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<p>Sepsis is an infection-induced systemic inflammatory response syndrome. T cell remodeling and senescence are linked to sepsis, so identifying T cell-related genes (TCRGs) and senescence-related genes (SRGs) as biomarkers is crucial for elucidating mechanisms, diagnosis, and targeted therapy. TCRGs were derived from single-cell sequencing data. Biomarkers were screened via differential expression analysis, machine learning, and expression analysis of public transcriptome data. Molecular mechanisms were explored through artificial neural network (ANN), GSEA, immune infiltration analysis, and drug prediction, with RT-qPCR validation in clinical samples. PATZ1, SIN3B, BLK, and MTHFD2 were identified. MTHFD2 was upregulated in sepsis, while the other three were downregulated (P &lt; 0.001); MTHFD2 showed no significant difference in validation (P &gt; 0.05). The ANN had high prediction accuracy. These genes were enriched in phosphatidylinositol signaling, hematopoietic cell lineage, and DNA replication. Immune infiltration analysis revealed correlations between the biomarkers and immune cells (e.g., PATZ1 with CD8 T cells/neutrophils). Emetine, latamoxef, and dihydroergotamine bound stably to the biomarkers. PATZ1, SIN3B, BLK, and MTHFD2, as T cell and senescence-related biomarkers in sepsis, offered valuable insights into sepsis pathogenesis and targeted therapy.</p>

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Integrative analysis of transcriptome and single-cell sequencing combined with experimental validation identifies biomarkers associated with T cell and senescence in sepsis

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年2月3日

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  • Kairui Yang,
  • Yunlong Hu,
  • Chunhui Ma,
  • Jianing Wang,
  • Yang Wang,
  • Xuejie Han,
  • Jiafeng Lv,
  • Xiaolei Zhang,
  • Yunliang Cui

摘要

Sepsis is an infection-induced systemic inflammatory response syndrome. T cell remodeling and senescence are linked to sepsis, so identifying T cell-related genes (TCRGs) and senescence-related genes (SRGs) as biomarkers is crucial for elucidating mechanisms, diagnosis, and targeted therapy. TCRGs were derived from single-cell sequencing data. Biomarkers were screened via differential expression analysis, machine learning, and expression analysis of public transcriptome data. Molecular mechanisms were explored through artificial neural network (ANN), GSEA, immune infiltration analysis, and drug prediction, with RT-qPCR validation in clinical samples. PATZ1, SIN3B, BLK, and MTHFD2 were identified. MTHFD2 was upregulated in sepsis, while the other three were downregulated (P < 0.001); MTHFD2 showed no significant difference in validation (P > 0.05). The ANN had high prediction accuracy. These genes were enriched in phosphatidylinositol signaling, hematopoietic cell lineage, and DNA replication. Immune infiltration analysis revealed correlations between the biomarkers and immune cells (e.g., PATZ1 with CD8 T cells/neutrophils). Emetine, latamoxef, and dihydroergotamine bound stably to the biomarkers. PATZ1, SIN3B, BLK, and MTHFD2, as T cell and senescence-related biomarkers in sepsis, offered valuable insights into sepsis pathogenesis and targeted therapy.