Integrating cuproptosis- and ferroptosis-related gene signatures to predict prognosis, immunotherapy response, and drug sensitivity in patients with skin cutaneous melanoma.
Skin cutaneous melanoma (SKCM) is a highly aggressive malignancy originating from melanocytes, with a continuously rising global incidence. Developing strategies for early prevention and precise treatment remains a major challenge in oncology. Notably, advances in immunotherapy have brought new hope to SKCM patients. Increasing evidence suggests that various forms of regulated cell death, particularly cuproptosis and ferroptosis, can modulate the tumor microenvironment (TME) by inducing the death of both tumor and immune cells, thereby influencing the efficacy of immunotherapy. Consequently, there is a critical need to establish methods for early diagnosis and to develop reliable prognostic models prognostic models based on immune-related biomarkers.
We integrated RNA sequencing data and corresponding clinical information from SKCM patients obtained from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) databases. Using single-cell transcriptomic data from the GSE72056 dataset, we analyzed the expression patterns of cuproptosis-ferroptosis-related genes (CFRGs) in SKCM and their enrichment in immune cell subsets. Key CFRG features were screened using machine learning algorithms to construct a prognostic risk-scoring model, which demonstrated robust predictive performance across multiple independent cohorts. Furthermore, we explored the associations between core CFRGs and patient survival outcomes, immunotherapy response, and drug sensitivity in SKCM.
We identified 10 key genes that were significantly associated with SKCM survival and successfully constructed a machine learning-based prognostic prediction model. This model showed strong predictive performance and demonstrated superior accuracy compared with existing prognostic models, as supported by cross-cohort and cross-cancer validation. Four key genes (IFNG, PTPN6, SLC38A1, and SOCS1) were further identified through association analyses with clinical phenotypes and showed significant correlations with clinical characteristics. Distinct immune cell infiltration patterns were observed between high- and low-risk groups stratified by these genes, indicating marked heterogeneity within the TME. This heterogeneity may directly influence patient responses to immunotherapy. Additionally, molecular docking analyses identified several potential therapeutic compounds, among which selumetinib demonstrated strong binding affinity to the target proteins IFNG, PTPN6, and SOCS1, suggesting a potential therapeutic strategy for advanced SKCM.
IFNG, PTPN6, SLC38A1, and SOCS1 may serve as potential biomarkers of poor prognosis in SKCM patients. These genes demonstrate predictive value for immunotherapy response and drug sensitivity, particularly indicating susceptibility to selumetinib treatment, and therefore show substantial potential for clinical translation.
We integrated RNA sequencing data and corresponding clinical information from SKCM patients obtained from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) databases. Using single-cell transcriptomic data from the GSE72056 dataset, we analyzed the expression patterns of cuproptosis-ferroptosis-related genes (CFRGs) in SKCM and their enrichment in immune cell subsets. Key CFRG features were screened using machine learning algorithms to construct a prognostic risk-scoring model, which demonstrated robust predictive performance across multiple independent cohorts. Furthermore, we explored the associations between core CFRGs and patient survival outcomes, immunotherapy response, and drug sensitivity in SKCM.
We identified 10 key genes that were significantly associated with SKCM survival and successfully constructed a machine learning-based prognostic prediction model. This model showed strong predictive performance and demonstrated superior accuracy compared with existing prognostic models, as supported by cross-cohort and cross-cancer validation. Four key genes (IFNG, PTPN6, SLC38A1, and SOCS1) were further identified through association analyses with clinical phenotypes and showed significant correlations with clinical characteristics. Distinct immune cell infiltration patterns were observed between high- and low-risk groups stratified by these genes, indicating marked heterogeneity within the TME. This heterogeneity may directly influence patient responses to immunotherapy. Additionally, molecular docking analyses identified several potential therapeutic compounds, among which selumetinib demonstrated strong binding affinity to the target proteins IFNG, PTPN6, and SOCS1, suggesting a potential therapeutic strategy for advanced SKCM.
IFNG, PTPN6, SLC38A1, and SOCS1 may serve as potential biomarkers of poor prognosis in SKCM patients. These genes demonstrate predictive value for immunotherapy response and drug sensitivity, particularly indicating susceptibility to selumetinib treatment, and therefore show substantial potential for clinical translation.