Integrative Machine Learning-Based Framework For the Identification of Biomarkers of Immunogenic Cell Death and Comprehensive Pharmacological Screening Strategy in Sepsis
DOI:
https://doi.org/10.71373/j50m9h35Keywords:
Immunogenic cell death, Machine Learning, Sepsis, Pharmacological Screening StrategyAbstract
This study employs machine learning algorithms to systematically screen key biomarkers closely linked to the Immunogenic Cell Death (ICD) process and deeply analyze their regulatory mechanisms in the sepsis immune microenvironment. It further explores traditional Chinese medicine (TCM) monomers and small-molecule drugs with regulatory potential via high-throughput targeted gene screening.
By analyzing the intersection of 5445 differentially expressed genes (DEGs) and 34 ICD-related genes, 20 ICD-associated DEGs were identified. The prediction model revealed the gimBoost + RF algorithm combination as optimal, screening 10 key genes; ROC curve showed CD8A, IFNGR1, ENTPD1 had the strongest predictive ability.
Specifically, CD8A was downregulated, positively correlated with CD8 T cells but negatively with neutrophils. IFNGR1 and ENTPD1 were upregulated, with opposite correlations. A ceRNA regulatory network targeting CD8A and ENTPD1 was constructed to explore upstream non-coding mechanisms.
Stigmasterol, an active component in Corn Silk, Ginseng, and Scutellariae Radix, bound stably to the three key genes. This study paves a new path for clinical translation of sepsis precision treatment.
