ArticleBMC infectious diseases2025
Integrated bioinformatics and experiment validation reveal cuproptosis-related biomarkers and therapeutic targets in sepsis-induced myocardial dysfunction.
Article in BMC infectious diseases, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Identification and Experimental Validation of PANoptosis Key Genes for Constructing a PANoptosis Risk Diagnostic Model in Intervertebral Disc Degeneration.The journal of gene medicine · 2026Article
- Effects of cuproptosis and its application in inflammatory bowel disease (Review).International journal of molecular medicine · 2026Review
- Integrative bioinformatics and machine learning reveal hub genes and immune signatures bridging type 2 diabetes mellitus, fracture susceptibility, and osteoblast differentiation dysfunction.Mammalian genome : official journal of the International Mammalian Genome Society · 2026Article
- Exploring the mechanism of Dieda Qili Tablet on fracture healing based on network pharmacology combined with machine learning models.Scientific reports · 2026Article
- Targeting lung cancer: synergistic therapeutic strategy of cuproptosis and immunogenic cell death.Frontiers in cell and developmental biology · 2026Review
- Sepsis-induced cardiomyopathy: mechanisms, epidemiology, diagnosis, and treatments.Frontiers in immunology · 2026Review
- Screening of hub genes and immunocytes related to tendon injury based on bioinformatics and machine learning models.Scientific reports · 2025Article
- Serum S100A12 in the clinical diagnosis of sepsis-induced myocardial dysfunction: an integrated bioinformatics and clinical data analysis.Frontiers in cardiovascular medicine · 2025Article
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Authors and funding
7 authors.
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Abstract
backgroundSepsis-induced myocardial dysfunction (SIMD) is a serious sepsis complication with high mortality, yet current diagnostic and therapeutic approaches remain limited. The lack of early, specific biomarkers and effective treatments necessitates exploration of novel mechanisms. Recently, cuproptosis has been implicated in various diseases, but its role in SIMD is unclear. This study aimed to identify cuproptosis-related biomarkers and potential therapeutic agents, supported by animal model validation.
methodsFour GEO datasets (GSE79962, GSE267388, GSE229925, GSE229298) were analyzed using Limma and WGCNA to identify overlapping genes from differentially expressed genes (DEGs), cuproptosis-related DEGs (DE-CRGs), and module-associated genes. Gene Set Enrichment Analysis (GSEA) and single-sample GSEA (ssGSEA) were performed to assess biological functions and immune cell infiltration, respectively. ceRNA and transcription factor networks were constructed to explore gene regulatory mechanisms, while consensus clustering was employed to define cuproptosis-related subtypes. Diagnostic genes were selected through SVM-RFE, LASSO, and random forest models. Additionally, potential gene-targeting agents were predicted using drug-gene interaction analysis. The findings were validated in SIMD animal models through qPCR and immunohistochemical analysis to confirm gene expression.
resultsPDHB and DLAT emerged as key cuproptosis-related biomarkers. GSEA indicated upregulation of oxidative phosphorylation and downregulation of chemokine signaling. ssGSEA revealed negative correlations with several immune cell types. A ceRNA network (51 nodes, 56 edges) was constructed. Machine learning identified PDHB, NDUFA9, and TIMMDC1 as diagnostic genes, with PDHB showing high accuracy (AUC = 0.995 in GSE79962; AUC = 0.960, 0.864, and 0.984 in external datasets). Using the DSigDB database, we predicted six drugs that exhibit significant binding activity with PDHB. qPCR and immunohistochemistry confirmed reduced PDHB and DLAT expression in SIMD animal models.
conclusionThis study identifies PDHB and DLAT as cuproptosis-related biomarkers, addressing the diagnostic and therapeutic gaps in SIMD by unveiling novel molecular insights for early intervention and targeted treatment. CLINICAL TRIAL NUMBER: Not applicable.
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