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已发表论文
整合机器学习分析程序性细胞死亡通路发现心房颤动的新型诊断生物标志物
Authors Peng H, Xia Z, Zhao Y, Xie D
Received 23 September 2025
Accepted for publication 27 November 2025
Published 30 December 2025 Volume 2025:18 Pages 18247—18266
DOI https://doi.org/10.2147/JIR.S568171
Checked for plagiarism Yes
Review by Single anonymous peer review
Peer reviewer comments 3
Editor who approved publication: Prof. Dr. Chengming Fan
Hongbo Peng,* Zhenwei Xia,* Yangyang Zhao, Di Xie
Department of Cardiology, Central Hospital of Dalian University of Technology, Dalian, Liaoning, People’s Republic of China
*These authors contributed equally to this work
Correspondence: Di Xie, Department of Cardiology, Central Hospital of Dalian University of Technology, 826 Xinan Road, Dalian, Liaoning, People’s Republic of China, Email xd_carrot@163.com
Purpose: Atrial fibrillation (AF) is a leading cause of stroke, heart failure, and mortality, yet the molecular mechanisms remain incompletely defined.
Patients and Methods: We integrated bulk transcriptomes from GEO with weighted gene co-expression network analysis, consensus clustering, and a 12-algorithm machine-learning pipeline (66 model combinations) to map programmed cell death (PCD) pathways and pinpoint diagnostic genes. Immune infiltration was profiled by CIBERSORT, xCell, and ssGSEA. Hub-gene expression was validated in an HL-1 atrial pacing model and in peripheral blood mononuclear cells (PBMCs) from patients with persistent AF.
Results: Four hub genes—SGPL1, NPC2, PTGDS, and RCAN1—were identified and incorporated into a nomogram and a PCD-based risk score (PCDscore). The nomogram showed robust discrimination in the training cohort and two independent validation datasets. Patients with a high PCDscore exhibited markedly increased immune-cell infiltration and dysregulated immune modulators, with macrophages consistently enriched across algorithms. qRT-PCR confirmed up-regulation of SGPL1, NPC2, and RCAN1 and down-regulation of PTGDS in AF cell models; NPC2 and SGPL1 were further elevated in PBMCs from AF patients.
Conclusion: Our integrative framework reveals PCD-linked remodeling in AF and nominates SGPL1, NPC2, PTGDS, and RCAN1 as candidate diagnostic biomarkers, providing a PCD-based nomogram and risk score that may inform patient stratification and hypothesis-generating targeted interventions.
Keywords: cardiac arrhythmias, immune remodeling, macrophage infiltration, molecular subtyping, apoptosis, diagnostic nomogram
Department of Cardiology, Central Hospital of Dalian University of Technology, Dalian, Liaoning, People’s Republic of China
*These authors contributed equally to this work
Correspondence: Di Xie, Department of Cardiology, Central Hospital of Dalian University of Technology, 826 Xinan Road, Dalian, Liaoning, People’s Republic of China, Email xd_carrot@163.com
Purpose: Atrial fibrillation (AF) is a leading cause of stroke, heart failure, and mortality, yet the molecular mechanisms remain incompletely defined.
Patients and Methods: We integrated bulk transcriptomes from GEO with weighted gene co-expression network analysis, consensus clustering, and a 12-algorithm machine-learning pipeline (66 model combinations) to map programmed cell death (PCD) pathways and pinpoint diagnostic genes. Immune infiltration was profiled by CIBERSORT, xCell, and ssGSEA. Hub-gene expression was validated in an HL-1 atrial pacing model and in peripheral blood mononuclear cells (PBMCs) from patients with persistent AF.
Results: Four hub genes—SGPL1, NPC2, PTGDS, and RCAN1—were identified and incorporated into a nomogram and a PCD-based risk score (PCDscore). The nomogram showed robust discrimination in the training cohort and two independent validation datasets. Patients with a high PCDscore exhibited markedly increased immune-cell infiltration and dysregulated immune modulators, with macrophages consistently enriched across algorithms. qRT-PCR confirmed up-regulation of SGPL1, NPC2, and RCAN1 and down-regulation of PTGDS in AF cell models; NPC2 and SGPL1 were further elevated in PBMCs from AF patients.
Conclusion: Our integrative framework reveals PCD-linked remodeling in AF and nominates SGPL1, NPC2, PTGDS, and RCAN1 as candidate diagnostic biomarkers, providing a PCD-based nomogram and risk score that may inform patient stratification and hypothesis-generating targeted interventions.
Keywords: cardiac arrhythmias, immune remodeling, macrophage infiltration, molecular subtyping, apoptosis, diagnostic nomogram