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已发表论文
脓毒症中程序性细胞死亡模式的多维表征用于预后分层和治疗见解
Received 24 July 2025
Accepted for publication 7 November 2025
Published 15 November 2025 Volume 2025:14 Pages 1313—1331
DOI https://doi.org/10.2147/ITT.S555950
Checked for plagiarism Yes
Review by Single anonymous peer review
Peer reviewer comments 3
Editor who approved publication: Dr Sarah Wheeler
Chen Zhou,1 Yunmeng Bai2
1Department of Laboratory Medicine, The Eighth Affiliated Hospital, Sun Yat-Sen University, Shenzhen, 518033, People’s Republic of China; 2School of Medicine, Southern University of Science and Technology, Shenzhen, 518055, People’s Republic of China
Correspondence: Yunmeng Bai, Email gantenyeah@163.com
Background: Sepsis is a complex and heterogeneous syndrome characterized by dysregulated immune responses and multiple forms of programmed cell death (PCD). Comprehensive understanding of the PCD landscape may provide insights into prognosis and therapeutic targets, whereas its role in sepsis is not well-explored.
Methods: Using the microarray dataset for sepsis (GSE65682), we systematically profiled 14 PCD patterns in sepsis and stratified patients into molecular subtypes with distinct immune landscapes and clinical outcomes. PCD-related prognostic signature was developed and validated across multiple cohorts. Single-cell and multi-organ transcriptomic analyses were conducted to elucidate cellular heterogeneity and temporal dynamics. Molecular docking was used to explore interactions between active compounds of Simiao Yongan Decoction (SMYAD) and key PCD-related proteins.
Results: Two clusters with differential transcriptional programs and immune infiltration patterns were identified, in which Cluster 1 showed poorer prognosis. We then developed a seven-gene signature (ELANE, CTSG, MPO, CAMP, TFRC, IL1B, CASP5) that effectively stratified patients by survival outcomes, with robust predictive performance across independent datasets. Neutrophils, monocytes, plasma, and dendritic cells were major mediators of PCD-associated immune dysregulation, in which neutrophils showing the strongest response. Temporal transcriptomics revealed peak expression of prognostic genes in bone marrow and peripheral blood within three days post-onset, suggesting an early therapeutic window. Finally, molecular docking indicated that SMYAD compounds may target PCD proteins (MPO, ELANE, IL1B) and modulate immune responses.
Conclusion: This study delineates the multi-dimensional role of PCD in sepsis, establishes a reliable prognostic model with strong predictive value, and highlights SMYAD as a potential multi-target therapy. These findings provide new avenues for risk stratification and suggest the promise of integrating PCD biology with adjunctive immunomodulatory strategies.
Keywords: sepsis, programmed cell death, machine learning, prognostic model, Simiao Yongan decoction
1Department of Laboratory Medicine, The Eighth Affiliated Hospital, Sun Yat-Sen University, Shenzhen, 518033, People’s Republic of China; 2School of Medicine, Southern University of Science and Technology, Shenzhen, 518055, People’s Republic of China
Correspondence: Yunmeng Bai, Email gantenyeah@163.com
Background: Sepsis is a complex and heterogeneous syndrome characterized by dysregulated immune responses and multiple forms of programmed cell death (PCD). Comprehensive understanding of the PCD landscape may provide insights into prognosis and therapeutic targets, whereas its role in sepsis is not well-explored.
Methods: Using the microarray dataset for sepsis (GSE65682), we systematically profiled 14 PCD patterns in sepsis and stratified patients into molecular subtypes with distinct immune landscapes and clinical outcomes. PCD-related prognostic signature was developed and validated across multiple cohorts. Single-cell and multi-organ transcriptomic analyses were conducted to elucidate cellular heterogeneity and temporal dynamics. Molecular docking was used to explore interactions between active compounds of Simiao Yongan Decoction (SMYAD) and key PCD-related proteins.
Results: Two clusters with differential transcriptional programs and immune infiltration patterns were identified, in which Cluster 1 showed poorer prognosis. We then developed a seven-gene signature (ELANE, CTSG, MPO, CAMP, TFRC, IL1B, CASP5) that effectively stratified patients by survival outcomes, with robust predictive performance across independent datasets. Neutrophils, monocytes, plasma, and dendritic cells were major mediators of PCD-associated immune dysregulation, in which neutrophils showing the strongest response. Temporal transcriptomics revealed peak expression of prognostic genes in bone marrow and peripheral blood within three days post-onset, suggesting an early therapeutic window. Finally, molecular docking indicated that SMYAD compounds may target PCD proteins (MPO, ELANE, IL1B) and modulate immune responses.
Conclusion: This study delineates the multi-dimensional role of PCD in sepsis, establishes a reliable prognostic model with strong predictive value, and highlights SMYAD as a potential multi-target therapy. These findings provide new avenues for risk stratification and suggest the promise of integrating PCD biology with adjunctive immunomodulatory strategies.
Keywords: sepsis, programmed cell death, machine learning, prognostic model, Simiao Yongan decoction