Combining bioinformatics and machine learning to analyze and validate sepsis-related cell senescence genes and potential drugs.

Abstract

Sepsis is a life-threatening organ malfunction induced by the host's abnormal reaction to infection. Sepsis can induce cellular senescence, thereby exacerbating tissue damage and organ dysfunction. However, the key biomarkers of cellular senescence and the corresponding targeted therapeutics in sepsis remain unknown. This study identified eight differentially expressed senescence-related genes, including TXN, CDKN1C, UTP6, BCL11B, SMAD3, ITPKB, PRPF19, and BCL2, through bioinformatics analysis and machine learning. These hub genes had good diagnostic performance for sepsis. Six hub genes showed consistent trends in the validation set and experimental samples with those in the training set. Immunoinfiltration analysis showed that eosinophils, macrophages M1, macrophages M2, NK cells activated, NK cells resting, T cells CD8, and Tregs were substantially linked with all hub genes. A large number of targeted compounds or drugs were obtained from the DSigDB database based on hub genes. These drugs primarily interacted with CDKN1C, BCL2, and SMAD3. The binding energies of fenofibrate with these target proteins were less than -5.0 kcal/mol. In both