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<p>Esophageal squamous cell carcinoma (ESCC) is an aggressive malignancy with limited therapies and poor prognosis. While exhausted T cell (Tex) heterogeneity under chronic antigen stimulation is recognized, the role of Tex subsets in shaping the tumor microenvironment (TME) and influencing ESCC outcomes remains unclear. We integrated single-cell RNA sequencing, TCR sequencing, microarray, and bulk RNA sequencing to dissect Tex cells. We identified a novel proliferative Tex (prolif Tex) subset across discovery and validation cohorts, with high infiltration correlating significantly with an improved patient survival. Pseudotime trajectories suggested prolif Tex originated from Tex cells, while TCR sequencing revealed clonal expansion and shared receptor repertoires between Tex and prolif Tex. Neoadjuvant chemo-immunotherapy reduced prolif Tex proportions and differentiation potential in ESCC samples. A machine learning-derived prognostic model was developed using prolif Tex subset-specific genes and validated via qRT-PCR using in-house ESCC samples. Experimental validation confirmed differential expression of candidate genes in tumors versus normal tissues, with high <i>ESCO2</i> expression linked to prolonged survival. Our findings unveil prolif Tex cells as a novel TME subset in ESCC, where its abundance predicts favorable outcomes. The prolif Tex-based prognostic model demonstrates strong prognostic value and validated hub genes offer potential biomarkers and therapeutic targets. Our findings underscore the potential of prolif Tex cells as a biomarker and therapeutic target in ESCC.</p>

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Discovery and validation of proliferative exhausted T cells as a favorable prognostic biomarker in esophageal squamous cell carcinoma

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年11月24日

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  • Guanyang Li,
  • Choon Yu Lam,
  • Rui Chen,
  • Xinran Wang,
  • Hao Zhang,
  • Fangqiu Fu,
  • Hanlin Zhou

摘要

Esophageal squamous cell carcinoma (ESCC) is an aggressive malignancy with limited therapies and poor prognosis. While exhausted T cell (Tex) heterogeneity under chronic antigen stimulation is recognized, the role of Tex subsets in shaping the tumor microenvironment (TME) and influencing ESCC outcomes remains unclear. We integrated single-cell RNA sequencing, TCR sequencing, microarray, and bulk RNA sequencing to dissect Tex cells. We identified a novel proliferative Tex (prolif Tex) subset across discovery and validation cohorts, with high infiltration correlating significantly with an improved patient survival. Pseudotime trajectories suggested prolif Tex originated from Tex cells, while TCR sequencing revealed clonal expansion and shared receptor repertoires between Tex and prolif Tex. Neoadjuvant chemo-immunotherapy reduced prolif Tex proportions and differentiation potential in ESCC samples. A machine learning-derived prognostic model was developed using prolif Tex subset-specific genes and validated via qRT-PCR using in-house ESCC samples. Experimental validation confirmed differential expression of candidate genes in tumors versus normal tissues, with high ESCO2 expression linked to prolonged survival. Our findings unveil prolif Tex cells as a novel TME subset in ESCC, where its abundance predicts favorable outcomes. The prolif Tex-based prognostic model demonstrates strong prognostic value and validated hub genes offer potential biomarkers and therapeutic targets. Our findings underscore the potential of prolif Tex cells as a biomarker and therapeutic target in ESCC.