalk/micrornapredictedtarget.html). This database incorporates the following software programs: DIANAmT (http://diana.imis.athena-innovation.gr/DianaTools), PicTar4 (http://pictar.mdc-berlin.de/cgi-bin/PicTar_vertebrate.cgi), PicTar 5 (http://pictar.mdc-berlin.de/cgi-bin/new_PicTar_vertebrate.cgi), miRanda (http://www.microrna.org), miRWalk (http://zmf.umm.uni-heidelberg.de/apps/zmf/mirwalk/micrornapredictedtarget.html), miRDB (http://www.mirdb.org/miRDB/), RNAhybrid (http://bibiserv.techfak.uni-bielefeld.de/rnahybrid), RNA22 (https://omictools.com/rna22-tool), PITA (https://genie.weizmann.ac.il/pubs/mir07/mir07_data.html) and TargetScan (http://www.targetscan.org). The target genes, which were co-predicted using five of the above software programs, were selected for further analysis.
GO and pathway enrichment analysis
The target genes were analyzed using the Database for Annotation, Visualization and Integrated Discovery (DAVID) online tool [DAVID bioinformatics resources 6.8 (National Institute of Allergy and Infectious Diseases/National Institutes of Health); https://david.ncifcrf.gov/home.jsp]. From this database, GO enrichment and KEGG pathway were used for further analysis (14). The GO enrichment analysis included biological processes (BPs), cell component and molecular function (MF).
PPI network and module analysis
The database, Search Tool of Retrieval of Interacting Genes (STRING; version 10.0; http://string-db.org/) was used to perform the PPI analysis. This database is an effective online tool that overlays 9.6 million proteins from 2,031 organisms and provides 184 million PPIs. The PPI were constructed using Cytoscape software 3.4.0 and the proteins with a combined score >0.4 were selected for PPI analysis (the combined score of >0.4 was considered to be statistically significant). Finally, the Cytoscapeplug-in, Molecular Complex Detection (MCODE), was used to screen the modules of the PPI network and define the top modules as having MCODE scores >5 and node numbers >5.
Statistical analysis
For GO and pathway enrichment analysis, P<0.05 was considered to indicate a statistically significant difference. Furthermore, P-values were adjusted using the false discovery rate (FDR) method for multiple hypothesis testing. FDR<0.05 was established as the threshold value (15). For PPI network and module analysis, P<0.05 was considered to indicate a statistically significant difference.
Results
Target gene prediction
A total of 6,574 target genes of miR-375 were predicted using miRWalk. Of these, 1,325 were co-predicted using five of the software programs and selected for GO enrichment, KEGG pathway and PPI analysis.
GO enrichment and KEGG pathway analysis
The co-predicted target genes were analyzed using GO enrichment and KEGG pathway analysis. During the GO enrichment analysis, the genes were significantly enriched in the regulation of nervous system development and cell differentiation during BP; the highest enrichment of MF was regulating the ion binding (Table I). In KEGG pathway analysis, the genes were enriched in the Hippo signaling pathway, glutamatergic synapse, circadian entrainment and phosphoinositide 3-kinase (PI3K)-Akt signaling (P<0.05), while these enrichments were not significant according to the adjusted FDR (FDR>0.05).
PPI network and module construction
Based on the STRING database, the PPI network of the co-predicted target genes was constructed using Cytoscape software (Fig. 1). In this network, the nodes that were high degree, were identified as the hub proteins. The 10 included hub proteins were as follows: Mechanistic target of rapamycin (mTOR), PH domain and leucine rich repeat protein phosphatase 1 (PHLPP1), ubiquitously transcribed tetratricopeptide repeat containing, Y-linked (UTY), histone deacetylase 2 (HDAC2), F-box and leucine rich repeat protein 19 (FBXL19), KIT proto-oncogene receptor tyrosine kinase (KIT), angiotensinogen (AGT), Janus kinase 2 (JAK2), fibroblast growth factor 2 (FGF2), RNA polymerase II subunit A (POLR2A). The degree of mTOR was 70, which was the highest node. This network consisted of 1,107 nodes and 4,252 edges. There were seven top significant modules selected as the MCODE scores >5 and node numbers >5. The genes in the top seven nodes predominantly regulated chemokines, extracellular matrix (ECM)-receptor interaction, focal adhesion, PI3K-Akt, amoebiasis and protein processing pathways (Table II; P<0.05 and FDR<0.05).
Discussion
miR-375 is important in certain diseases, including cancer, diabetes (10,11) and allergic diseases (4–13). According to previous studies, miR-375 is involved in the development and progression of cancer (16,17). As the functions of miRs are performed by target genes (1–3), further investigation into the regulatory role of the target genes is required. In the current study, the miR-375 target genes were predicted using the miRWalk online tool, which includes 10 software programs. This online tool predicted 6,574 genes and 1,325 of these were co-predicted using five of the software programs, which were further evaluated by GO enrichment, KEGG pathway and PPI analysis.
The GO enrichment analysis indicated that the co-predicted genes were significantly enriched in nervous system development and cell differentiation regulation during BP. In addition, it revealed that the highest enrichment of MF was during the regulation of ion binding. In KEGG pathway analysis, the genes were enriched in glutamatergic synapse, circadian entrainment, and Hippo and PI3K-Akt signaling pathways. These indicated that the functions of the target genes were associated with the growth of nerve and cancer cells, as previously reported (16–19). Abdelmohsen et al (18) evaluated the functions of miR-375 in neuronal cells. The results demonstrated that miR-375 reduces the Hu antigen D levels of neurites and inhibits cell differentiation and, in turn, affects the abundance of dendrites. Osako et al (16) evaluated the role of miR-375 in esophageal squamous cell carcinoma. Thei results revealed that regulation of the miR-375 expression levels affects matrix metalloproteinase 13, and markedly inhibits cancer cell migration and invasion. Lian et al (17) identified that miR-375 may target the recepteur d'origine nantais receptor and function as a tumor-suppressor in gastric cancer. He et al (19) investigated the role of miR-375 as a prognostic biomarker in esophageal carcinoma and the results revealed that this miR may serve as a biomarker in regions of China with a high incidence of esophageal carcinoma. The above-mentioned studies indicated that miR-375 is important in the progression of cancer and certain diseases, and may present as a novel biomarker during cancer diagnosis.
In the PPI analysis, the results demonstrated that the top 10 hub proteins included mTOR, PHLPP1, UTY, HDAC2, FBXL19, KIT, AGT, JAK2, FGF2 and POLR2A. mTOR was the highest degree node and, as a hub protein, interacted with 70 genes in the regulatory network. This protein is a member of the phosphatidylinositol kinase-related kinases, which mediate cellular reactions, such as DNA injury and inanition. mTOR may be arrested during the cell-cycle signaling pathway and causes immunosuppressive effects (20). The PHLPP1 protein acts as a tumor inhibitor in certain types of cancer and accelerates apoptosis by dephosphorylating and inactivating Akt (21). The UTY gene is involved in PPI, as this gene encodes a protein that contains tetratricopeptide repeats (22). The HDAC2 protein has a significant role in cell cycle progression, transcriptional regulation and developmental events by forming transcription inhibitory factor complexes (23). The FBXL19 protein is a member of the E3 ubiquitin ligases. This protein combines with the transmembrane receptor, interleukin 1 receptor 1 and regulates lung inflammation and psoriasis (24). The KIT protein is a type 3 transmembrane receptor of mast cell growth factor. The gene mutations were reported to be associated with gastrointestinal stromal tumors, acute myeloid leukemia, peel spot disease and large cell disease (25). The AGT protein participates in the pathogenesis and progression of hypertension and pre-eclampsia. Furthermore, gene mutations were reported to be associated with a susceptibility to hypertension, the causing of nephric tubular dysgenesis and the disorder of nephric tubular development. The defects of this gene were proposed to be associated with atrial fibrillation and inflammatory bowel disease (26–28). The JAK2 gene has been implicated in cytokine receptor signaling pathways and was identified to be associated with the reactions to human interferon-γ (29). The FGF2 protein is a member of the FGF family and is involved in wound healing, limb and nervous system development, and carcinoma growth (30). POLR2A encodes the largest subunit of RNA polymerase II, the product of this gene includes a carboxy-terminal domain composed of heptapeptide repeats, which are required for polymerase activity (31). The functions of the above-mentioned hub proteins indicate that the hub proteins were particularly important in certain BPs, and primarily regulated the development and progression of cancer, hypertension, essential thrombocythemia and inflammation. Recent studies reveal that certain hub proteins may serve as novel biomarkers and targets in certain diseases (20,23,25,27,28).
The PPI module analysis demonstrated that the genes in the seven selected top modules were predominantly enriched in chemokines, ECM-receptor interactions, focal adhesion, the PI3K-Akt signaling pathway, amoebiasis and the protein processing signaling pathway. The present study indicates that miR-375 is important in immunologic disease and cancer progression, as previously reported (12,13,16). Furthermore, chemokine and ECM-receptor interactions influence the immune system. Lu et al (32) found that miR-375 was downregulated in esophageal squamous and bronchial columnar epithelial cells, using interleukin (IL)-13 stimulation. This indicated that miR-375 was involved in regulating and fine adjustment to IL-13-mediated reactions. The PI3K-Akt signaling pathway has been reported to be closely associated with the growth of cells, such as proliferation, migration, invasion and metastasis in cancer (33–35), and certain other diseases, including diabetic kidney (36) and traumatic brain injury (37).
In conclusion, miR-375 is important in certain diseases, such as cancer, metabolic and immune disease. The target genes and hub proteins predicted during the current study may present as potential novel biomarkers in tumor diagnosis and as candidate targets during treatment of certain diseases. In addition, the current study provides a basis for further research into the function of miR-375.
Acknowledgements
The current study was funded by grants from the Technical New Star of Zhujiang, Pan Yu district, Guangzhou (grant nos. 2013-special-15-6.09 and 2013-special-15-6.10), the Traditional Chinese Medicine Bureau of Guangdong Province (grant no. 20161186) and the Science and technology program of Guangdong (grant no. 2016A020215012).
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