ArticleFrontiers in genetics2025
Identification of key modules and hub genes for sepsis-induced myopathy using weighted gene co-expression network analysis.
Article in Frontiers in genetics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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Who cites it
1 citing paper in PubMed.
- A radiotherapy resistance-related prognostic signature predicts survival and the immune landscape in rectal cancer.Translational cancer research · 2026Article
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Authors and funding
8 authors.
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Abstract
Background: Sepsis-induced myopathy (SIM) is a severe complication of sepsis, leading to significant muscle dysfunction and increased mortality. The molecular mechanisms underlying SIM remain poorly understood, necessitating comprehensive studies to identify potential therapeutic targets. This study aims to explore the molecular basis of SIM through gene expression analysis and bioinformatics approaches. Methods: In this study, we employed a lipopolysaccharide-induced mouse model to investigate the molecular basis of SIM. We conducted comprehensive RNA sequencing of the gastrocnemius muscle, which resulted in the identification of 1,166 genes exhibiting altered expression levels. To further analyze the data, we applied weighted gene co-expression network analysis (WGCNA) to distinguish critical gene clusters associated with SIM. Additionally, we performed functional enrichment analyses using Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and protein-protein interaction (PPI) network approaches. Results: Our findings revealed that the identified gene clusters predominantly pertained to immune response, inflammation, and apoptosis pathways. Notably, validation through real-time quantitative polymerase chain reaction (RT-qPCR) confirmed the significant upregulation of key hub genes, including Cxcl10, Il6, and Stat1. Receiver Operating Characteristic (ROC) curve analysis further indicated the potential diagnostic utility of these hub genes. Additionally, leveraging the Connectivity Map (CMAP) database allowed us to predict six potential pharmacological agents-halcinonide, lomitapide, TG-101348, GSK-690693, loteprednol, and indacaterol-that might serve as therapeutic interventions for SIM. Conclusion: This research advances our understanding of the molecular basis of SIM, presenting new diagnostic biomarkers and potential drug targets. Further studies with larger clinical datasets are warranted to validate these findings and explore the therapeutic potential of the identified drugs.
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