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Objectives <p>Cancer-associated fibroblasts (CAFs) play a pivotal role in Gastric cancer (GC) progression and immune modulation. This study aimed to identify CAF subtypes using single-cell analysis and evaluate their prognostic and therapeutic relevance in GC.</p> Methods <p>CAF gene sets were derived from 13 single-cell datasets and quantified via ssGSEA in bulk transcriptomic cohorts (TCGA and GEO). Consensus clustering defined CAF-based subtypes. Immune infiltration was evaluated using CIBERSORT, xCell, MCPcounter, and ESTIMATE. Immunotherapy response was predicted using TIDE and ImmuCellAI. Chemotherapeutic sensitivity was assessed via PRISM, CTRP, and GDSC databases. Hub genes were identified by WGCNA, and a prognostic model was constructed and validated in external cohorts and at the single-cell level.</p> Results <p>Two CAF subtypes, FA_H and FA_L, were identified. FA_H was associated with poor prognosis, higher M2 macrophage infiltration, and immunosuppressive pathways, while FA_L correlated with improved survival and stronger predicted response to immune checkpoint inhibitors. Dasatinib was predicted as a potential therapeutic agent specifically for FA_H subtype. A five-gene prognostic model (COL1A2, NDN, SPARC, VCAN, TCEAL7) showed consistent predictive performance across datasets. Functional validation confirmed upregulation of TCEAL7 in CAFs and its role in promoting GC cell invasion.</p> Conclusion <p>Single-cell-based CAF subtyping defines clinically relevant heterogeneity in GC. The FA_H subtype may serve as both a prognostic biomarker and therapeutic target, particularly for dasatinib-based or immunomodulatory strategies.</p>

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Single cell analysis reveals cancer associated fibroblast subtypes as prognostic markers and therapeutic targets in gastric cancer

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年12月28日

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  • Dazhou Li,
  • Guihui Tong,
  • Wenqing Huang

摘要

Objectives

Cancer-associated fibroblasts (CAFs) play a pivotal role in Gastric cancer (GC) progression and immune modulation. This study aimed to identify CAF subtypes using single-cell analysis and evaluate their prognostic and therapeutic relevance in GC.

Methods

CAF gene sets were derived from 13 single-cell datasets and quantified via ssGSEA in bulk transcriptomic cohorts (TCGA and GEO). Consensus clustering defined CAF-based subtypes. Immune infiltration was evaluated using CIBERSORT, xCell, MCPcounter, and ESTIMATE. Immunotherapy response was predicted using TIDE and ImmuCellAI. Chemotherapeutic sensitivity was assessed via PRISM, CTRP, and GDSC databases. Hub genes were identified by WGCNA, and a prognostic model was constructed and validated in external cohorts and at the single-cell level.

Results

Two CAF subtypes, FA_H and FA_L, were identified. FA_H was associated with poor prognosis, higher M2 macrophage infiltration, and immunosuppressive pathways, while FA_L correlated with improved survival and stronger predicted response to immune checkpoint inhibitors. Dasatinib was predicted as a potential therapeutic agent specifically for FA_H subtype. A five-gene prognostic model (COL1A2, NDN, SPARC, VCAN, TCEAL7) showed consistent predictive performance across datasets. Functional validation confirmed upregulation of TCEAL7 in CAFs and its role in promoting GC cell invasion.

Conclusion

Single-cell-based CAF subtyping defines clinically relevant heterogeneity in GC. The FA_H subtype may serve as both a prognostic biomarker and therapeutic target, particularly for dasatinib-based or immunomodulatory strategies.