ArticleComputational and mathematical methods in medicine2020
Gene Expression Profiling of Type 2 Diabetes Mellitus by Bioinformatics Analysis.
Article in Computational and mathematical methods in medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.
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16 citing papers in PubMed, 1 synthesis or guideline pooled it, 32 citations in OpenAlex.
- Common miRNAs, Genes, and Regulatory Pathways in Alzheimer's Disease and Type 2 Diabetes Mellitus: An Integrative Analysis of Systematic Reviews, Bioinformatics and Data Mining.Journal of neurochemistry · 2025Pooled it
- Circulating miR-326 and miR-532-3p: novel biomarkers for early gestational diabetes mellitus detection with high diagnostic accuracy.Journal of endocrinological investigation · 2026Article
- Loosening the Lid on Shoulder Osteoarthritis: How the Transcriptome and Metabolic Syndrome Correlate with End-Stage Disease.International journal of molecular sciences · 2025Article
- Gene expression knowledge graph for patient representation and diabetes prediction.Journal of biomedical semantics · 2025Article
- Common molecular links and therapeutic insights between type 2 diabetes and kidney cancer.PloS one · 2025Article
- Integrative analysis of gene expression, protein abundance, and metabolomic profiling elucidates complex relationships in chronic hyperglycemia-induced changes in human aortic smooth muscle cells.Journal of biological engineering · 2024Article
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- Hypoglycemic Activity of Rice Resistant-Starch Metabolites: A Mechanistic Network Pharmacology and In Vitro Approach.Metabolites · 2024Article
- The Role of Cdc42 in the Insulin and Leptin Pathways Contributing to the Development of Age-Related Obesity.Nutrients · 2023Review
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- Establishment and Verification of a Gene Signature for Diagnosing Type 2 Diabetics by WGCNA, LASSO Analysis, and In Vitro Experiments.BioMed research international · 2022Article
- Comprehensive analysis of key m5C modification-related genes in type 2 diabetes.Frontiers in genetics · 2022Article
- Systems Network Pharmacology-Based Prediction and Analysis of Potential Targets and Pharmacological Mechanism ofEvidence-based complementary and alternative medicine : eCAM · 2022Article
- ASK1-Interacting Protein 1 Acts as a Novel Predictor of Type 2 Diabetes.Frontiers in endocrinology · 2022Article
- Pharmacoinformatics and UPLC-QTOF/ESI-MS-Based Phytochemical Screening ofMolecules (Basel, Switzerland) · 2021Article
- qPCR Analysis Reveals Association of Differential Expression ofFrontiers in endocrinology · 2021Article
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Authors and funding
7 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveThe aim of this study was to identify the candidate genes in type 2 diabetes mellitus (T2DM) and explore their potential mechanisms.
methodsThe gene expression profile GSE26168 was downloaded from the Gene Expression Omnibus (GEO) database. The online tool GEO2R was used to obtain differentially expressed genes (DEGs). Gene Ontology (GO) term enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were performed by using Metascape for annotation, visualization, and comprehensive discovery. The protein-protein interaction (PPI) network of DEGs was constructed by using Cytoscape software to find the candidate genes and key pathways.
resultsA total of 981 DEGs were found in T2DM, including 301 upregulated genes and 680 downregulated genes. GO analyses from Metascape revealed that DEGs were significantly enriched in cell differentiation, cell adhesion, intracellular signal transduction, and regulation of protein kinase activity. KEGG pathway analysis revealed that DEGs were mainly enriched in the cAMP signaling pathway, Rap1 signaling pathway, regulation of lipolysis in adipocytes, PI3K-Akt signaling pathway, MAPK signaling pathway, and so on. On the basis of the PPI network of the DEGs, the following 6 candidate genes were identified: PIK3R1, RAC1, GNG3, GNAI1, CDC42, and ITGB1.
conclusionOur data provide a comprehensive bioinformatics analysis of genes, functions, and pathways, which may be related to the pathogenesis of T2DM.
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