ArticleFrontiers in medicine2026
Identification and validation of novel candidate genes with diagnostic value for sepsis via weighted gene co-expression network analysis.
Article in Frontiers in medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Background: Sepsis is a systemic inflammatory response caused by a variety of causes, which is characterized by high morbidity and mortality. Our work aimed to screen the candidate genes of sepsis and evaluate their diagnostic value using bioinformatics tools. Methods: Multiple GEO datasets were integrated. GSE9960 and GSE28750 were merged as the training set, with other datasets used as validation sets for immune infiltration, prognosis, ROC analysis, subtype analysis, and single-cell analysis. Sepsis-associated genes were identified via weighted gene co-expression network analysis (WGCNA) and protein-protein interaction (PPI) network analysis. Functional enrichment was explored using GSVA and GSEA. Diagnostic potential was evaluated by ROC curves. Correlation between gene expression and immune cells was analyzed by Pearson correlation, and gene expression was validated by qRT-PCR. Results: WGCNA identified 1,463 sepsis-associated genes, which were enriched in biosynthesis/metabolism and immune-related signaling pathways. Five hub genes ( Conclusion: In this study, we identified five sepsis-associated genes (
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