Abstract
PURPOSE: Sepsis, a life-threatening condition with high mortality, is closely linked to energy metabolism (EM) and immune-inflammatory responses. However, the precise mechanisms remain incompletely understood. This research aims to identity EM-related genes (EMRGs) in sepsis and examine the diagnostic potential and molecular mechanisms through machine learning and single-cell RNA sequencing (scRNA-seq). METHODS: This study utilized the GSE65682 and GSE95233 datasets from the Gene Expression Omnibus (GEO) for analysis. Kaplan-Meier (KM) survival analysis and receiver operating characteristic (ROC) diagnostic analysis were applied to identify key EM-related genes. A sepsis model with overexpression of key genes was developed, and RNA sequencing (RNA-Seq) was employed to identify associated genes. Additionally, scRNA-seq was conducted to examine cell type distributions and gene expression profiles in sepsis. RESULTS: ATM and PEA15 were identified as critical genes. Overexpression of PEA15 alleviated septic symptoms. In sepsis, alterations in immune cell infiltration, particularly T follicular helper cells, were correlated with key gene expression. Core genes (RSAD2, IFI44, MX1, IFIT3, and ISG15), closely associated with the key genes, were also identified. Single-cell analysis further delineated the cell type profiles and core gene expression patterns in sepsis. From a translational perspective, PEA15 addresses a critical gap in sepsis management. Its strong prognostic and diagnostic performance, validated through KM and ROC analyses, positions it as a promising biomarker for early sepsis detection-essential for improving patient outcomes through timely intervention. CONCLUSION: PEA15 and its associated core genes represent potential therapeutic targets: modulating PEA15 expression or targeting the underlying molecular network may help restore immune balance and reduce septic damage, offering a novel approach for targeted therapeutic strategies.