Utilising Machine Learning and Single-Cell Analysis to Uncover SKCM Metastasis-Related Genes.

The high mortality rate of metastatic cutaneous melanoma (SKCM) remains a major challenge in clinical treatment. This study used single-cell RNA sequencing (scRNA-Seq) 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-SVM) 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-SVM model exhibited superior classification performance and successfully identified five key metastasis-related genes: SFN, S100A8, KLF5, ARL4D and TINCR. Furthermore, the expression differences of these genes in the metastatic group were verified at the single-cell level, clarifying their regulatory roles in different cell types and states. Through an innovative analytical strategy integrating single-cell 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-SVM-integrated analysis method also offers new insights for research on metastasis mechanisms of other cancers.