Efficient Machine Learning-based Gene Selection Exploiting Immune-related Biomarkers and Recursive Feature Elimination for Sepsis Diagnosis

Differential expression gene (DEG) analysis of transcriptomic data allows for a comprehensive examination of the regulation in gene expression profiles related to specific biological states. The result of this analysis typically consists of an extensive record of genes that display varying levels of expression among two or more groups. A portion of these genes with altered expression could potentially function as candidate biomarkers, chosen through either existing biological insights or data-driven techniques. In diagnosing sepsis, a life-threatening health problem, our work proposes a novel approach using immune-related gene data to identify the optimal gene combination as signature biomarkers to improve the diagnosis performance. Our proposed method involves sequential gene selection procedures, including the DEG analysis and the machine learning-based importance assessment, and a Recursive Feature Elimination (RFE) process supported by Principal Component Analysis (PCA). The selected gene combination, which consists of twelve immune-related genes, shows remarkable cross-validation results with an accuracy of 99.35%, AUC score of 99.56%, Sensitivity and a Specificity of 99.44% and 90.00%, respectively. Besides, the proposed 12 gene markers combined with the XGBoost algorithm were also tested in three individual cohorts with appropriately significant results, demonstrating the effectiveness of our developed method in different cohorts and the reliability of the proposed gene selection procedure.