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pmid:41582386
Decoding Diabetes: Hub Genes as Pivotal Players in Cardiomyopathy and Kidney Disease.
Jiarong Liu · Wen Chen · Yun Zou · Jixiong Xu
Endocrine, metabolic & immune disorders drug targets · 2026 doi:10.2174/0118715303393363251028113435
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Complete file
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In this study, we performed an in- -depth bioinformatics analysis to identify shared therapeutic targets and key molecular players in DKD and DCM.\nMETHODS: Integrated computational and experimental approaches were employed. Bioinformatics analysis of GEO datasets (GSE30122 and GSE197850) identified differentially expressed genes (DEGs). Hub genes were extracted via protein-protein interaction networks and functional enrichment. In vitro validation was performed using AGE-stimulated cardiomyocytes and podocytes analyzed by qPCR, complemented by in vivo studies in rat models. Additionally, protein-chemical interactions and drug repurposing analyses were performed.\nRESULTS: We identified 48 common DEGs (P-adj< 0.05 and |log2FC| > 1.0) and prioritized 7 hub genes (CD200, CRHBP, DHRS3, EMCN, HPGD, PDGFRB, and SULF1), which were validated as dysregulated in in vitro and in vivo models. Computational screening revealed 10 promising therapeutic candidates (P-adj < 0.05) targeting core pathogenic networks.\nDISCUSSION: Our study is one of the first to simultaneously investigate the molecular underpinnings of DKD and DCM by integrating bioinformatics data with experimental validation. Meanwhile, the relatively small sample sizes may limit the statistical power and generalizability of the identified DEGs.\nCONCLUSION: This study uncovers novel shared mechanisms between DCM and DKD, providing a framework for dual-organ protective therapies to advance the management of diabetic complications.", "claims": [{"id": "claim-method", "statement": "To compare the two datasets, we analyzed each separately using the R package “limma” (Version 4.3.1).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:41582386", "locator": "sections[4] \"Identification of DEGs through Data Pre-processing and Screening\" paragraph 2", "quote": "To compare the two datasets, we analyzed each separately using the R package “limma” (Version 4.3.1)."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets analyzed in our study were downloaded from the GEO database, and are available from the corresponding author upon reasonable request.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:41582386", "locator": "sections[36] \"AVAILABILITY OF DATA AND MATERIALS\" paragraph 2", "quote": "The datasets analyzed in our study were downloaded from the GEO database, and are available from the corresponding author upon reasonable request."}], "state": "complete"}, {"id": "claim-target-result", "statement": "A Venn diagram analysis revealed 48 overlapping DEGs (Fig. 2C), and heatmaps of these overlapping DEGs are shown in Figs. 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