Establishment of a Predictive Model for Mortality in Sepsis Patients Using WGCNA and Machine Learning Algorithms.

Background: Predicting mortality in sepsis patients remains a challenge in clinical settings. This study aimed to identify genes associated with sepsis-related mortality and determine the predictive value of a model using these death-related genes. Methods: We employed Weighted Gene Co-expression Network Analysis (WGCNA), differential expression analysis, and machine learning (ML) algorithms to identify genes related to sepsis mortality by analyzing the GSE185263 dataset. A predictive model was constructed base on the genes related to sepsis mortality and validated using external cohorts. Blood samples from 120 sepsis patients were collected to test the expression of key genes. The predictive value of the key genes was verified in clinical samples. Results: WGCNA results revealed that the turquoise module was related to sepsis mortality. Thirty-five death-related genes were identified by intersecting module genes with differentially expressed genes between survivor and non-survivor samples. Three ML algorithms identified TMTC1, AMOTL1, and PTGFR as key genes. The model constructed with these three genes showed moderate predictive value. External cohorts with a total of 523 samples validated the predictive value of the model at moderate to high levels. Clinical sample results indicated that all three genes were elevated in deceased sepsis patients, and the predictive value was moderate. Combining the model with the APACHE II score achieved high predictive accuracy. Conclusion: This study identified and validated three genes related to sepsis mortality and established a predictive model that performed well in distinguishing sepsis patients at high risk of death.