{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,23]],"date-time":"2026-09-23T11:44:52Z","timestamp":1790163892387,"version":"4.1.0"},"reference-count":25,"publisher":"Wiley","issue":"6","license":[{"start":{"date-parts":[[2024,6,17]],"date-time":"2024-06-17T00:00:00Z","timestamp":1718582400000},"content-version":"vor","delay-in-days":16,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["82073019"],"award-info":[{"award-number":["82073019"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Experimental Dermatology"],"published-print":{"date-parts":[[2024,6]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>This manuscript presents a comprehensive investigation into the role of lactate metabolism\u2010related genes as potential prognostic markers in skin cutaneous melanoma (SKCM). Bulk\u2010transcriptome data from The Cancer Genome Atlas (TCGA) and GSE19234, GSE22153, and GSE65904 cohorts from GEO database were processed and harmonized to mitigate batch effects. Lactate metabolism scores were assigned to individual cells using the \u2018AUCell\u2019 package. Weighted Co\u2010expression Network Analysis (WGCNA) was employed to identify gene modules correlated with lactate metabolism. Machine learning algorithms were applied to construct a prognostic model, and its performance was evaluated in multiple cohorts. Immune correlation, mutation analysis, and enrichment analysis were conducted to further characterize the prognostic model's biological implications. Finally, the function of key gene NDUFS7 was verified by cell experiments. Machine learning resulted in an optimal prognostic model, demonstrating significant prognostic value across various cohorts. In the different cohorts, the high\u2010risk group showed a poor prognosis. Immune analysis indicated differences in immune cell infiltration and checkpoint gene expression between risk groups. Mutation analysis identified genes with high mutation loads in SKCM. Enrichment analysis unveiled enriched pathways and biological processes in high\u2010risk SKCM patients. NDUFS7 was found to be a hub gene in the protein\u2013protein interaction network. After the expression of NDUFS7 was reduced by siRNA knockdown, CCK\u20108, colony formation, transwell and wound healing tests showed that the activity, proliferation and migration of A375 and WM115 cell lines were significantly decreased. This study offers insights into the prognostic significance of lactate metabolism\u2010related genes in SKCM.<\/jats:p>","DOI":"10.1111\/exd.15119","type":"journal-article","created":{"date-parts":[[2024,6,17]],"date-time":"2024-06-17T08:53:37Z","timestamp":1718614417000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Unravelling the metabolic landscape of cutaneous melanoma: Insights from single\u2010cell sequencing analysis and machine learning for prognostic assessment of lactate metabolism"],"prefix":"10.1111","volume":"33","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4992-498X","authenticated-orcid":false,"given":"Jiaheng","family":"Xie","sequence":"first","affiliation":[{"name":"Department of Plastic Surgery, Xiangya Hospital Central South University  Changsha 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