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<p>Soft tissue sarcomas (STS), including rhabdomyosarcoma (RMS), exhibit significant heterogeneity and limited responsiveness to immune checkpoint blockade (ICB). Unsupervised tumor immune phenotype based on multi-omics expression profiling of STS has been less studied. To reveal the tumor immune phenotype of STS and identify promising therapeutic targets, multi-omics expression profiling across various subtypes of STS was investigated. Here, we established a novel molecular classifier based on immune cell subsets related to TGFβ1 and IFNγ to identify distinct immune phenotypes with higher or lower cytotoxic contents. Immune-high clusters demonstrated enriched immune cell infiltration, elevated IFNγ-related signatures, and favorable clinical outcomes. In contrast, immune-low clusters were enriched for immunosuppressive cell types and exhibited poor survival. CHEK1 emerged as a key node associated with immunosuppressive phenotypes and was significantly overexpressed in immune-low tumors. In situ analysis of independent validation cohorts revealed the significant correlation between CHEK1 and tumor-infiltrating immune cells. Collectively, our findings establish a novel risk assessment strategy for RMS and STS patients, and highlight the potential of CHEK1 as a promising therapeutic target in combination with immune checkpoint inhibitor therapy.</p>

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Integrative bioinformatics analysis and experimental validation identify CHEK1 as an unfavorable prognostic biomarker related to immunosuppressive phenotypes in soft tissue sarcomas

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年8月1日

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  • Chao Rong,
  • Yun Liu,
  • Fang Xiang,
  • Xin Zhao,
  • Jinjin Zhang,
  • Zuorun Xiao,
  • Jinsha Wang,
  • Lin Chen,
  • Zhiqi Guo,
  • Ziyu Zhang,
  • Jingnan An,
  • Jing Shen,
  • Jochen Hess,
  • Xiaodong Yuan,
  • Qiong Zhang,
  • Shouli Wang

摘要

Soft tissue sarcomas (STS), including rhabdomyosarcoma (RMS), exhibit significant heterogeneity and limited responsiveness to immune checkpoint blockade (ICB). Unsupervised tumor immune phenotype based on multi-omics expression profiling of STS has been less studied. To reveal the tumor immune phenotype of STS and identify promising therapeutic targets, multi-omics expression profiling across various subtypes of STS was investigated. Here, we established a novel molecular classifier based on immune cell subsets related to TGFβ1 and IFNγ to identify distinct immune phenotypes with higher or lower cytotoxic contents. Immune-high clusters demonstrated enriched immune cell infiltration, elevated IFNγ-related signatures, and favorable clinical outcomes. In contrast, immune-low clusters were enriched for immunosuppressive cell types and exhibited poor survival. CHEK1 emerged as a key node associated with immunosuppressive phenotypes and was significantly overexpressed in immune-low tumors. In situ analysis of independent validation cohorts revealed the significant correlation between CHEK1 and tumor-infiltrating immune cells. Collectively, our findings establish a novel risk assessment strategy for RMS and STS patients, and highlight the potential of CHEK1 as a promising therapeutic target in combination with immune checkpoint inhibitor therapy.