Dataset Information

Bioinformatics analysis of the regulatory lncRNA?miRNA?mRNA network and drug prediction in patients with hypertrophic cardiomyopathy.

ABSTRACT: Hypertrophic cardiomyopathy (HCM) is a complex inherited cardiovascular disease. The present study investigated the long noncoding (lnc)RNA/microRNA (mi)RNA/mRNA expression pattern of patients with HCM and aimed to identify key molecules involved in the development of this condition. An integrated strategy was conducted to identify differentially expressed miRNAs (DEmiRs), differentially expressed lncRNAs (DElncs) and differentially expressed genes (DEGs) based on the GSE36961 (mRNA), GSE36946 (miRNA), GSE68316 (lncRNA/mRNA) and GSE32453 (mRNA) expression profiles downloaded from the Gene Expression Omnibus datasets. Bioinformatics tools were employed to perform function and pathway enrichment analysis, protein?protein interaction, lncRNA?miRNA?mRNA and hub gene networks. Subsequently, DEGs were used as targets to predict drugs. The results indicated that a total of 2,234 DElncs (1,120 upregulated and 1,114 downregulated), 5 DEmiRs (2 upregulated and 3 downregulated) and 42 DEGs (35 upregulated and 7 downregulated) were identified in 4 microarray profiles. Gene ontology analysis revealed that DEGs were mainly involved in actin filament and stress fiber formation and in calcium ion binding, whereas Kyoto Encyclopedia of Genes and Genomes pathway analysis identified the hypoxia inducible factor?1, transforming growth factor?? and tumor necrosis factor signaling pathways as the main pathways involved in these processes. The hub genes were screened using cytoHubba. A total of 1,086 lncRNA?miRNA?mRNA interactions including 67 lncRNAs, 5 miRNAs and 25 mRNAs were mined in the present study based on prediction websites. Drug prediction indicated that the targeted drugs mainly included angiotensin converting enzyme inhibitors or ??blockers. A comprehensive bioinformatics analysis of the molecular regulatory lncRNA?miRNA?mRNA network was performed and potential therapeutic applications of drugs were predicted in HCM patients. The data may unravel the future molecular mechanism of HCM.

SUBMITTER: Li J

PROVIDER: S-EPMC6579968 | biostudies-literature | 2019 Jul

REPOSITORIES: biostudies-literature

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Bioinformatics analysis of the regulatory lncRNA‑miRNA‑mRNA network and drug prediction in patients with hypertrophic cardiomyopathy.

Li Jiajianghui J Wu Zining Z Zheng Deqiang D Sun Yue Y Wang Sisi S Yan Yuxiang Y

Molecular medicine reports 20190523 1

Hypertrophic cardiomyopathy (HCM) is a complex inherited cardiovascular disease. The present study investigated the long noncoding (lnc)RNA/microRNA (mi)RNA/mRNA expression pattern of patients with HCM and aimed to identify key molecules involved in the development of this condition. An integrated strategy was conducted to identify differentially expressed miRNAs (DEmiRs), differentially expressed lncRNAs (DElncs) and differentially expressed genes (DEGs) based on the GSE36961 (mRNA), GSE36946 ( ...[more]

PMID: 31180540

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Project description:Asthma is a chronic respiratory disease caused by environment-host interactions. Bronchial epithelial cells (BECs) are the first line of defense against environmental toxins. However, the mechanisms underlying the role of BECs in severe asthma (SA) are not yet fully understood. Long noncoding RNAs (lncRNAs) and microRNAs (miRNAs) have been shown to play important roles in the regulation of gene expression in the pathogenesis of SA. In this study, bioinformatics was used for the first time to reveal the lncRNA-miRNA-mRNA regulatory network of BECs in SA. Five mRNA datasets of bronchial brushing samples from patients with SA and healthy controls (HC) were downloaded from the Gene Expression Omnibus (GEO) database. A combination of the Venn diagram and robust rank aggregation (RRA) method was used to identify core differentially expressed genes (DEGs). Protein-protein interaction (PPI) analysis of core DEGs was performed to screen hub genes. The miRDB, miRWalk, and ENCORI databases were used to predict the miRNA-mRNA relationships, and the ENCORI and starBase v2.0 databases were used to predict the upstream lncRNAs of the miRNA-mRNA relationships. Four core DEGs were identified: carcinoembryonic antigen-related cell adhesion molecule 5 (CEACAM5), interleukin-1 receptor type 2 (IL1R2), trefoil factor 3 (TFF3), and vascular endothelial growth factor A (VEGFA). These 4 core DEGs indicated that SA was not significantly associated with sex. Enrichment analysis showed that the MAPK, Rap1, Ras, PI3K-Akt and Calcium signaling pathways may serve as the principal pathways of BECs in SA. A lncRNA-miRNA-mRNA regulatory network of the severe asthmatic bronchial epithelium was constructed. The top 10 competing endogenous RNAs (ceRNAs) were FGD5 antisense RNA 1 (FGD5-AS1), metastasis associated lung adenocarcinoma transcript 1 (MALAT1), X inactive specific transcript (XIST), HLA complex group 18 (HCG18), small nucleolar RNA host gene 16 (SNHG16), has-miR-20b-5p, has-miR-106a-5p, hsa-miR-106b-5p, has-miR-519d-3p and Fms related receptor tyrosine kinase 1 (FLT1). Our study revealed a potential mechanism for the lncRNA-miRNA-mRNA regulatory network in BECs in SA.

Project description:Liver hepatocellular carcinoma (LIHC) is one of the major causes of cancer-related death worldwide with increasing incidences, however there are very few studies about the underlying mechanisms and pathways in the development of LIHC. We obtained LIHC samples from The Cancer Genome Atlas (TCGA) to screen differentially expressed mRNAs, lncRNAs, miRNAs and driver mutations. The Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway, Gene ontology enrichment analyses and protein-protein interaction (PPI) network were performed. Moreover, we constructed a competing endogenous lncRNAs-miRNAs-mRNAs network. Finally, cox proportional hazards regression analysis was used to identify important prognostic differentially expressed genes. Total of 1284 mRNAs, 123 lncRNAs, 47 miRNAs were identified within different tissues of LIHC patients. GO analysis indicated that upregulated and downregulated differentially expressed mRNAs (DEmRNAs) were mainly associated with cell division, DNA replication, mitotic sister chromatid segregation and complement activation respectively. Meanwhile, KEGG terms revealed that upregulated and downregulated DEmRNAs were primarily involved in DNA replication, Metabolic pathways, cell cycle and Metabolic pathways, chemical carcinogenesis, retinol metabolism pathway respectively. Among the DERNAs, 542 lncRNAs-miRNAs-mRNAs pairs were predicted to construct a ceRNA regulatory network including 35 DElncRNAs, 26 DEmiRNAs and 112 DEmRNAs. In the Kaplan-Meier analysis, total of 43 mRNAs, 14 lncRNAs and 3 miRNAs were screened out to be significantly correlated with overall survival of LIHC. The mutation signatures were analyzed and its correlation with immune infiltrates were evaluated using the TIMER in LIHC. Among the mutation genes, TTN mutation is often associated with poor immune infiltration and a worse prognosis in LIHC. This work conducted a novel lncRNAs-miRNAs-mRNAs network and mutation signatures for finding potential molecular mechanisms underlying the development of LIHC. The biomarkers also can be used for predicting prognosis of LIHC.

Project description:Background:Chronic obstructive pulmonary disease (COPD) has become a major cause of morbidity and mortality worldwide. Increasing evidence indicates that aberrantly expressed microRNAs (miRNAs) are involved in the pathogenesis of COPD. However, an integrative exploration of miRNA-mRNA regulatory network in COPD plasma remains lacking. Methods:The microarray datasets GSE24709, GSE61741, and GSE31568 were downloaded from the GEO database and analyzed using GEO2R tool to identify differentially expressed miRNAs (DEMs) between COPD and normal plasma. The consistently changing miRNAs in the three datasets were screened out as candidate DEMs. Potential upstream transcription factors and downstream target genes of candidate DEMs were predicted by FunRich and miRNet, respectively. Next, GO annotation and KEGG pathway enrichment analysis for target genes were performed using DAVID. Then, PPI and DEM-hub gene network were constructed using the STRING database and Cytoscape software. Finally, GSE56768 was used to evaluate the hub gene expressions. Results:A total of nine (six upregulated and three downregulated) DEMs were screened out in the above three datasets. SP1 was predicted to potentially regulate most of the downregulated DEMs, while YY1 and E2F1 could regulate both upregulated and downregulated DEMs. 1139 target genes were then predicted, including 596 upregulated DEM target genes and 543 downregulated DEM target genes. Target genes of DEMs were mainly enriched in PI3K/Akt signaling pathway, mTOR signaling pathway, and autophagy. Through the DEM-hub gene network construction, most of the hub genes were found to be potentially modulated by miR-497-5p, miR-130b-5p, and miR-126-5p. Among the top 12 hub genes, MYC and FOXO1 expressions were consistent with that in the GSE56768 dataset. Conclusion:In the study, potential miRNA-mRNA regulatory network was firstly constructed in COPD plasma, which may provide a new insight into the pathogenesis and treatment of COPD.

Project description:Background: Osteoarthritis (OA) is a degenerative joint disease that seriously affects the quality of people. Unfortunately, the pathogenesis of OA has not been fully known. Therefore, this study aimed to construct a ceRNA regulatory network related to OA to explore the pathogenesis of OA. Methods: Differentially expressed circRNAs (DEcircRNAs), microRNAs (DEmiRNAs), and mRNAs (DEmRNAs) were obtained from the Gene Expression Omnibus microarray data (GSE175959, GSE105027, and GSE169077). The miRNA response elements and target mRNAs were identified using bioinformatics approaches. Additionally, a circRNA-miRNA-mRNA network was established using Cytoscape version 3.8.0. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses of mRNAs in the network were conducted to explore the possible mechanisms underlying OA development. Protein-protein interaction (PPI) analysis was performed to determine the hub genes. Based on the hub genes, a sub network was constructed using Cytoscape 3.8.0 version. Finally, connectivity map (CMap) and drug-gene interaction database (DGIdb) analyses were performed to identify the potential therapeutic targets for OA. Results: Altogether, five DEcircRNAs, 89 DEmiRNAs, and 345 DEmRNAs were identified. Moreover, a circRNA-miRNA-mRNA network was established using three circRNAs, seven miRNAs, and 37 mRNAs. GO and KEGG analyses demonstrated that the mRNAs in the network could be related to the occurrence and development of OA. PPI analysis was performed and six key genes, namely serpin family H member 1 [SERPINH1], collagen type VIII alpha 2 chain [COL8A2], collagen type XV alpha 1 chain [COL15A1], collagen type VI alpha 3 chain [COL6A3], collagen type V alpha 1 chain [COL5A1], and collagen type XI alpha 1 chain [COL11A1], were identified. Furthermore, a circRNA-miRNA-hub gene subnetwork was established in accordance with two circRNAs (hsa_circ_0075320 and hsa_circ_0051428), two miRNAs (hsa-miR-6124 and hsa-miR-1207-5p), and six hub genes (COL11A1, SERPINH1, COL6A3, COL5A1, COL8A2, and COL15A1). Finally, three chemicals (noscapine, diazepam, and TG100-115) based on CMap analysis and two drugs (collagenase Clostridium histolyticum and ocriplasmin) based on DGIdb were discovered as potential treatment options for OA. Conclusion: This study presents novel perspectives on the pathogenesis and treatment of OA based on circRNA-related competitive endogenous RNA regulatory networks.

Project description:BackgroundMicroRNA (miRNA) has been confirmed to be involved in the occurrence, development, and prevention of diabetic nephropathy (DN), but its mechanism of action is still unclear.ObjectiveWith the help of the GEO database, bioinformatics methods are used to explore the miRNA-mRNA regulatory relationship pairs related to diabetic nephropathy and explain their potential mechanisms of action.MethodsThe DN-related miRNA microarray dataset (GSE51674) and mRNA expression dataset (GSE30122) are downloaded through the GEO database, online analysis tool GEO2R is used for data differential expression analysis, TargetScan, miRTarBase, and miRDB databases are used to predict potential downstream target genes regulated by differentially expressed miRNAs, and intersection with differential genes is used to obtain candidate target genes. According to the regulatory relationship between miRNA and mRNA, the miRNA-mRNA relationship pair is clarified, and the miRNA-mRNA regulatory network is constructed using Cytoscape. DAVID is used to perform GO function enrichment analysis and KEGG pathway analysis of candidate target genes. By GeneMANIA prediction of miRNA target genes and coexpressed genes, the protein interaction network is constructed. Results and Conclusions. A total of 67 differentially expressed miRNAs were screened in the experiment, of which 42 were upregulated and 25 were downregulated; a total of 448 differentially expressed mRNAs were screened, of which 93 were upregulated and 355 were downregulated. Using TargetScan, miRTarBase, and miRDB databases to predict downstream targets of differentially expressed miRNAs, 2283 downstream target genes coexisting in 3 databases were predicted to intersect with differentially expressed mRNAs to obtain 96 candidate target genes. Finally, 44 miRNA-mRNA relationship pairs consisting of 12 differentially expressed miRNAs and 27 differentially expressed mRNAs were screened out; further analysis showed that miRNA regulatory network genes may participate in the occurrence and development of diabetic nephropathy through PI3K/Akt, ECM-receptor interaction pathway, and RAS signaling pathway.

Dataset Information

Bioinformatics analysis of the regulatory lncRNA?miRNA?mRNA network and drug prediction in patients with hypertrophic cardiomyopathy.

Publications

Bioinformatics analysis of the regulatory lncRNA‑miRNA‑mRNA network and drug prediction in patients with hypertrophic cardiomyopathy.

Similar Datasets

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