Construction of a lncRNA-miRNA-mRNA network to determine the regulatory roles of lncRNAs in psoriasis.
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ABSTRACT:
SUBMITTER: Zhou Q
PROVIDER: S-EPMC6781786 | biostudies-literature | 2019 Nov
REPOSITORIES: biostudies-literature
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Project description:Abdominal aortic aneurysm (AAA) is one of the most significant causes of morbidity and mortality in populations aged >65 years worldwide. However, the underlying mechanisms of AAA based on the competitive endogenous RNA (ceRNA) hypothesis have remained elusive. In the present study, differently expressed long non-coding RNA (lncRNA)-microRNA (miRNA)-mRNA networks in AAA were constructed by analyzing public datasets, including GSE7084, GSE24194 from rats and that of a previous study. A total of 1,219 mRNAs, 2,093 lncRNAs and 57 miRNAs were identified to differently express in AAA. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses were performed to explore the potential roles of differently expressed lncRNAs based on their regulating mRNAs. Based on the ceRNA hypothesis, lncRNA-miRNA-mRNA networks in AAA were, for the first time, constructed at a system-wide level. The present study identified 5 upregulated lncRNAs [nuclear paraspeckle assembly transcript 1, cyclin-dependent kinase inhibitor 2B antisense RNA 1, small Cajal body-specific RNA 10, AC005224.4 and SUMO1/sentrin/SMT3-specific peptidase 3-eukaryotic translation initiation factor 4A1] and the downregulated zinc ribbon domain containing 1 antisense RNA 1 as key lncRNAs in ceRNA networks. To the best of our knowledge, the present study was the first to screen ceRNA networks in AAA. In addition, key lncRNA-mRNA-biological processes analysis indicated that these key lncRNAs were involved in regulating signal transduction, protein amino acid phosphorylation, immune response, transcription, development and cell differentiation. The present study provides novel clues to explore the molecular mechanisms of AAA progression in terms of lncRNA implication.
| S-EPMC6176170 | biostudies-literature
Project description:Previous studies have reported that long, non‑coding RNAs (lncRNAs) are important in cardiovascular disease. However, the lncRNAs involved in myocardial infarction and their detailed mechanism have not been well characterized. In the present study, an affymetrix microarray associated with myocardial infarction was re‑annotated, following which a myocardial infarction‑related differential lncRNA‑mRNA co‑expression network (MILMN) was constructed. Subsequently, pathway enrichment analysis was used for all the mRNAs in the MILMN, and an lncRNA‑pathway network was constructed. It was found that the mRNAs were predominantly involved in certain cardiovascular disease‑associated pathway, for example the dilated cardiomyopathy and mitogen‑activated protein kinase signaling pathway. Finally, a total of 39 key lncRNAs were identified, which regulate crucial pathways in myocardial infarction. Through pathway analysis of these 39 key lncRNAs, the novel function of an annotated lncRNAs‑H19 was predicted, which may regulate apoptosis signal‑regulating kinase, which is a protein that promotes pathological cardiac remodeling following myocardial infarction. The results of the present study not only provide potential non‑coding RNA biomarkers, but also provide further insights into understanding the molecular mechanism of lncRNAs.
| S-EPMC4732855 | biostudies-literature
Project description:BACKGROUND:A growing evidence suggests that long non-coding RNAs (lncRNAs) can function as a microRNA (miRNA) sponge in various diseases including oral cancer. However, the pathophysiological function of lncRNAs remains unclear. METHODS:Based on the competitive endogenous RNA (ceRNA) theory, we constructed a lncRNA-miRNA-mRNA network in oral cancer with the human expression profiles GSE74530 from the Gene Expression Omnibus (GEO) database. We used topological analysis to determine the hub lncRNAs in the regulatory ceRNA network. Then, function enrichment analysis was performed using the clusterProfiler R package. Clinical information was downloaded from The Cancer Genome Atlas (TCGA) database and survival analysis was performed with Kaplan-Meier analysis. RESULTS:A total of 238 potential co-dysregulated competing triples were obtained in the lncRNA-associated ceRNA network in oral cancer, which consisted of 10 lncRNA nodes, 41 miRNA nodes and 122 mRNA nodes. Additionally, we found lncRNA HCG22 exhibiting superior potential as a diagnostic and prognostic marker of oral cancer. CONCLUSIONS:Our findings provide novel insights to understand the ceRNA regulation in oral cancer and identify a novel lncRNA as a potential molecular biomarker.
| S-EPMC7310129 | biostudies-literature
Project description:Hepatocellular carcinoma (HCC) is a heterogeneous malignancy with a high incidence and poor prognosis. Exploration of the underlying mechanisms and effective prognostic indicators is conducive to clinical management and optimization of treatment. The RNA-seq and clinical phenotype data of HCC were retrieved from The Cancer Genome Atlas (TCGA), and differential expression analysis was performed. Then, a differential lncRNA-miRNA-mRNA regulatory network was constructed, and the key genes were further identified and validated. By integrating this network with the online tool-based ceRNA network, an HCC-specific ceRNA network was obtained, and lncRNA-miRNA-mRNA regulatory axes were extracted. RNAs associated with prognosis were further obtained, and multivariate Cox regression models were established to identify the prognostic signature and nomogram. As a result, 198 DElncRNAs, 120 DEmiRNAs, and 2827 DEmRNAs were identified, and 30 key genes identified from the differential network were enriched in four cancer-related pathways. Four HCC-specific lncRNA-miRNA-mRNA regulatory axes were extracted, and SNHG11, CRNDE, MYLK-AS1, E2F3, and CHEK1 were found to be related with HCC prognosis. Multivariate Cox regression analysis identified a prognostic signature, comprised of CRNDE, MYLK-AS1, and CHEK1, for overall survival (OS) of HCC. A nomogram comprising the prognostic signature and pathological stage was established and showed some net clinical benefits. The AUC of the prognostic signature and nomogram for 1-year, 3-year, and 5-year survival was 0.777 (0.657-0.865), 0.722 (0.640-0.848), and 0.630 (0.528-0.823), and 0.751 (0.664-0.870), 0.773 (0.707-0.849), and 0.734 (0.638-0.845), respectively. These results provided clues for the study of potential biomarkers and therapeutic targets for HCC. In addition, the obtained 30 key genes and 4 regulatory axes might also help elucidate the underlying mechanism of HCC.
| S-EPMC7774738 | biostudies-literature
Project description:Aimwe aimed to construct a bioinformatics-based co-regulatory network of mRNAs and non coding RNAs (ncRNAs), which is implicated in the pathogenesis of non-alcoholic fatty liver disease (NAFLD), followed by its validation in a NAFLD animal model.Materials and methodsThe mRNAs-miRNAs-lncRNAs regulatory network involved in NAFLD was retrieved and constructed utilizing bioinformatics tools. Then, we validated this network using an NAFLD animal model, high sucrose and high fat diet (HSHF)-fed rats. Finally, the expression level of the network players was assessed in the liver tissues using reverse transcriptase real-time polymerase chain reaction.Resultsin-silico constructed network revealed six mRNAs (YAP1, FOXA2, AMOTL2, TEAD2, SMAD4 and NF2), two miRNAs (miR-650 and miR-1205), and two lncRNAs (RPARP-AS1 and SRD5A3-AS1) that play important roles as a co-regulatory network in NAFLD pathogenesis. Moreover, the expression level of these constructed network-players was significantly different between NAFLD and normal control. Conclusion and future perspectives: this study provides new insight into the molecular mechanism of NAFLD pathogenesis and valuable clues for the potential use of the constructed RNA network in effective diagnostic or management strategies of NAFLD.
| S-EPMC8269036 | biostudies-literature
Project description:Background: Glioblastoma multiforme (GBM), the most common and aggressive human malignant brain tumor, is notorious for its limited treatment options and poor prognosis. MicroRNAs (miRNAs) are found to be involved in tumorigenesis of GBM. However, a comprehensive miRNA-mRNA regulatory network has still not been established. Methods: A miRNA microarray dataset (GSE90603) was obtained from GEO database. Then, we employed GEO2R tool to perform differential expression analysis. Potential transcription factors and target genes of screened differentially expressed miRNAs (DE-miRNAs) were predicted. The GBM mRNA dataset were downloaded from TCGA database for identifying differentially expressed genes (DEGs). Next, GO annotation and KEGG pathway enrichment analysis was conducted. PPI network was then established, and hub genes were identified via Cytoscape software. The expression and prognostic roles of hub genes was further evaluated. Results: Total 33 DE-miRNAs, consisting of 10 upregulated DE-miRNAs and 23 downregulated DE-miRNAs, were screened. SP1 was predicted to potentially regulate most of screened DE-miRNAs. Three thousand and twenty seven and 3,879 predicted target genes were obtained for upregulated and downregulated DE-miRNAs, respectively. Subsequently, 1,715 upregulated DEGs and 1,259 downregulated DEGs were identified. Then, 149 and 295 potential downregulated and upregulated genes commonly appeared in target genes of DE-miRNAs and DEGs were selected for GO annotation and KEGG pathway enrichment analysis. The downregulated genes were significantly enriched in cGMP-PKG signaling pathway and calcium signaling pathway whereas the upregulated genes were enriched in pathways in cancer and PI3K-Akt signaling pathway. Construction and analysis of PPI network showed that STXBP1 and TP53 were recognized as hub genes with the highest connectivity degrees. Expression analytic result of the top 20 hub genes in GBM using GEPIA database was generally identical with previous differential expression analysis for TCGA data. EGFR, PPP3CB, and MYO5A expression was significantly associated with patients' OS. Conclusions: In this study, we established a potential GBM-related miRNA-mRNA regulatory network, which explores a comprehensive understanding of the molecular mechanisms and provides key clues in seeking novel therapeutic targets for GBM. In the future, more experiments need to be performed to validate our current findings.
| S-EPMC6444190 | biostudies-literature
Project description:BackgroundBreast cancer is a malignant tumor that occurs in the epithelial tissue of the breast gland and has become the most common malignancy in women. The regulation of the expression of related genes by microRNA (miRNA) plays an important role in breast cancer. We constructed a comprehensive breast cancer-miRNA-gene interaction map.MethodsThree miRNA microarray datasets (GSE26659, GSE45666, and GSE58210) were obtained from the GEO database. Then, the R software "LIMMA" package was used to identify differential expression analysis. Potential transcription factors and target genes of screened differentially expressed miRNAs (DE-miRNAs) were predicted. The BRCA GE-mRNA datasets (GSE109169 and GSE139038) were downloaded from the GEO database for identifying differentially expressed genes (DE-genes). Next, GO annotation and KEGG pathway enrichment analysis were conducted. A PPI network was then established, and hub genes were identified via Cytoscape software. The expression and prognostic roles of hub genes were further evaluated.ResultsWe found 6 upregulated differentially expressed- (DE-) miRNAs and 18 downregulated DE-miRNAs by analyzing 3 Gene Expression Omnibus databases, and we predicted the upstream transcription factors and downstream target genes for these DE-miRNAs. Then, we used the GEO database to perform differential analysis on breast cancer mRNA and obtained differentially expressed mRNA. We found 10 hub genes of upregulated DE-miRNAs and 10 hub genes of downregulated DE-miRNAs through interaction analysis.ConclusionsIn this study, we have performed an integrated bioinformatics analysis to construct a more comprehensive BRCA-miRNA-gene network and provide new targets and research directions for the treatment and prognosis of BRCA.
| S-EPMC7657683 | biostudies-literature
Project description:The aim of this study was to establish a novel competing endogenous RNA (ceRNA) network able to predict prognosis in patients with triple-negative breast cancer (TNBC). Differential gene expression analysis was performed using the GEO2R tool. Enrichr and STRING were used to conduct protein-protein interaction and pathway enrichment analyses, respectively. Upstream lncRNAs and miRNAs were identified using miRNet and mirTarBase, respectively. Prognostic values, expression, and correlational relationships of mRNAs, lncRNAs, and miRNAs were examined using GEPIA, starBase, and Kaplan-Meier plotter. It total, 860 upregulated and 622 downregulated differentially expressed mRNAs were identified in TNBC. Ten overexpressed and two underexpressed hub genes were screened. Next, 10 key miRNAs upstream of these key hub genes were predicted, of which six upregulated miRNAs were significantly associated with poor prognosis and four downregulated miRNAs were associated with good prognosis in TNBC. NEAT1 and MAL2 were selected as key lncRNAs. An mRNA-miRNA-lncRNA network in TNBC was constructed. Thus, we successfully established a novel mRNA-miRNA-lncRNA regulatory network, each component of which is prognostic for TNBC.
| S-EPMC7835059 | biostudies-literature
Project description:Background: Ovarian cancer (OC) is the most lethal malignancy in the female reproductive system. Growing evidences demonstrates that competing endogenous RNA (ceRNA) network play crucial roles in the occurrence and progression of tumors. Therefore, we aimed to explore and identify novel mRNA-miRNA-lncRNA ceRNA networks associated with prognosis of OC. Methods: The differentially expressed gene (DEGs) of four expression profiles datasets (GSE5438, GSE40595, GSE38666 and GSE26712) were collected from Gene Expression Omnibus (GEO) database and analyzed with NetworkAnalyst. Intersection of DEGs were further employed for Gene Ontology (GO) and Kyoto Encyclopedia of Gene and Genome (KEGG) pathway analysis. Protein-protein interaction (PPI) network and hub genes of DEGs were also identified. The expression levels and survival analysis of the hub genes in OC and their upstream miRNAs and lncRNAs were performed by various bioinformatics databases. More importantly, ceRNA networks were constructed based on mRNA-miRNA-lncRNA in OC. Results: A total of 178 DEGs including 38 upregulated and 140 downregulated genes from intersected DEGs of four expression profiles were identified in OC. Functional enrichment analysis suggested that the commonly DEGs were enriched in regulating enzyme inhibitor activity, glycosaminoglycan and G protein-coupled receptor binding, cell morphogenesis, and involved in pathways including metabolic process, proteoglycans in cancer. Top 10 hub genes with higher connectivity degree were selected for subsequent expression and prognosis analysis. After take expression levels and prognostic roles of hub genes and their upstream miRNAs and lncRNAs in OC into consideration, 2 mRNAs (TACC3 and CXCR4), 2 miRNAs (hsa-miR-425-5p and hsa-miR-146a-5p) and 3 lncRNAs (FUT8-AS1, LINC00665 and LINC01535) were significantly associated with the poor prognosis of OC. The mRNA-miRNA-lncRNA networks (TACC3-hsa-miR-425-5p-FUT8-AS1 and CXCR4-hsa-miR-146a-5p-LINC00665/LINC01535) were eventually constructed in OC based on ceRNA mechanism. Conclusion: We successfully constructed novel ceRNA network associated with the prognosis of ovarian cancer, which may provide a new strategy for early diagnosis and therapeutic intervention of OC.
| S-EPMC7592000 | biostudies-literature
Project description:Background:To identify pivotal lncRNAs in papillary thyroid cancer (PTC) using lncRNA-mRNA-miRNA ceRNA network analysis. Methods:We obtained gene expression profiles from the gene expression omnibus database. Cancer specific lncRNA, cancer specific miRNA and cancer specific mRNA were identified. An integrated analysis was conducted to detect potential lncRNA-miRNA-mRNA ceRNA in regulating disease transformation. The lncRNA regulated gene ontology (GO) terms and regulated pathways were performed by function analysis. Survival analysis was performed for the pivotal lncRNAs. Results:A total of four lncRNAs, 15 miRNAs and 375 mRNAs are identified as the key mediators in the pathophysiological processes of PTC. GO annotation enrichment analysis showed the most relevant GO terms are signal transduction, integral component of membrane and calcium ion binding. Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis showed different changed genes mainly enriched in pathways in cancer, PI3K-Akt signaling pathway and focal adhesion. Among four lncRNAs, only SLC26A4-AS1 was significantly associated with PTC patient disease free survival. Conclusion:This study has constructed lncRNA-mRNA-miRNA ceRNA networks in PTC. The study provides a set of pivotal lncRNAs for future investigation into the molecular mechanisms.
| S-EPMC6754980 | biostudies-literature