Development and validation of a cuproptosis-immune prognostic signature for risk stratification and personalized therapy in cutaneous melanoma

Skin cutaneous melanoma (SKCM) is a highly aggressive malignancy with rising global incidence and mortality. Despite advances in immunotherapy and targeted therapies, treatment resistance remains a challenge, necessitating novel prognostic biomarkers and therapeutic strategies. Cuproptosis, a copper-dependent form of regulated cell death, and immune-related pathways have emerged as critical players in tumor progression. However, their combined prognostic potential in SKCM remains unexplored. Here, we constructed a cuproptosis-immune-related gene signature to predict SKCM prognosis and guide therapy. Using the TCGA database, we identified 474 cuproptosis-related immune genes through Pearson correlation analysis. By integrating the GTEx database, differential expression analysis revealed that 194 of these genes were significantly dysregulated in SKCM. Univariate Cox and LASSO regression analyses established a 12-gene prognostic model (C3AR1, CCL8, CCR1, CTLA4, HLA-DRB1, IFIH1, IL2RA, IRF9, KIR2DL4, TLR1, TNFRSF21, XCL2), stratifying patients into high- and low-risk groups. The model demonstrated robust predictive accuracy in training and validation cohorts. High-risk patients exhibited poorer survival, reduced immune infiltration, suppressed checkpoint expression, and lower tumor mutational burden (TMB), suggesting an immunosuppressive microenvironment. Conversely, low-risk patients showed enhanced immune infiltration, higher TMB, and increased checkpoint-related gene expression, suggesting an immune-inflamed but functionally restrained phenotype with potential relevance to immune checkpoint blockade. Drug sensitivity analysis revealed high-risk patients may benefit more from targeted therapies. A nomogram integrating risk scores and clinical factors further improved prognostic prediction, with calibration curves demonstrating strong concordance between predicted and observed survival probabilities. Single-cell RNA sequencing illustrated the cellular distribution of model genes, and functional experiments demonstrated that XCL2 suppresses melanoma cell proliferation, migration, and invasion. This study develops and validates a cuproptosis-immune integrated prognostic signature for SKCM, providing a framework to link cuproptosis-associated biology with immune microenvironmental features, risk stratification, and potential therapeutic decision-making.

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Publication Details

Journal
Discover Oncology
Published
2026-06-09
DOI
https://doi.org/10.1007/s12672-026-05396-0
Primary Topic
Ferroptosis and cancer prognosis
Type
article
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article

Development and validation of a cuproptosis-immune prognostic signature for risk stratification and personalized therapy in cutaneous melanoma

Meiru Zhao, Xi Zhang, Junyan Zhang, M. Xiao et al.
Discover Oncology
Ferroptosis and cancer prognosis
article

Development and validation of a cuproptosis-immune prognostic signature for risk stratification and personalized therapy in cutaneous melanoma

Meiru Zhao, Xi Zhang, Junyan Zhang, M. Xiao, Huiping Wang, T Liu
article en

Abstract

Skin cutaneous melanoma (SKCM) is a highly aggressive malignancy with rising global incidence and mortality. Despite advances in immunotherapy and targeted therapies, treatment resistance remains a challenge, necessitating novel prognostic biomarkers and therapeutic strategies. Cuproptosis, a copper-dependent form of regulated cell death, and immune-related pathways have emerged as critical players in tumor progression. However, their combined prognostic potential in SKCM remains unexplored. Here, we constructed a cuproptosis-immune-related gene signature to predict SKCM prognosis and guide therapy. Using the TCGA database, we identified 474 cuproptosis-related immune genes through Pearson correlation analysis. By integrating the GTEx database, differential expression analysis revealed that 194 of these genes were significantly dysregulated in SKCM. Univariate Cox and LASSO regression analyses established a 12-gene prognostic model (C3AR1, CCL8, CCR1, CTLA4, HLA-DRB1, IFIH1, IL2RA, IRF9, KIR2DL4, TLR1, TNFRSF21, XCL2), stratifying patients into high- and low-risk groups. The model demonstrated robust predictive accuracy in training and validation cohorts. High-risk patients exhibited poorer survival, reduced immune infiltration, suppressed checkpoint expression, and lower tumor mutational burden (TMB), suggesting an immunosuppressive microenvironment. Conversely, low-risk patients showed enhanced immune infiltration, higher TMB, and increased checkpoint-related gene expression, suggesting an immune-inflamed but functionally restrained phenotype with potential relevance to immune checkpoint blockade. Drug sensitivity analysis revealed high-risk patients may benefit more from targeted therapies. A nomogram integrating risk scores and clinical factors further improved prognostic prediction, with calibration curves demonstrating strong concordance between predicted and observed survival probabilities. Single-cell RNA sequencing illustrated the cellular distribution of model genes, and functional experiments demonstrated that XCL2 suppresses melanoma cell proliferation, migration, and invasion. This study develops and validates a cuproptosis-immune integrated prognostic signature for SKCM, providing a framework to link cuproptosis-associated biology with immune microenvironmental features, risk stratification, and potential therapeutic decision-making.

Discover Oncology
Tianjin Medical University General Hospital (CN)
Openalex Percentile: Top 7%
Ferroptosis and cancer prognosis
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