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Background <p>Mitotic catastrophe (MC) is a well-recognized endogenous mechanism of tumor cell death, characterized as a delayed cell death process associated with aberrant mitosis. However, its prognostic significance in the context of intratumoral heterogeneity in esophageal squamous cell carcinoma (ESCC) remains largely unexplored.</p> Methods <p>We performed an in-depth analysis of single-cell RNA sequencing (scRNA-seq) data from ESCC obtained from the Gene Expression Omnibus (GEO) database. MC scores for individual cells were calculated using the AddModuleScore function, and T cell specific gene modules were identified via the high-dimensional weighted gene co-expression network analysis (hdWGCNA) framework. To further elucidate the developmental trajectories and intercellular interactions of T cells, pseudotime analysis and cell-cell communication inference were conducted. A prognostic risk model was then constructed using three machine learning algorithms combined with multivariate Cox regression analysis. Following risk stratification, we performed immune infiltration profiling, drug sensitivity analysis, and molecular docking to comprehensively assess the functional implications of the risk model in ESCC. Based on preliminary results from quantitative Real-time PCR (qRT-PCR) and Western blotting (WB), we selected the hub gene SLF2 for functional validation using wound healing, Cell Counting Kit-8 (CCK-8) assay, Transwell, and colony formation assays.</p> Results <p>Based on T cell mitotic catastrophe associated genes (MCAGs) and utilizing machine learning algorithms, we established a robust prognostic risk model for ESCC. The model demonstrated excellent stratification capability in predicting patient outcomes and effectively revealed the heterogeneity of the tumor immune microenvironment (TIME) and drug sensitivity. Furthermore, functional experiments confirmed that knockdown of the hub gene SLF2 significantly inhibited the migration, invasion, and proliferation of ESCC cells.</p> Conclusion <p>The prognostic model based on MCAGs we developed serves as an effective tool for predicting outcomes in ESCC.T cell-specific MCAGs drive intratumoral heterogeneity in ESCC, serving as potential prognostic biomarkers and therapeutic targets.</p>

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Integrative single-cell and bulk transcriptomic analysis reveals the landscape of T cell mitotic catastrophe associated genes in esophageal squamous cell carcinoma

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年8月29日

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  • Shuang Li,
  • Zheng Tao,
  • Nan Wang,
  • Yazhou Liu,
  • Kai Xie,
  • Haitao Ma

摘要

Background

Mitotic catastrophe (MC) is a well-recognized endogenous mechanism of tumor cell death, characterized as a delayed cell death process associated with aberrant mitosis. However, its prognostic significance in the context of intratumoral heterogeneity in esophageal squamous cell carcinoma (ESCC) remains largely unexplored.

Methods

We performed an in-depth analysis of single-cell RNA sequencing (scRNA-seq) data from ESCC obtained from the Gene Expression Omnibus (GEO) database. MC scores for individual cells were calculated using the AddModuleScore function, and T cell specific gene modules were identified via the high-dimensional weighted gene co-expression network analysis (hdWGCNA) framework. To further elucidate the developmental trajectories and intercellular interactions of T cells, pseudotime analysis and cell-cell communication inference were conducted. A prognostic risk model was then constructed using three machine learning algorithms combined with multivariate Cox regression analysis. Following risk stratification, we performed immune infiltration profiling, drug sensitivity analysis, and molecular docking to comprehensively assess the functional implications of the risk model in ESCC. Based on preliminary results from quantitative Real-time PCR (qRT-PCR) and Western blotting (WB), we selected the hub gene SLF2 for functional validation using wound healing, Cell Counting Kit-8 (CCK-8) assay, Transwell, and colony formation assays.

Results

Based on T cell mitotic catastrophe associated genes (MCAGs) and utilizing machine learning algorithms, we established a robust prognostic risk model for ESCC. The model demonstrated excellent stratification capability in predicting patient outcomes and effectively revealed the heterogeneity of the tumor immune microenvironment (TIME) and drug sensitivity. Furthermore, functional experiments confirmed that knockdown of the hub gene SLF2 significantly inhibited the migration, invasion, and proliferation of ESCC cells.

Conclusion

The prognostic model based on MCAGs we developed serves as an effective tool for predicting outcomes in ESCC.T cell-specific MCAGs drive intratumoral heterogeneity in ESCC, serving as potential prognostic biomarkers and therapeutic targets.