Objective:

To screen candidate genes of sepsis and evaluate their diagnostic value using bioinformatics tools, including weighted gene co-expression network analysis and protein–protein interaction network analysis.

Approach:

• Data Integration: Multiple GEO datasets were integrated, with GSE9960 and GSE28750 as the training set and others for validation.
• Gene Identification: Sepsis-associated genes were identified using weighted gene co-expression network analysis (WGCNA) and protein–protein interaction (PPI) network analysis.
• Functional Enrichment: Functional enrichment was explored using Gene Set Variation Analysis (GSVA) and Gene Set Enrichment Analysis (GSEA) with specific bioinformatics tools.
• Diagnostic Evaluation: Diagnostic potential was assessed through receiver operating characteristic (ROC) curves, and gene expression was validated by quantitative reverse transcription PCR (qRT-PCR).

Key Findings:

• WGCNA identified 1,463 sepsis-associated genes enriched in biosynthesis/metabolism and immune-related pathways.
• Five hub genes (CDK1, CCNB1, CCNA2, AURKB, BUB1) were identified, all highly expressed in sepsis.
• Higher expression of AURKB or BUB1 correlated with shorter overall survival.
• A logistic regression model showed AUC values of 0.747 in the training set and 0.799 in the validation cohort for distinguishing sepsis from healthy controls, indicating good diagnostic performance.
• The model also differentiated septic shock from cardiogenic shock (AUC = 0.743) and non-septic shock (AUC = 0.766), demonstrating its potential utility.

Interpretation:

The identified genes correlate with immune cell infiltration in sepsis; however, results are correlational and should be interpreted as hypothesis-generating.

Limitations:

• Current results do not establish a causal role for the identified genes.
• Further validation in independent prospective cohorts is required to confirm these findings.

Conclusion:

Five sepsis-associated genes were identified, and a logistic regression model based on these genes demonstrated improved diagnostic performance for sepsis.