A cross-scale multimodal framework identifies clinically actionable immunotherapy biomarkers in melanoma through bulk to single-cell and spatial transcriptomics integration

Background Melanoma, a type of skin cancer that can spread to other parts of the body, currently lacks highly precise individualized treatment options. Methods We performed multi-omics integration on The Cancer Genome Atlas Skin Cutaneous Melanoma (TCGA-SKCM) cohort to identify melanoma molecular subtypes. The identified genes were validated in independent meta cohorts from GEO, followed by transcriptome-wide association study (TWAS) validation using Genotype-Tissue Expression (GTEx) and UK Biobank datasets. Additionally, we analyzed machine learning-driven signature (CMLS) development, tumor microenvironment characteristics, immunotherapy response, and potential therapeutic targets. Finally, single-cell and spatial transcriptomics provided further biological insights and the pathomechanisms. Result Our study identified two distinct molecular subtypes of SKCM using multimodal data integration with the MOVICS package: Cancer Subtype 1 (CS1) and CS2. CS2 was enriched in immune-suppressive pathways like WNT-β signaling with showing better prognosis, while CS1 exhibited higher activation of the PI3K pathway and DNA repair mechanisms with greater tumor invasiveness. TWAS analysis results combined the findings from TCGA-SKCM and the meta-cohort, identifying six significant prognostic-related genes (SPRGs). The CMLS prognostic model, based on SPRGs ( CAP2 , SELL , and LAPTM5 as risk factors and GZMA , FCER1G , and LYZ as protective factors), stratified patients into high-group (poorer survival) and low-risk groups. Single-cell and spatial transcriptomic analyses further validated CMLS prognostic results, highlighting distinct tumor microenvironment interactions and progression trajectories. Conclusion Identifications of molecular subtypes and CMLS represent a valuable tool for early prediction of patient prognosis and for screening potential candidates likely to benefit from immunotherapy, with broad implications for clinical practice foundation for personalized therapies