Development of a novel risk model for stratifying skin cutaneous melanoma patients based on prognostic senescence-related genes

Abstract

Introduction: Skin cutaneous melanoma (SKCM) is a highly lethal skin carcinoma. Cellular senescence has a dual effect on tumor progression.

Objective: This study investigates whether senescence-related genes can guide patient stratification by examining their association with survival outcomes in SKCM.

Methods: Univariate and multivariate Cox regression analyses were performed to identify prognostic senescence-related genes. Based on these genes, samples were classified into two subtypes using consensus unsupervised clustering. Kaplan-Meier survival analysis was conducted, with statistical significance assessed via the log-rank test. Immune infiltration patterns were assessed using CIBERSORT. Finally, a prognostic risk model was constructed using Lasso regression following by multivariate Cox regression.

Results: Based on the prognostic senescence-related genes, samples were classified into two subtypes with distinct survival outcomes and immune profiles. Cluster 2, linked to improved survival and enriched in cytokine-cytokine receptor interactions, showed higher infiltration of activated natural killer (NK) cells, CD8+ T cells as well as CD4+ memory T cells, along with enhanced immune pathway activation compared to cluster 1. Subsequently, a risk model was constructed based on the identified key genes and validated using both internal and external datasets. Stratification of patients by the median risk score showed that the low-risk cohort had a significantly better prognosis, with a favorable immune microenvironment enriched in CD8+ T cells, anti-tumor M1 macrophages, and activated NK cells.

Conclusion: Senescence-related gene expression effectively stratified SKCM patients into subtypes with distinct survival outcomes and immune microenvironment profiles, offering new insights for the development of personalized medicine.

Keywords

Skin cutaneous melanoma / Cellular senescence / Immune microenvironment / Risk model / High-throughput RNA sequencing

Funding

This work was supported in part by General project of Chongqing Joint Fund of Science and Technology (Grant No. CSTB2024NSCQ-LMX0016) [Y.X.]; Chongqing Wanzhou PhD “through train” Research Project (Grant No. Wzstc20230402) [M.Y.].

Conflict of interest

Mingzhu Yin is an Editor in Chief of this journal but was not in any way involved in the editorial and peer-review process conducted for this paper, directly or indirectly. Separately, other authors declared that they have no known competing financial interests or personal relationships that could have influenced the work reported in this paper.

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