Metabolism-associated molecular classification and prognostic model of skin cutaneous melanoma

• 1、School of Public Health,Harbin medical University,Harbin 150081

Abstract：Objective Skin cutaneous melanoma(SKCM) is highly metabolically heterogeneous and shows diverse responses to therapy. Developing a metabolic gene-based classification system to characterize the molecular features of SKCM, improve the understanding of SKCM heterogeneity and facilitate accurate prognostic stratification and treatment. Method TCGA is used as a training set and GEO is used as a test set for analysis. Metabolism-related genes were used for non-negative matrix factorization clustering, and then the transcriptome characteristics, metabolic characteristics, immune characteristics, drug sensitivity, and prognostic value of each subtype in the original dataset were analyzed. Results Three subtypes of SKCM (cluster1, cluster2, and cluster3) were identified in both the training set and the test set. The cluster3 subtype showed high metabolic activity and poor prognosis; cluster2 showed metabolic exhaustion, had a good prognosis, and showed High expression of immune checkpoint genes, SubMap analysis indicated that they may be sensitive to PD1 drugs; cluster1 showed intermediate activity. A leaner gene score prognostic model was constructed based on metabolically-related differentially expressed genes between subtypes and validated using data from the test set. The results of the ROC curve showed that the AUC value of the risk score curve (0.75) was higher than that of the N stage, and the OS of patients in the high-risk group was significantly lower in the subgroup. Conclusion Melanoma classification based on metabolic heterogeneity can provide an important basis for clinicians to choose treatment options and predict patient prognosis.

Keywords： Skin cutaneous melanoma molecular classification metabolism non-negative matrix factorization clustering prognostic model