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Identification of core gene in obese type 2 diabetes patients using bioinformatics analysis.

ABSTRACT: Objectives Adipocytes and adipocyte lipid metabolism are closely related with obesity and type 2 diabetes, but the molecular mechanism still needs further investigation. The aim of this study is to discover the adipocyte genes and pathways involved in obesity and type 2 diabetes using bioinformatics analysis.Methods The GSE27951 gene expression profile was obtained. Software and online tools (STRING, Cytoscape, BioGPS, CTD, and FunRich) were used to identify core genes.21 human subcutaneous adipose samples, with 10 from type 2 diabetic patients and 11 from normal controls, were included in these analyses.Results 184 differentially expressed genes (DEGs) including 42 up-regulated genes and 142 down-regulated genes were found to be enriched in metabolism, receptor activity, collagen type IV and glutamine biosynthesis I pathway by using the enrichment analysis. Seven hub genes were identified from the PPI network using various software (Cytoscape, STRING, BioGPS, and CTD). Four core genes (COL4A2, ACACB, GLUL, and CD36) were found to be highly expressed in subcutaneous adipose tissue of obese patients accompanying type 2 diabetes.Conclusion COL4A2, ACACB, GLUL and CD36 might be the core molecular biomarkers of obesity in patients with or without type 2 diabetes.

SUBMITTER: Dong Z

PROVIDER: S-EPMC8183531 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature

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Identification of core gene in obese type 2 diabetes patients using bioinformatics analysis.

Dong Zhiyong Z Lei Xinyi X Kujawa Stacy A SA Bolu NaciEmre N Zhao Hong H Wang Cunchuan C

Adipocyte 20211201 1

<b>Objectives</b> Adipocytes and adipocyte lipid metabolism are closely related with obesity and type 2 diabetes, but the molecular mechanism still needs further investigation. The aim of this study is to discover the adipocyte genes and pathways involved in obesity and type 2 diabetes using bioinformatics analysis.<b>Methods</b> The GSE27951 gene expression profile was obtained. Software and online tools (STRING, Cytoscape, BioGPS, CTD, and FunRich) were used to identify core genes.21 human sub ...[more]

PMID: 34085602

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Project description:Background Gastric cancer is the third leading cause of cancer-related mortality in China. Most patients with gastric cancer have no obvious early symptoms; thus, many of them are in the middle and late stages of gastric cancer at first diagnosis and miss the best treatment opportunity. Molecular targeted therapy is particularly important in changing this status quo. Methods Three microarray datasets (GSE29272, GSE33651, and GSE54129) were selected from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were screened using GEO2R. The Database for Annotation, Visualization and Integrated Discovery (DAVID) was used to analyze the functional features of these DEGs and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment. The protein-protein interaction (PPI) of these DEGs was visualized by Cytoscape software. The expressions of hub genes were evaluated based on Gene Expression Profiling Interactive Analysis (GEPIA). Moreover, we used the online Kaplan-Meier plotter survival analysis tool to evaluate the prognostic values of hub genes. The Target Scan database was used to predict microRNAs that could regulate the target gene, collagen type IV alpha 1 chain (COL4A1). The OncomiR database was used to analyze the expression levels of three microRNAs, as well as the relationships with tumor stage, grade, and prognosis. Results We identified 78 DEGs, including 53 upregulated genes and 25 downregulated genes. The DEGs were mainly enriched in extracellular matrix organization, extracellular structure organization, and response to wounding. Moreover, three KEGG pathways were markedly enriched, including focal adhesion, complement and coagulation cascades, and extracellular matrix (ECM)-receptor interaction. Among these 78 genes, we selected 10 hub genes. The overexpression levels of these hub genes were closely related to poor prognosis and the development of gastric cancer (except for COL3A1, LOX, and CXCL8). Moreover, we found that microRNA-29a-3p, miR-29b-3p, and miR-29c-3p were the potential microRNAs that could regulate the target gene, COL4A1. Conclusions Our results showed that FN1, COL1A1, TIMP1, COL1A2, SPARC, COL4A1, and SERPINE1 could contribute to the development of novel molecular targets and biomarker-driven treatments for gastric cancer.

Project description:BackgroundEsophageal squamous cell carcinoma (ESCC) is a common human malignancy worldwide. The tumorigenesis mechanism in ESCC is unclear.Materials and methodsTo explore potential therapeutic targets for ESCC, we analyzed 3 microarray datasets (GSE20347, GSE38129, and GSE67269) derived from the gene expression omnibus (GEO) database. Then, the GEO2R tool was used to screen out differently expressed genes (DEGs) between ESCC and normal tissue. Gene ontology function and kyoto encyclopedia of genes and genomes pathway enrichment analysis were performed using the database for annotation, visualization and integrated discovery to identify the pathways and functional annotation of DEGs. Protein-protein interaction of these DEGs was analyzed based on the search tool for the retrieval of interacting genes database and visualized by Cytoscape software. In addition, we used encyclopedia of RNA interactomes (ENCORI), gene expression profiling interactive analysis (GEPIA), and the human protein atlas to confirm the expression of hub genes in ESCC. Finally, GEPIA was used to evaluate the prognostic value of hub genes expression in ESCC patients and we estimated the associations between hub genes expression and immune cell populations (B Cell, CD8+ T Cell, CD4+ T Cell, Macrophage, Neutrophil, and Dendritic Cell) in esophageal carcinoma (ESCA) using tumor immune estimation resource (TIMER).ResultsIn this study, 707 DEGs (including 385 upregulated genes and 322 downregulated genes) and 6 hub genes (cyclin B1 [CCNB1], cyclin dependent kinase 1 [CDK1], aurora kinase A [AURKA], ubiquitin conjugating enzyme E2C [UBE2C], cyclin A2 [CCNA2], and cell division cycle 20 [CDC20]) were identified. All of the 6 hub genes were highly expressed in ESCC tissues. Among of them, only CCNB1 and CDC20 were associated with stage of ESCC and all of them were not associated with survival time of patients.ConclusionDEGs and hub genes were confirmed in our study, providing a thorough, scientific and comprehensive research goals for the pathogenesis of ESCC.

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Identification of core gene in obese type 2 diabetes patients using bioinformatics analysis.

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Identification of core gene in obese type 2 diabetes patients using bioinformatics analysis.

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