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This study used single\u2010cell RNA sequencing (scRNA\u2010Seq) technology to compare the differences between metastatic and primary tumour cells. By manually annotating cell types, significant disparities in cell communication patterns and functional pathways between the two groups were identified. Combined with transcriptomic data, differential gene analysis was performed to screen out a core gene set associated with tumour metastasis. To achieve accurate prediction of tumour metastasis, this study innovatively constructed a binary classification algorithm (PSO\u2013SVM) integrating particle swarm optimisation (PSO) and support vector machines (SVMs). This model optimises SVM parameters via the PSO algorithm, addressing the limitations of traditional machine learning models such as insufficient accuracy and poor generalization ability in tumour metastasis prediction. Verified by comparison with mainstream machine learning methods, the PSO\u2013SVM model exhibited superior classification performance and successfully identified five key metastasis\u2010related genes: SFN, S100A8, KLF5, ARL4D and TINCR. Furthermore, the expression differences of these genes in the metastatic group were verified at the single\u2010cell level, clarifying their regulatory roles in different cell types and states. Through an innovative analytical strategy integrating single\u2010cell and transcriptomic data, this study elucidated the core molecular mechanisms of SKCM metastasis and key regulatory pathways in the tumour microenvironment, providing potential biomarkers and therapeutic targets for the early diagnosis and targeted treatment of SKCM metastasis. This PSO\u2013SVM\u2013integrated analysis method also offers new insights for research on metastasis mechanisms of other cancers.<\/jats:p>","DOI":"10.1049\/syb2.70061","type":"journal-article","created":{"date-parts":[[2026,4,10]],"date-time":"2026-04-10T18:26:03Z","timestamp":1775845563000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Utilising Machine Learning and Single\u2010Cell Analysis to Uncover SKCM Metastasis\u2013Related Genes"],"prefix":"10.1049","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-4488-6276","authenticated-orcid":false,"given":"Zhiwei","family":"Liao","sequence":"first","affiliation":[{"name":"Guangdong Pharmaceutical University Guangzhou University Town  Guangzhou China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Weiming","family":"Chen","sequence":"additional","affiliation":[{"name":"Guangdong Pharmaceutical University Guangzhou University Town  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