Identification of featured necroptosis-related genes and imbalanced immune infiltration in sepsis via machine learning - She, Han; Tan, Lei; Yang, Ruibo; Zheng, Jie; Wang, Yi; Du, Yuanlin; Peng, Xiaoyong; Li, Qinghui; Lu, Haibin; Xiang, Xinming; Hu, Yi; Liu, Liangming; et al. (Frontiers in Genetics, 2023)
Background: The precise diagnostic and prognostic biological markers were needed in immunotherapy for sepsis. Considering the role of necroptosis and immune cell infiltra-tion in sepsis, differentially expressed necroptosis-related genes (DE-NRGs) were iden-tified, and the relationship between DE-NRGs and the immune microenvironment in sepsis was analyzed. Methods: Machine learning algorithms were applied for screening hub genes related to necroptosis in the training cohort. CIBERSORT algorithms were employed for immune infiltration landscape analysis. Then, the diagnostic value of these hub genes was veri-fied by the ROC curve and nomogram. In addition, consensus clustering was applied to divide the sepsis patients into different subgroups, and quantitative real-time PCR was used to detect the mRNA levels of the hub genes between sepsis patients (SP) (n=30) and healthy controls (HC) (n=15). Finally, a multivariate prediction model based on heart rate, temperature, white blood count and 4 hub genes was established. Results: A total of 47 DE-NRGs were identified between SP and HC and 4 hub genes (BACH2, GATA3, LEF1, and BCL2) relevant to necroptosis were screened out via mul-tiple machine learning algorithms. The high diagnostic value of these hub genes was validated by the ROC curve and Nomogram model. Besides, the immune scores, corre-lation analysis and immune cell infiltrations suggested an immunosuppressive microen-vironment in sepsis. Septic patients were divided into 2 clusters based on the expres-sions of hub genes using consensus clustering, and the immune microenvironment landscapes and immune function between the 2 clusters were significantly different. The mRNA levels of the 4 hub genes significantly decreased in SP as compared with HC. The area under the curve (AUC) was better in the multivariate prediction model than in other indicators. Conclusions: This study indicated that these necroptosis hub genes might have great potential in prognosis prediction and personalized immunotherapy for sepsis. .