Combination of machine learning and protein‑protein interaction network established one ATM‑DPP4‑TXN ferroptotic diagnostic model with experimental validation.

Ferroptosis and lethal sepsis are interlinked, although this association remains largely unknown to clinical panels. Sepsis is characterized by dysfunction of the inflammatory microenvironment. Most septic biomarkers lack independent validation, and a comprehensive diagnosis comprising biomarker assessment combined with clinical evaluation may improve sepsis management. Targeting ferroptosis regulators may offer new hope for uncovering the inflammatory machinery and for developing novel diagnostic methods for sepsis, and bioinformatics analyses are a valuable tool to investigate this further. In the present study, septic datasets were obtained from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) were subsequently introduced in enrichment analyses and intersected with ferroptotic genes for acquiring ferroptosis‑related DEGs (FRDEGs). A protein‑protein interaction network (PPIN) was then constructed to retain hub‑FRDEGs, and this was imported into three machine learning algorithms. A nomogram based on the logistic regression model was subsequently built and validated