ArticleJournal of translational medicine2022
Uncovering the gene regulatory network of type 2 diabetes through multi-omic data integration.
Article in Journal of translational medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 18 papers, 1 of them a synthesis that pooled it.
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Who cites it
18 citing papers in PubMed, 1 synthesis or guideline pooled it, 29 citations in OpenAlex.
- Plasma proteins and onset of type 2 diabetes and diabetic complications: Proteome-wide Mendelian randomization and colocalization analyses.Cell reports. Medicine · 2023Pooled it
- Cross-Cultural Nutritional Epigenomics: Diet and Microbiome Interactions Shaping Type 2 Diabetes in Arab and Western Populations.Nutrients · 2026Review
- The Application of Omics Technologies in Type II Diabetes Mellitus Research.Current diabetes reviews · 2026Review
- Single-Cell Multi-Omics in Type 2 Diabetes Mellitus: Revealing Cellular Heterogeneity and Mechanistic Insights.International journal of molecular sciences · 2025Review
- From omics to AI-mapping the pathogenic pathways in type 2 diabetes.FEBS letters · 2025Review
- Foodomics in Diabetes Management: A New Approach.Food science & nutrition · 2025Review
- Gene-Diet Interactions in Diabetes Mellitus: Current Insights and the Potential of Personalized Nutrition.Genes · 2025Review
- Gene expression knowledge graph for patient representation and diabetes prediction.Journal of biomedical semantics · 2025Article
- Epigenetic Insights Into Necrotizing Enterocolitis: Unraveling Methylation-Regulated Biomarkers.Inflammation · 2025Article
- Article
- MicroRNAs and rs1803274 SNP-based BuChe downregulation are associated with metabolic syndrome through ghrelin hydrolysis and expression quantitative trait loci regulation in PD patients.Frontiers in molecular neuroscience · 2025Article
- Identification of TACSTD2 as novel therapeutic targets for cisplatin-induced acute kidney injury by multi-omics data integration.Human genetics · 2024Article
- Gene expression analysis reveals diabetes-related gene signatures.Human genomics · 2024Article
- An Analysis of a Transposable Element Expression Atlas during 27 Developmental Stages in Porcine Skeletal Muscle: Unveiling Molecular Insights into Pork Production Traits.Animals : an open access journal from MDPI · 2023Article
- Molecular Insight into the Pharmacological Potential ofMedicina (Kaunas, Lithuania) · 2023Article
- Potential miRNA-gene interactions determining progression of various ATLL cancer subtypes after infection by HTLV-1 oncovirus.BMC medical genomics · 2023Article
- Correction: Uncovering the gene regulatory network of type 2 diabetes through multi-omic data integration.Journal of translational medicine · 2023Article
- Precision Medicine in Type 2 Diabetes Mellitus: Utility and Limitations.Diabetes, metabolic syndrome and obesity : targets and therapy · 2023Review
Corrections and comments
- Erratum issued
Authors and funding
6 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundType 2 diabetes (T2D) onset is a complex, organized biological process with multilevel regulation, and its physiopathological mechanisms are yet to be elucidated. This study aims to find out the key drivers and pathways involved in the pathogenesis of T2D through multi-omics analysis.
methodsThe datasets used in the experiments comprise three groups: (1) genomic (2) transcriptomic, and (3) epigenomic categories. Then, a series of bioinformatics technologies including Marker set enrichment analysis (MSEA), weighted key driver analysis (wKDA) was performed to identify key drivers. The hub genes were further verified by the Receiver Operator Characteristic (ROC) Curve analysis, proteomic analysis, and Real-time quantitative polymerase chain reaction (RT-qPCR). The multi-omics network was applied to the Pharmomics pipeline in Mergeomics to identify drug candidates for T2D treatment. Then, we used the drug-gene interaction network to conduct network pharmacological analysis. Besides, molecular docking was performed using AutoDock/Vina, a computational docking program.
resultsModule-gene interaction network was constructed using MSEA, which revealed a significant enrichment of immune-related activities and glucose metabolism. Top 10 key drivers (PSMB9, COL1A1, COL4A1, HLA-DQB1, COL3A1, IRF7, COL5A1, CD74, HLA-DQA1, and HLA-DRB1) were selected by wKDA analysis. Among these, COL5A1, IRF7, CD74, and HLA-DRB1 were verified to have the capability to diagnose T2D, and expression levels of PSMB9 and CD74 had significantly higher in T2D patients. We further predict the co-expression network and transcription factor (TF) binding specificity of the key driver. Besides, based on module interaction networks and key driver networks, 17 compounds are considered to possess T2D-control potential, such as sunitinib.
conclusionsWe identified signature genes, biomolecular processes, and pathways using multi-omics networks. Moreover, our computational network analysis revealed potential novel strategies for pharmacologic interventions of T2D.
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