Scholarlink

Background <p>Sepsis is a severe syndrome characterized by programmed cell death and organ dysfunction. The association between PANoptosis and mitochondrial dysfunction in sepsis remains unclear. This study aimed to investigate their relationship and identify potential biomarkers for diagnosis and treatment.</p> Methods <p>Transcriptomic and single-cell RNA sequencing (scRNA-seq) data from the GEO database were analyzed to identify differentially expressed genes. Machine learning algorithms and scRNA-seq were used to identify biomarkers and investigate their expression levels at the single-cell level, followed by experimental validation using reverse transcription followed by polymerase chain reaction (qRT-PCR) and Western blot.</p> Results <p>Four biomarkers—<i>BCL2</i>, <i>C1QBP</i>, <i>AIFM1</i>, and <i>BCL2L1</i>—were identified and incorporated into a nomogram with high predictive accuracy (AUC: 0.983). scRNA-seq analysis revealed differential expression in macrophages, and experiments confirmed decreased <i>AIFM1</i>, <i>BCL2,</i> and <i>C1QBP</i> levels and increased <i>BCL2L1</i> levels in septic mice.</p> Conclusions <p>This study highlights the role of the PANoptosis–mitochondrial dysfunction in sepsis and identifies biomarkers with significant diagnostic and therapeutic potential.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Single-cell and transcriptomic analyses identify PANoptosis–mitochondria-associated biomarkers with diagnostic and therapeutic potential in sepsis

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年5月25日

查看全文
  • Xu Zhang,
  • Qiao Ye,
  • Yue Shi,
  • Shuangrui Wang,
  • Bo Ye,
  • Weiliang Zhao

摘要

Background

Sepsis is a severe syndrome characterized by programmed cell death and organ dysfunction. The association between PANoptosis and mitochondrial dysfunction in sepsis remains unclear. This study aimed to investigate their relationship and identify potential biomarkers for diagnosis and treatment.

Methods

Transcriptomic and single-cell RNA sequencing (scRNA-seq) data from the GEO database were analyzed to identify differentially expressed genes. Machine learning algorithms and scRNA-seq were used to identify biomarkers and investigate their expression levels at the single-cell level, followed by experimental validation using reverse transcription followed by polymerase chain reaction (qRT-PCR) and Western blot.

Results

Four biomarkers—BCL2, C1QBP, AIFM1, and BCL2L1—were identified and incorporated into a nomogram with high predictive accuracy (AUC: 0.983). scRNA-seq analysis revealed differential expression in macrophages, and experiments confirmed decreased AIFM1, BCL2, and C1QBP levels and increased BCL2L1 levels in septic mice.

Conclusions

This study highlights the role of the PANoptosis–mitochondrial dysfunction in sepsis and identifies biomarkers with significant diagnostic and therapeutic potential.