Sepsis is a life-threatening disease due to a dysregulated host response to infection, with an unknown regulatory mechanism for prognostic necroptosis-related genes (NRGs). Using GEO datasets GSE65682 and GSE134347, we identified six NRG biomarkers (ATRX, TSC1, CD40, BACH2, BCL2, and LEF1) with survival and diagnostic significance through Kaplan-Meier (KM) and receiver operating characteristic (ROC) analyses. Afterward, the ingenuity pathway analysis (IPA) highlighted enrichment in hepatic fibrosis pathways and BEX2 protein. Moreover, we examined their regulatory targets and functional links with necroptotic signaling molecules via miRDB, TargetScan, Network analyst, and GeneMANIA. The molecular regulatory network displayed that hsa-miR-5195-3p and hsa-miR-145-5p regulated ATRX, BACH2, and CD40, while YY1 showed strong connectivity, concurrently controlling LEF1, ATRX, BCL2, BACH2, and CD40. CD40 exhibited similar expression patterns to RIPK3 and MLKL, and LEF1 was functionally associated with MLKL. Additionally, DrugBank analysis identified paclitaxel, docetaxel, and rasagiline as potential BCL2-targeting sepsis treatments. Finally, real-time quantitative PCR confirmed ATRX, TSC1, and LEF1 downregulation in sepsis samples, contrasting CD40’s increased expression in CTL samples. In conclusion, ATRX, TSC1, CD40, BACH2, BCL2, and LEF1 may be critical regulatory targets of necroptosis in sepsis, providing a basis for further necroptosis-related studies in sepsis.

Sepsis is a life-threatening disease due to a dysregulated host response to infection, with an unknown regulatory mechanism for prognostic necroptosis-related genes (NRGs). Using GEO datasets GSE65682 and GSE134347, we identified six NRG biomarkers (ATRX, TSC1, CD40, BACH2, BCL2, and LEF1) with survival and diagnostic significance through Kaplan-Meier (KM) and ROC analyses. The ingenuity pathway analysis (IPA) highlighted enrichment in hepatic fibrosis pathways and BEX2 protein. We examined their regulatory targets and functional links with necroptotic signaling molecules via miRDB, TargetScan, Network analyst, and GeneMANIA. Hsa-miR-5195-3p and hsa-miR-145-5p regulated ATRX, BACH2, and CD40, while TF YY1 showed strong connectivity, concurrently controlling LEF1, ATRX, BCL2, BACH2, and CD40. CD40 exhibited similar expression patterns to RIPK3 and MLKL, and LEF1 was functionally associated with MLKL. Additionally, DrugBank analysis identified Paclitaxel, Docetaxel, and Rasagiline as potential BCL2-targeting sepsis treatments. Real-Time Quantitative PCR confirmed ATRX, TSC1, and LEF1 down-regulation in sepsis samples, contrasting CD40's increased expression in case samples. Variations in BACH2 and BCL2 expression between disease and normal samples may result from sample differences. In conclusion, ATRX, TSC1, CD40, BACH2, BCL2, and LEF1 may be critical regulatory targets of necroptosis in sepsis, providing a basis for further necroptosis-related studies in sepsis.

Purpose: Sepsis, a life-threatening condition with high mortality, is closely linked to energy metabolism (EM) and immuneinflammatory responses. However, the precise mechanisms remain incompletely understood. This research aims to identity EMrelated 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.