Integration of graph neural networks and transcriptomics analysis identify key pathways and gene signature for immunotherapy response and prognosis of skin melanoma.

OBJECTIVE

The assessment of immunotherapy plays a pivotal role in the clinical management of skin melanoma. Graph neural networks (GNNs), alongside other deep learning algorithms and bioinformatics approaches, have demonstrated substantial promise in advancing cancer diagnosis and treatment strategies.

METHODS

GNNs models were developed to predict the response to immunotherapy and to pinpoint key pathways. Utilizing the genes from these key pathways, multi-omics bioinformatics methods were employed to refine the construction of a gene signature, termed responseScore, aimed at enhancing the precision of immunotherapy response predictions. Subsequently, responseScore was explored from the perspectives of prognosis, genetic variation, pathway enrichment, and the tumor microenvironment. Concurrently, the association among 13 genes contributing to responseScore and factors such as immunotherapy response, prognosis, and the tumor microenvironment was investigated. Among these genes, PSMB6 was subjected to an in-depth analysis of its biological effect through experimental approaches like transfection and co-culture.

RESULTS

In the finalized model utilizing GNNs, it has revealed an AUC of 0.854 within the training dataset and 0.824 within the testing set, pinpointing key pathways such as R-HSA-70,268. The indicator named as responseScore excelled in its predictive accuracy regarding immunotherapy response and patient prognosis. Investigations into genetic variation, pathway enrichment, tumor microenvironment disclosed a profound association between responseScore and the enhancement of immune cell infiltration and anti-tumor immunity. A negative correlation was observed between the expression of PSMB6 and immune genes, with elevated PSMB6 expression correlating with poor prognosis. ELISA detection after co-cultivation experiments revealed significant reductions in the levels of cytokines IL-6 and IL-1β in specimens from the PCDH-PSMB6 group.

CONCLUSION

The GNNs prediction model and the responseScore developed in this research effectively indicate the immunotherapy response and prognosis for patients with skin melanoma. Additionally, responseScore provides insights into the tumor microenvironment and the characteristics of tumor immunity of melanoma. Thirteen genes identified in this study show promise as potential tumor markers or therapeutic targets. Notably, PSMB6 emerges as a potential therapeutic target for skin melanoma, where its elevated expression exhibits an inhibitory effect on the tumor immunity.