Combining multi-omics analysis with machine learning to uncover novel molecular subtypes, prognostic markers, and insights into immunotherapy for melanoma.

BACKGROUND

Melanoma (SKCM) is an extremely aggressive form of cancer, characterized by high mortality rates, frequent metastasis, and limited treatment options. Our study aims to identify key target genes and enhance the diagnostic accuracy of melanoma prognosis by employing multi-omics analysis and machine learning techniques, ultimately leading to the development of novel therapeutic strategies.

METHODS

We obtained and processed transcriptomic data, including RNA expression profiles, methylation microarray data, gene mutation data, and clinical information, from the TCGA dataset using multi-omics analysis and machine learning techniques. We comprehensively evaluated the molecular subtypes of melanoma, the characteristics of the tumor microenvironment (TME), and their effects on patient outcomes. By analyzing the TCGA-SKCM and GEO cohorts, we identified three melanoma subtypes with distinct prognostic features. Additionally, we developed a machine learning-driven signature (MLDS) based on marker genes for different molecular subtypes to significantly improve the prognostic prediction accuracy for melanoma patients. We also extensively examined differences in clinical features, immune cell infiltration, mutational landscapes, and drug treatment effects between high- and low-scoring subgroups. The predictive reliability of MLDS was further explored by knocking down the key signature gene AGPAT2 in melanoma cells using small interfering RNA.

RESULTS

The MLDS demonstrated high C-index values in both the training and validation cohorts, indicating its potential for clinical decision-making. The study also found that MLDS scores were associated with reduced immune cell infiltration and lower expression levels of immune checkpoints. Patients in the low MLDS group may be more responsive to chemotherapeutic agents and more likely to benefit from immune checkpoint inhibitors (ICIs). Single-cell sequencing analysis revealed that the MAPK signaling pathway between AGPAT2 + melanoma cells and fibroblasts/myeloid cells promotes tumor survival in the TME. Finally, the oncogenic role of AGPAT2 in melanoma cell lines was successfully confirmed through cell function assays and subcutaneous tumor formation assays in nude mice.

CONCLUSION

This study not only uncovers the diversity and complexity of melanoma molecular subtypes but also underscores the crucial role of the TME in melanoma progression. It provides new insights and tools for personalized treatment and prognostic assessment of SKCM.