Cancer‐Associated Fibroblast Signature Can Predict Prognosis and Therapeutic Responses in Skin Cutaneous Melanoma

Skin cutaneous melanoma (SKCM) is a lethal skin cancer with a poor prognosis and limited response to immunotherapy. Cancer-associated fibroblasts (CAFs) are key contributors to tumor progression, therapy resistance, and immunosuppression. In this study, mRNA sequencing and clinical data from SKCM samples were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases to evaluate the prognostic significance, therapeutic implications, and potential for enhancing immunotherapy through targeting CAFs in SKCM. A CAF-related risk model comprising nine genes was developed, revealing that patients classified as low-risk exhibited superior survival outcomes and increased sensitivity to immunotherapy. Spearman correlation analysis identified significant associations between the risk score and the sensitivity to 40 drugs, as well as resistance to 17 drugs. Additionally, CAFs were categorized into three distinct subgroups in SKCM, with antigen-presenting CAFs (apCAFs) notably suppressing the infiltration of anti-tumor immune cells and strongly correlating with poor prognosis. In summary, the CAF-related risk model offers a robust prognostic tool for SKCM, capable of predicting both survival outcomes and therapeutic sensitivity. Moreover, the pivotal role of apCAFs within the immune microenvironment suggests that targeting these cells may enhance the efficacy of immunotherapy.

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Publication Details

Journal
Pigment Cell & Melanoma Research
Published
2025-02-26
DOI
https://doi.org/10.1111/pcmr.70005
Citations
2
Primary Topic
Cancer Immunotherapy and Biomarkers
Type
article
Field-Weighted Citation Impact
2.09
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article

Cancer‐Associated Fibroblast Signature Can Predict Prognosis and Therapeutic Responses in Skin Cutaneous Melanoma

Weihuan Shao, Tongxin Ge, Ai Zhuang, Shengfang Ge et al.
2 citations
Pigment Cell & Melanoma Research
Cancer Immunotherapy and Biomarkers
2.09
article

Cancer‐Associated Fibroblast Signature Can Predict Prognosis and Therapeutic Responses in Skin Cutaneous Melanoma

Weihuan Shao, Tongxin Ge, Ai Zhuang, Shengfang Ge, Ludi Yang, Yue Zheng, Renbing Jia
article en
2 citations

Abstract

Skin cutaneous melanoma (SKCM) is a lethal skin cancer with a poor prognosis and limited response to immunotherapy. Cancer-associated fibroblasts (CAFs) are key contributors to tumor progression, therapy resistance, and immunosuppression. In this study, mRNA sequencing and clinical data from SKCM samples were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases to evaluate the prognostic significance, therapeutic implications, and potential for enhancing immunotherapy through targeting CAFs in SKCM. A CAF-related risk model comprising nine genes was developed, revealing that patients classified as low-risk exhibited superior survival outcomes and increased sensitivity to immunotherapy. Spearman correlation analysis identified significant associations between the risk score and the sensitivity to 40 drugs, as well as resistance to 17 drugs. Additionally, CAFs were categorized into three distinct subgroups in SKCM, with antigen-presenting CAFs (apCAFs) notably suppressing the infiltration of anti-tumor immune cells and strongly correlating with poor prognosis. In summary, the CAF-related risk model offers a robust prognostic tool for SKCM, capable of predicting both survival outcomes and therapeutic sensitivity. Moreover, the pivotal role of apCAFs within the immune microenvironment suggests that targeting these cells may enhance the efficacy of immunotherapy.

Pigment Cell & Melanoma ResearchVol. 38(2)
Shanghai Jiao Tong University (CN), Shanghai Ninth People's Hospital (CN), Shanghai Institute of Hematology (CN)
Openalex Percentile: Top 14%
Cancer Immunotherapy and Biomarkers
2.09
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Cancer‐Associated Fibroblast Signature Can Predict Prognosis and Therapeutic Responses in Skin Cutaneous Melanoma — Weihuan Shao, Tongxin Ge, et al. · Pigment Cell & Melanoma Research (2025) | TGRS Research Map | TGRS