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Background <p>Skin cutaneous melanoma (SKCM) is a highly aggressive and deadly subtype of skin cancer. Lack of efficient biomarkers for prognosis has limited the improvement of survival outcome for patients with SKCM.</p> Methods <p>In this study, we obtained RNA-seq data from TCGA and GTEx databases, followed by identification of differential expressed genes, univariate Cox regression, and LASSO regression to identify prognostic SASP-related genes in the TCGA datasets and constructed a prognostic risk-scoring model.</p> Results <p>The establishment of prognostic model was based on the expression levels of 14 SASP-related genes, including <i>ASPRV1</i>, <i>ICAM1</i>, <i>IL2RA</i>, <i>ABCC2</i>, <i>HLA-B</i>, <i>TPMT</i>, <i>ATM</i>, <i>CD59</i>, <i>KIR2DL4</i>, <i>CTLA4</i>, <i>ITGB3</i>, <i>FOXM1</i>, <i>NOX4</i>, and <i>TRIM21</i>. Patients with melanoma who were in the high-risk group had a shorter overall survival (OS), indicating that the model served as an independent prognostic index. Furthermore, we found that the risk score was potentially linked to immune scores, estimate score, immune cell infiltration level, and immunotherapy efficacy.</p> Conclusions <p>This study presented a new prognostic model for assessing therapy in melanoma patients, providing a fresh perspective for combating melanoma.</p>

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Construction of a prognostic risk-scoring model based on SASP-related genes in patients with skin cutaneous melanoma

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年8月27日

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  • Ming-Feng Li,
  • Jing Du,
  • Gang Wang,
  • Wei Feng,
  • Ju-Gao Chen,
  • Chao Zhang

摘要

Background

Skin cutaneous melanoma (SKCM) is a highly aggressive and deadly subtype of skin cancer. Lack of efficient biomarkers for prognosis has limited the improvement of survival outcome for patients with SKCM.

Methods

In this study, we obtained RNA-seq data from TCGA and GTEx databases, followed by identification of differential expressed genes, univariate Cox regression, and LASSO regression to identify prognostic SASP-related genes in the TCGA datasets and constructed a prognostic risk-scoring model.

Results

The establishment of prognostic model was based on the expression levels of 14 SASP-related genes, including ASPRV1, ICAM1, IL2RA, ABCC2, HLA-B, TPMT, ATM, CD59, KIR2DL4, CTLA4, ITGB3, FOXM1, NOX4, and TRIM21. Patients with melanoma who were in the high-risk group had a shorter overall survival (OS), indicating that the model served as an independent prognostic index. Furthermore, we found that the risk score was potentially linked to immune scores, estimate score, immune cell infiltration level, and immunotherapy efficacy.

Conclusions

This study presented a new prognostic model for assessing therapy in melanoma patients, providing a fresh perspective for combating melanoma.