ArticleFrontiers in immunology2025
SHKBP1 is a target for sepsis: evidence from WGCNA and multiple machine learning algorithms.
Article in Frontiers in immunology, 2025. 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: Identifying novel biomarkers for sepsis is essential for improving patient outcomes. Cuproptosis, a recently discovered form of cell death associated with various diseases, has an unclear relationship with sepsis. This study aimed to elucidate the expression patterns of cuproptosis-related genes(CRGs) in sepsis, identifying potential biomarkers and therapeutic targets. Methods: We investigated the expression patterns of cuproptosis-related genes in sepsis and performed consensus clustering. A diagnostic model for sepsis was constructed using weighted gene co-expression network analysis (WGCNA) combined with four machine learning algorithms. Prognosis-related genes were identified via Kaplan-Meier survival analysis and validated in septic mice. Results: We identified 28 differentially expressed CRGs and characterized a specific immune landscape. Our findings showed that sepsis samples could be divided into two clusters based on CRGs expression. We established a diagnostic model based on five key genes(SHKBP1,ICAM2,CTSD,SNX3, and SLC22A4), and Kaplan-Meier survival analysis revealed that SHKBP1 was significantly correlated with both the diagnosis and prognosis of sepsis. Conclusion: Our study provides a comprehensive analysis of CRGs expression in sepsis, establishes a diagnostic model, and identifies SHKBP1 as a biomarker for both diagnosis and prognosis prediction, offering new insights for sepsis management.
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