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Identification and validation of TME-related signatures to predict prognosis and response to anti-tumor therapies in skin cutaneous melanoma

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Abstract

The tumor microenvironment (TME) dynamically regulates cancer progression and affects clinical outcomes. This study aimed to identify molecular subtypes and construct a prognostic risk model based on TME-related signatures in skin cutaneous melanoma (SKCM) patients. We categorized SKCM patients based on transcriptome data of SKCM from The Cancer Genome Atlas (TCGA) database and 29 TME-related gene signatures. Differentially expressed genes were identified using univariate Cox regression and Lasso regression analysis, which were used for risk model construction. The robustness of this model was validated in independent external cohorts. Genetic landscape alterations, immune characteristics, and responsiveness to immunotherapy/chemotherapy were evaluated. Three TME-related subtypes were identified, and subtype C3 exhibited the most favorable prognosis, had enriched immune-related pathways, and possessed more infiltration of T_cells_CD8, T_cells_CD4_memory_activated, and Macrophages_M1 but a lower TumorPurity, whereas Macrophages_M2 were increased in subtype C1 and subtype C2. Subtype C1 was more sensitive to Cisplatin, subtype C2 was more sensitive to Temozolomide, and subtype C3 was more sensitive to Paclitaxel; 8 TME-related genes (NOTCH3, HEYL, ZNF703, ABCC2, PAEP, CCL8, HAPLN3, and HPDL) were screened for risk model construction. High-risk patients had dismal prognosis with good prediction performance. Moreover, low-risk patients were more sensitive to Paclitaxel and Temozolomide, whereas high-risk patients were more sensitive to Cisplatin. This risk model had robustness in predicting prognosis in SKCM patients. The results facilitate the understanding of TME-related genes in SKCM and provide a TME-related genes-based predictive model in prognosis and direction of personalized options for SKCM patients.

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Data availability

The data supporting the conclusions of this article can be obtained in The Cancer Genome Atlas (TCGA, https://portal.gdc.cancer.gov/) database and the Gene-Expression Omnibus (GEO, https://www.ncbi.nlm.nih.gov/geo/) database.

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Funding

This study was funded by Xiang’an Hospital of Xiamen University and the Guidance in Medical and Health Program of Xiamen, China (3502Z20224ZD1148).

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Authors and Affiliations

  1. Department of Burns and Plastic & Wound Repair Surgery, Xiang’an Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, 361100, China

    Wenqin Lian

  2. Department of General Surgery, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, 200437, China

    Xiao Zheng

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  1. Wenqin Lian
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  2. Xiao Zheng
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All authors wrote, revised, and approved the manuscript. All authors read and approved the final manuscript.

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Correspondence to Xiao Zheng.

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The study was conducted in compliance with the ethical principles of the Declaration of Helsinki and its later amendments. The Ethics Committee of Xiang’an Hospital of Xiamen University approved our study protocol (approval no. XAHLL2022046).

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The authors declare no competing interests.

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Lian, W., Zheng, X. Identification and validation of TME-related signatures to predict prognosis and response to anti-tumor therapies in skin cutaneous melanoma. Funct Integr Genomics 23, 153 (2023). https://doi.org/10.1007/s10142-023-01051-x

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