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Oxidative stress‐driven metabolic reprogramming defines two subtypes of cutaneous melanoma

Year: 2026
Type: article
Abstract: The high heterogeneity of skin cutaneous melanoma (SKCM) remains a major obstacle to effective personalized treatment. Existing classification methods may not fully capture the molecular characteristics dictated by the extensive metabolic reprogramming, limiting treatment options. While existing classifications focus on driver mutations, they often overlook the metabolic plasticity due to changes in physicochemical conditions in the cancer microenvironments that facilitate therapeutic resistance. This study aimed to identify novel, metabolism‐centric molecular subtypes of SKCM samples to explore potential clinical vulnerabilities and investigate possible underlying mechanisms. We identified two distinct subtypes through unsupervised clustering based on multi‐omic data. The two subtypes were found to have distinct prognostic and biological drivers: the pro‐oxidative metabolic subtype (POMS) and the inflammation‐driven subtype (IMS). The POMS tends to have significantly worse survival than the IMS patients. Our integrative analyses support a computational prediction that POMS is associated with elevated oxidative‐stress‐related signatures, increased TMB, reduced immune‐related pathway activity, and hyperactive mTORC1 signaling, suggesting a stress‐adaptive metabolic state that warrants future experimental validation. The IMS relied on inflammatory signaling, such as the JAK‐STAT pathway. In silico predictions suggest that POMS exhibit increased sensitivity to Trametinib and Sorafenib, while IMS is sensitive to PLX4720, Rapamycin, Ruxolitinib, and Cisplatin. In retrospective public data, the classifier showed strong prognostic relevance beyond American Joint Committee on Cancer staging, while prospective validation on independent clinical samples is needed before clinical applications. Our study may inform future investigations into the treatment resistance of the disease.
Authors: Qikun Gao, Ziyi Liu, Ying Xu
Language: English
Open Access status: gold
Estimated APC paid: $3,000
OpenAlex ID: W7171327768
FWCI:
Cites: 54
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