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已发表论文
能量代谢相关基因 PEA15 在脓毒症中的诊断价值和分子机制评估
Authors Yu X, Liu C, Jia L, Wan B, Feng J, Wu Y, Tang J, Jia Y , Liu H, Luo S, Li Q, Kong G, Li P
Received 29 July 2025
Accepted for publication 24 January 2026
Published 3 February 2026 Volume 2026:19 556793
DOI https://doi.org/10.2147/JIR.S556793
Checked for plagiarism Yes
Review by Single anonymous peer review
Peer reviewer comments 2
Editor who approved publication: Dr Anh Ngo
Xiao Yu,1,2,* Congrui Liu,1,* Libin Jia,1,* Bingjie Wan,2 Jun Feng,2 Yonghong Wu,3 Jin Tang,1 Yachun Jia,2 Hongwei Liu,2 Siyu Luo,2 Qiao Li,2 Guangyao Kong,2 Ping Li1
1Department of General Practice, The Second Affiliated Hospital of Xi’an Jiaotong University, Xi’an, Shaanxi, People’s Republic of China; 2National & Local Joint Engineering Research Center of Biodiagnosis and Biotherapy, Precision Medical Institute, The Second Affiliated Hospital of Xi’an Jiaotong University, Xi’an, Shaanxi, People’s Republic of China; 3College of Medical Technology, Xi’an Medical University, Xi’an, Shaanxi, People’s Republic of China
*These authors contributed equally to this work
Correspondence: Ping Li, Department of General Practice, The Second Affiliated Hospital of Xi’an Jiaotong University, Xi’an, Shaanxi, 710004, People’s Republic of China, Email lipingjun@163.com Guangyao Kong, National & Local Joint Engineering Research Center of Biodiagnosis and Biotherapy, Precision Medical Institute, The Second Affiliated Hospital of Xi’an Jiaotong University, Xi’an, Shaanxi, 710004, People’s Republic of China, Email konggy@xjtu.edu.cn
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.
Keywords: sepsis, energy metabolism, key gene, immune, single-cell RNA-sequencing
1Department of General Practice, The Second Affiliated Hospital of Xi’an Jiaotong University, Xi’an, Shaanxi, People’s Republic of China; 2National & Local Joint Engineering Research Center of Biodiagnosis and Biotherapy, Precision Medical Institute, The Second Affiliated Hospital of Xi’an Jiaotong University, Xi’an, Shaanxi, People’s Republic of China; 3College of Medical Technology, Xi’an Medical University, Xi’an, Shaanxi, People’s Republic of China
*These authors contributed equally to this work
Correspondence: Ping Li, Department of General Practice, The Second Affiliated Hospital of Xi’an Jiaotong University, Xi’an, Shaanxi, 710004, People’s Republic of China, Email lipingjun@163.com Guangyao Kong, National & Local Joint Engineering Research Center of Biodiagnosis and Biotherapy, Precision Medical Institute, The Second Affiliated Hospital of Xi’an Jiaotong University, Xi’an, Shaanxi, 710004, People’s Republic of China, Email konggy@xjtu.edu.cn
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.
Keywords: sepsis, energy metabolism, key gene, immune, single-cell RNA-sequencing