Unravelling tumour cell diversity and prognostic signatures in cutaneous melanoma through machine learning analysis.

ABSTRACT: Melanoma, a highly malignant tumour, presents significant challenges due to its cellular heterogeneity, yet research on this aspect in cutaneous melanoma remains limited. In this study, we utilized single-cell data from 92,521 cells to explore the tumour cell landscape. Through clustering analysis, we identified six distinct cell clusters and investigated their differentiation and metabolic heterogeneity using multi-omics approaches. Notably, cytotrace analysis and pseudotime trajectories revealed distinct stages of tumour cell differentiation, which have implications for patient survival. By leveraging markers from these clusters, we developed a tumour cell-specific machine learning model (TCM). This model not only predicts patient outcomes and responses to immunotherapy, but also disting

SUBMITTER: Cheng W 

PROVIDER: S-EPMC11272603 | biostudies-literature | 2024 Jul

REPOSITORIES: biostudies-literature