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<p>Metabolic reprogramming plays a critical role in the initiation and progression of skin cutaneous melanoma (SKCM). This study aims to construct a prognostic model based on metabolic-related genes (MRGs) to forecast patient outcomes and their response to immunotherapy. 10 machine learning algorithms within a cross-validation framework were utilized to compute prognostic risk scores based on MRGs, dividing SKCM patients into high- and low-risk groups. Further exploration included immune-related scores, immune infiltration levels, and oncological phenotype between these groups. The expression levels of six essential MRGs were assessed, and the effect of GALNT2 on proliferation and migration in SKCM cell lines was confirmed. This study has developed a new MRGs prognostic risk model that effectively predicts the survival of melanoma patients. The low-risk group exhibits higher immune scores and immune cell infiltration, which are beneficial for immunotherapy. In contrast, the high-risk group is positively correlated with the malignant phenotype of tumors, with increased MRG expression promoting tumor development. The study also identified six key genes, among which both the silencing and overexpression of GALNT2 significantly affect the proliferation and migration of melanoma cells. This study highlights the significance of MRGs in predicting patient survival and immunotherapy outcomes, providing insights for potential future targeted therapies.</p>

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Establishing and validating a new metabolic marker-driven prognosis signature for cutaneous melanoma

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年12月2日

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  • Chen Wang,
  • Jiajie Chen,
  • Xian Ding,
  • Xu Wang,
  • Xinyu Liang,
  • Xi Wen,
  • Shulin Yu,
  • Yu Gui,
  • Huabing Zhang,
  • Shengxiu Liu

摘要

Metabolic reprogramming plays a critical role in the initiation and progression of skin cutaneous melanoma (SKCM). This study aims to construct a prognostic model based on metabolic-related genes (MRGs) to forecast patient outcomes and their response to immunotherapy. 10 machine learning algorithms within a cross-validation framework were utilized to compute prognostic risk scores based on MRGs, dividing SKCM patients into high- and low-risk groups. Further exploration included immune-related scores, immune infiltration levels, and oncological phenotype between these groups. The expression levels of six essential MRGs were assessed, and the effect of GALNT2 on proliferation and migration in SKCM cell lines was confirmed. This study has developed a new MRGs prognostic risk model that effectively predicts the survival of melanoma patients. The low-risk group exhibits higher immune scores and immune cell infiltration, which are beneficial for immunotherapy. In contrast, the high-risk group is positively correlated with the malignant phenotype of tumors, with increased MRG expression promoting tumor development. The study also identified six key genes, among which both the silencing and overexpression of GALNT2 significantly affect the proliferation and migration of melanoma cells. This study highlights the significance of MRGs in predicting patient survival and immunotherapy outcomes, providing insights for potential future targeted therapies.