Deciphering the Immunomodulatory Function of <scp>GSN</scp> <sup>+</sup> Inflammatory Cancer‐Associated Fibroblasts in Renal Cell Carcinoma Immunotherapy: Insights From Pan‐Cancer Single‐Cell Landscape and Spatial Transcriptomics Analysis

The heterogeneity of cancer-associated fibroblasts (CAFs) could affect the response to immune checkpoint inhibitor (ICI) therapy. However, limited studies have investigated the role of inflammatory CAFs (iCAFs) in ICI therapy using pan-cancer single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics sequencing (ST-seq) analysis. We performed pan-cancer scRNA-seq and ST-seq analyses to identify the subtype of GSN<sup>+</sup> iCAFs, exploring its spatial distribution characteristics in the context of ICI therapy. The pan-cancer scRNA-seq and bulk RNA-seq data are incorporated to develop the Caf.Sig model, which predicts ICI response based on CAF gene signatures and machine learning approaches. Comprehensive scRNA-seq analysis, along with in vivo and in vitro experiments, investigates the mechanisms by which GSN<sup>+</sup> iCAFs influence ICI efficacy. The Caf.Sig model demonstrates well performances in predicting ICI therapy response in pan-cancer patients. A higher proportion of GSN<sup>+</sup> iCAFs is observed in ICI non-responders compared to responders in the pan-cancer landscape and clear cell renal cell carcinoma (ccRCC). Using real-world immunotherapy data, the Caf.Sig model accurately predicts ICI response in pan-cancer, potentially linked to interactions between GSN<sup>+</sup> iCAFs and CD8<sup>+</sup> Tex cells. ST-seq analysis confirms that interactions and cellular distances between GSN<sup>+</sup> iCAFs and CD8<sup>+</sup> exhausted T (Tex) cells impact ICI efficacy. In a co-culture system of primary CAFs, primary tumour cells and CD8<sup>+</sup> T cells, downregulation of GSN on CAFs drives CD8<sup>+</sup> T cells towards a dysfunctional state in ccRCC. In a subcutaneously tumour-grafted mouse model, combining GSN overexpression with ICI treatment achieves optimal efficacy in ccRCC. Our study provides the Caf.Sig model as an outperforming approach for patient selection of ICI therapy, and advances our understanding of CAF biology and suggests potential therapeutic strategies for upregulating GSN in CAFs in cancer immunotherapy.

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Publication Details

Journal
Cell Proliferation
Published
2025-05-15
DOI
https://doi.org/10.1111/cpr.70062
Citations
3
Primary Topic
Cancer Immunotherapy and Biomarkers
Type
article
Field-Weighted Citation Impact
3.14

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article

Deciphering the Immunomodulatory Function of GSN + Inflammatory Cancer‐Associated Fibroblasts in Renal Cell Carcinoma Immunotherapy: Insights From Pan‐Cancer Single‐Cell Landscape and Spatial Transcriptomics Analysis

Mingjie Lin, Kangbo Huang, Liangmin Fu, Bai‐Ou Guan et al.
3 citations
Cell Proliferation
Cancer Immunotherapy and Biomarkers
3.14
article

Deciphering the Immunomodulatory Function of GSN + Inflammatory Cancer‐Associated Fibroblasts in Renal Cell Carcinoma Immunotherapy: Insights From Pan‐Cancer Single‐Cell Landscape and Spatial Transcriptomics Analysis

Mingjie Lin, Kangbo Huang, Liangmin Fu, Bai‐Ou Guan, Guannan Shu, Z. Deng, Haoqian Feng, Xinwei Zhou, Yihui Pan, Minyu Chen, Hansen Lin, Zhengkun Zhang, Wuyuan Liao, Anze Yu, Jinwei Chen, Zihao Feng, Tian Su, Shan Li, Yuhang Chen
article en
3 citations

Abstract

The heterogeneity of cancer-associated fibroblasts (CAFs) could affect the response to immune checkpoint inhibitor (ICI) therapy. However, limited studies have investigated the role of inflammatory CAFs (iCAFs) in ICI therapy using pan-cancer single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics sequencing (ST-seq) analysis. We performed pan-cancer scRNA-seq and ST-seq analyses to identify the subtype of GSN<sup>+</sup> iCAFs, exploring its spatial distribution characteristics in the context of ICI therapy. The pan-cancer scRNA-seq and bulk RNA-seq data are incorporated to develop the Caf.Sig model, which predicts ICI response based on CAF gene signatures and machine learning approaches. Comprehensive scRNA-seq analysis, along with in vivo and in vitro experiments, investigates the mechanisms by which GSN<sup>+</sup> iCAFs influence ICI efficacy. The Caf.Sig model demonstrates well performances in predicting ICI therapy response in pan-cancer patients. A higher proportion of GSN<sup>+</sup> iCAFs is observed in ICI non-responders compared to responders in the pan-cancer landscape and clear cell renal cell carcinoma (ccRCC). Using real-world immunotherapy data, the Caf.Sig model accurately predicts ICI response in pan-cancer, potentially linked to interactions between GSN<sup>+</sup> iCAFs and CD8<sup>+</sup> Tex cells. ST-seq analysis confirms that interactions and cellular distances between GSN<sup>+</sup> iCAFs and CD8<sup>+</sup> exhausted T (Tex) cells impact ICI efficacy. In a co-culture system of primary CAFs, primary tumour cells and CD8<sup>+</sup> T cells, downregulation of GSN on CAFs drives CD8<sup>+</sup> T cells towards a dysfunctional state in ccRCC. In a subcutaneously tumour-grafted mouse model, combining GSN overexpression with ICI treatment achieves optimal efficacy in ccRCC. Our study provides the Caf.Sig model as an outperforming approach for patient selection of ICI therapy, and advances our understanding of CAF biology and suggests potential therapeutic strategies for upregulating GSN in CAFs in cancer immunotherapy.

Cell ProliferationVol. 58(12)
Central South University (CN), Sun Yat-sen University (CN), Tianjin Medical University Cancer Institute and Hospital (CN), Soochow University (CN), The First Affiliated Hospital, Sun Yat-sen University (CN), Sun Yat-sen University Cancer Center (CN), Second Xiangya Hospital of Central South University (CN), Guangdong Provincial People's Hospital (CN), Children's Hospital of Chongqing Medical University (CN), Chongqing Medical University (CN), Guangzhou Medical University (CN)
National Natural Science Foundation of China, China Postdoctoral Science Foundation, Natural Science Foundation of Guangdong Province, Guangdong Medical Research Foundation, Natural Science Foundation of Jiangsu Province
Good health and well-being
Openalex Percentile: Top 8%
Cancer Immunotherapy and Biomarkers
3.14
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