Molecular and immune landscape of melanoma: a risk stratification model for precision oncology
Abstract Background Melanoma is a highly aggressive skin cancer with significant heterogeneity in immune infiltration and clinical outcomes. Accurate risk stratification is essential for improving personalized treatment strategies. Methods This study utilized data from The Cancer Genome Atlas (TCGA)...
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Springer
2025-05-01
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| Series: | Discover Oncology |
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| Online Access: | https://doi.org/10.1007/s12672-025-02497-0 |
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| author | Miao Sun Junliang Wu |
| author_facet | Miao Sun Junliang Wu |
| author_sort | Miao Sun |
| collection | DOAJ |
| description | Abstract Background Melanoma is a highly aggressive skin cancer with significant heterogeneity in immune infiltration and clinical outcomes. Accurate risk stratification is essential for improving personalized treatment strategies. Methods This study utilized data from The Cancer Genome Atlas (TCGA) to explore immune-related gene expression in melanoma. Single-sample gene set enrichment analysis (ssGSEA) was employed to classify patients into high and low immune groups. Tumor microenvironment (TME) characteristics, including immune cell infiltration, HLA gene expression, and TME scores, were analyzed. Prognostic genes were identified using univariate and multivariate Cox regression analyses. A risk score model and nomogram were constructed, and gene set enrichment analysis (GSEA) and gene set variation analysis (GSVA) were conducted to explore relevant signaling pathways. Results ssGSEA-based classification revealed significant differences between high and low immune groups in terms of immune infiltration and HLA gene expression. The risk model incorporated immune-related genes such as GBP2, SEMA4D, and KIR2DL4, which demonstrated distinct tumor expression profiles and strong prognostic value. GSEA and GSVA analyses uncovered critical immune-related and oncogenic pathways linked to risk stratification. A predictive nomogram integrating molecular risk scores and clinical variables improved prognostic accuracy. Computational immune deconvolution highlighted CD8+ T cell infiltration as a key prognostic factor. To validate the functional role of candidate genes, KIR2DL4 was silenced in A375 melanoma cells using shRNA. Knockdown efficiency was confirmed by qRT-PCR. Functional assays revealed that KIR2DL4 silencing significantly reduced cell proliferation, as assessed by MTT assay, and impaired migratory capacity, as demonstrated by wound healing assay. These in vitro findings support the computational predictions and suggest that KIR2DL4 may play a tumor-promoting role in melanoma. Conclusion This study provides a robust immune-related prognostic model for melanoma. It underscores the value of immune gene expression and T cell infiltration, particularly CD8+ T cells, in predicting patient outcomes. The model facilitates personalized treatment decisions and advances precision oncology approaches in melanoma. The integration of transcriptomic analysis with experimental validation confirms the tumor-promoting role of KIR2DL4 and enhances the translational value of the model in guiding precision immunotherapy. |
| format | Article |
| id | doaj-art-2c3ef17c34d94897b731582500a0fe0d |
| institution | DOAJ |
| issn | 2730-6011 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Springer |
| record_format | Article |
| series | Discover Oncology |
| spelling | doaj-art-2c3ef17c34d94897b731582500a0fe0d2025-08-20T02:55:35ZengSpringerDiscover Oncology2730-60112025-05-0116111310.1007/s12672-025-02497-0Molecular and immune landscape of melanoma: a risk stratification model for precision oncologyMiao Sun0Junliang Wu1Operating Room, Department of Anesthesiology, West China Hospital, Sichuan University/West China School of Nursing, Sichuan UniversityDepartment of Plastic and Burn Surgery , West China School of Medicine, West China Hospital, Sichuan UniversityAbstract Background Melanoma is a highly aggressive skin cancer with significant heterogeneity in immune infiltration and clinical outcomes. Accurate risk stratification is essential for improving personalized treatment strategies. Methods This study utilized data from The Cancer Genome Atlas (TCGA) to explore immune-related gene expression in melanoma. Single-sample gene set enrichment analysis (ssGSEA) was employed to classify patients into high and low immune groups. Tumor microenvironment (TME) characteristics, including immune cell infiltration, HLA gene expression, and TME scores, were analyzed. Prognostic genes were identified using univariate and multivariate Cox regression analyses. A risk score model and nomogram were constructed, and gene set enrichment analysis (GSEA) and gene set variation analysis (GSVA) were conducted to explore relevant signaling pathways. Results ssGSEA-based classification revealed significant differences between high and low immune groups in terms of immune infiltration and HLA gene expression. The risk model incorporated immune-related genes such as GBP2, SEMA4D, and KIR2DL4, which demonstrated distinct tumor expression profiles and strong prognostic value. GSEA and GSVA analyses uncovered critical immune-related and oncogenic pathways linked to risk stratification. A predictive nomogram integrating molecular risk scores and clinical variables improved prognostic accuracy. Computational immune deconvolution highlighted CD8+ T cell infiltration as a key prognostic factor. To validate the functional role of candidate genes, KIR2DL4 was silenced in A375 melanoma cells using shRNA. Knockdown efficiency was confirmed by qRT-PCR. Functional assays revealed that KIR2DL4 silencing significantly reduced cell proliferation, as assessed by MTT assay, and impaired migratory capacity, as demonstrated by wound healing assay. These in vitro findings support the computational predictions and suggest that KIR2DL4 may play a tumor-promoting role in melanoma. Conclusion This study provides a robust immune-related prognostic model for melanoma. It underscores the value of immune gene expression and T cell infiltration, particularly CD8+ T cells, in predicting patient outcomes. The model facilitates personalized treatment decisions and advances precision oncology approaches in melanoma. The integration of transcriptomic analysis with experimental validation confirms the tumor-promoting role of KIR2DL4 and enhances the translational value of the model in guiding precision immunotherapy.https://doi.org/10.1007/s12672-025-02497-0Melanoma stratificationImmune infiltrationPrognostic biomarkersGene expression profilingTCGA data analysis |
| spellingShingle | Miao Sun Junliang Wu Molecular and immune landscape of melanoma: a risk stratification model for precision oncology Discover Oncology Melanoma stratification Immune infiltration Prognostic biomarkers Gene expression profiling TCGA data analysis |
| title | Molecular and immune landscape of melanoma: a risk stratification model for precision oncology |
| title_full | Molecular and immune landscape of melanoma: a risk stratification model for precision oncology |
| title_fullStr | Molecular and immune landscape of melanoma: a risk stratification model for precision oncology |
| title_full_unstemmed | Molecular and immune landscape of melanoma: a risk stratification model for precision oncology |
| title_short | Molecular and immune landscape of melanoma: a risk stratification model for precision oncology |
| title_sort | molecular and immune landscape of melanoma a risk stratification model for precision oncology |
| topic | Melanoma stratification Immune infiltration Prognostic biomarkers Gene expression profiling TCGA data analysis |
| url | https://doi.org/10.1007/s12672-025-02497-0 |
| work_keys_str_mv | AT miaosun molecularandimmunelandscapeofmelanomaariskstratificationmodelforprecisiononcology AT junliangwu molecularandimmunelandscapeofmelanomaariskstratificationmodelforprecisiononcology |