An immune genes signature for predicting mortality in sepsis patients - Lin, Shirong; Li, Ping; Yang, Jibin; Liu, Shiwen; Huang, Shaofang; Huang, Ziyan; Zhou, Congyang; Liu, Ying (Frontiers in Immunology, 2023)
Increasing evidence has supported the core role of the immune system in sepsis. This study aimed to establish a robust gene signature based on immune genes and develop a nomogram that could predict mortality in patients with sepsis. Herein, data were extracted from the Gene Expression Omnibus and Biological Information Database of Sepsis (BIDOS) databases. We enrolled 479 participants with complete survival data using the GSE65682 dataset, and grouped them randomly into training (n = 240) and internal validation (n = 239) sets based on a 1:1 proportion. GSE95233 was set as the external validation dataset (n=51). We validated the expression and prognostic value of the immune genes using the BIDOS database. We established a prognostic immune genes signature (including ADRB2, CTSG, CX3CR1, CXCR6, IL4R, LTB, and TMSB10) via LASSO and Cox regression analyses in the training set. Based on the training and validation sets, the Receiver Operating Characteristic curves and Kaplan-Meier analysis revealed that the immune risk signature has good predictive power in predicting sepsis mortality risk. The external validation cases also showed that patients in the high-risk group had a higher mortality rate than those in the low-risk group. Subsequently, a nomogram integrating the combined immune risk score and other clinical features was developed. Finally, a web-based calculator was built to facilitate a convenient clinical application of the nomogram. In summary, the signature based on the immune gene signatures holds potential as a novel prognostic predictor for sepsis.