An immunogenomic signature for molecular classification in melanoma.

Abstract

e21562 Background: Melanoma is one of the most aggressive tumors in the world, characterized by an increasing incidence and high mortality with half of the patients dying from the advanced tumors. The melanoma tumor is very heterogenous and the appropriate classification of subtypes is the key to guide precision treatment. Previous studies have revealed the important roles of immune cells in melanoma. Therefore, the better classification method and comprehensive analysis of immune cells and immune-related genes can help to better understand the tumor progression and develop effective treatment strategies in melanoma. Methods: Two datasets containing RNA sequencing data and paired patient information with melanoma were collected in this study. The first dataset of skin cutaneous melanoma patients downloaded from The Cancer Genome Atlas database (n = 204) was used as the training set, and the second dataset from a published study was used as internal validation set (n = 102). Single sample gene enrichment analysis (ssGSEA), ESTIMATE, CIBERSORT and k-means analysis were used to classify melanoma patients into two different immune clusters. We then constructed a molecular signature with nine immune related genes that could predict the overall survival (OS) of melanoma patients by LASSO regression and Cox regression analysis. The accuracy of the risk signature was then verified in eight independent cancer types. Results: The melanoma patients were divided into two different immune clusters, the high-immune cell infiltration cluster (Immunity_H) and low-immune cell infiltration cluster (Immunity_L). 1,556 and 1445 differentially expressed genes (DEGs) between the Immunity_H and Immunity_L were detected in the training set and validation set, respectively. Accordingly, 677 and 147 genes associated with overall survival (OS) was further selected by Univariate Cox regression analysis. 59 genes were shared between the 677 and 147 OS related genes. Eventually, 9 DEGs (CST7, CSF2RB, SPARC, SPOCK2, PLA2G2D, MYO1G, NCF1, ICOS and HSH2D) were screened out by LASSO regression analysis and were then subjected to multivariate Cox regression analysis to further construct the prognostic signature. Patients were divided into high-risk and low-risk groups based on the computed risk threshold. Kaplan-Meier analysis indicated that patients in the high-risk group had a significantly worse OS. Finally, 8 independent external pan-cancer cohorts were used to validate the robustness of the model. Conclusions: This study identified two immune-related clusters and a risk signature based on nine immune-related genes was constructed, which could predict the melanoma cancer OS and the constructed model can be used as prognosis predictor in melanoma.