AUTHOR=Lin Siming , Cai Kexin , Chen Ai , Wu Weibin , Lian Guili , Feng Shaodan , Lin Zhihong , Xie Liangdi TITLE=Identification of key modules and hub genes for sepsis-induced myopathy using weighted gene co-expression network analysis JOURNAL=Frontiers in Genetics VOLUME=Volume 16 - 2025 YEAR=2025 URL=https://www.frontiersin.org/journals/genetics/articles/10.3389/fgene.2025.1607575 DOI=10.3389/fgene.2025.1607575 ISSN=1664-8021 ABSTRACT=BackgroundSepsis-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.MethodsIn 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.ResultsOur 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.ConclusionThis 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.