Bioinformatics combined with machine learning for potential biomarker screening and immune infiltration analysis in neonatal sepsis

Objective: Apply bioinformatics combined with machine learning algorithms to screen potential biomarkers of neonatal sepsis (NS) and explore the correlation between biomarkers and immune cells. Methods: Expression profiling data containing NS samples and control samples were downloaded from the Gene Expression Omnibus (GEO) database. First, the differentially expressed genes (DEGs) were found. Then gene set enrichment analysis (GSEA) was carried out to reveal the significantly enriched pathways, and the core genes within these pathways were aggregated. Subsequently, weighted gene co-expression network analysis (WGCNA) was used to identify the gene modules significantly correlated with NS, from which the key genes were screened. The feature genes were selected by the use of three machine learning algorithms: least absolute shrinkage and selection operator (LASSO) regression, support vector machine recursive feature elimination (SVM-RFE), and random forest cross-validation (RF-CV), and then the potential biomarkers of NS were identified by logistic regression analyses. The dataset was randomly split into training and test sets. A logistic regression model with biomarkers was constructed using the training set, and its diagnostic value was evaluated separately on both the training and test sets. In addition, immune infiltration analysis was performed using immune cell deconvolution algorithm CIBERSORT to explore the correlation between biomarkers and immune cells. Results: T cells. Conclusion: T cells. These conclusions should be regarded as exploratory biomarker discovery rather than diagnostic readiness.