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<p>Melanoma is a highly invasive malignancy with poor prognoses in advanced stages. Developing a risk model that can accurately assess prognosis and guide personalized treatment is crucial for improving the clinical management of melanoma. This study aims to develop and validate an immune-based prognostic risk model for melanoma through comprehensive bioinformatics analysis. We collected transcriptomic data from multiple public databases and identified 9 immune features significantly associated with prognosis using single-sample Gene Set Enrichment Analysis (ssGSEA) and Cox regression. These features were utilized to construct the risk model, which was subsequently validated using relevant bulk transcriptomic datasets and single-cell transcriptomic datasets from the GEO database, encompassing diverse patient populations and sample types. The model effectively stratified patients into high-risk and low-risk groups with distinct survival outcomes. Further analysis revealed significant associations between the risk model and genomic heterogeneity indicators, such as tumor mutational burden (TMB), loss of heterozygosity (LOH), and immune checkpoint gene expression. The model robustness was confirmed using single-cell transcriptomic data, highlighting key genes with potential therapeutic relevance. Our findings provide a reliable prognostic tool and novel insights for personalized melanoma treatment, emphasizing the need for further clinical validation.</p>

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Development and validation of an immune signature-based risk model for prognostic assessment in melanoma

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年3月17日

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  • Rui Tao,
  • Jingjing Ruan,
  • Xuejie Chen,
  • Boshi Pang,
  • Sicheng Li,
  • Shengzhi Zhou,
  • Sis Aghayants,
  • Zeqi Shi,
  • Zhanyong Zhu

摘要

Melanoma is a highly invasive malignancy with poor prognoses in advanced stages. Developing a risk model that can accurately assess prognosis and guide personalized treatment is crucial for improving the clinical management of melanoma. This study aims to develop and validate an immune-based prognostic risk model for melanoma through comprehensive bioinformatics analysis. We collected transcriptomic data from multiple public databases and identified 9 immune features significantly associated with prognosis using single-sample Gene Set Enrichment Analysis (ssGSEA) and Cox regression. These features were utilized to construct the risk model, which was subsequently validated using relevant bulk transcriptomic datasets and single-cell transcriptomic datasets from the GEO database, encompassing diverse patient populations and sample types. The model effectively stratified patients into high-risk and low-risk groups with distinct survival outcomes. Further analysis revealed significant associations between the risk model and genomic heterogeneity indicators, such as tumor mutational burden (TMB), loss of heterozygosity (LOH), and immune checkpoint gene expression. The model robustness was confirmed using single-cell transcriptomic data, highlighting key genes with potential therapeutic relevance. Our findings provide a reliable prognostic tool and novel insights for personalized melanoma treatment, emphasizing the need for further clinical validation.