Shared Gene Expression and Immune Pathway Changes Associated with Progression from Nevi to Melanoma

SIMPLE SUMMARY: Melanoma is a deadly skin cancer, and the incidence of melanoma is rising. Chemoprevention, using small molecule drugs to prevent the development of cancer, is a key strategy that could reduce the burden of melanoma on society. The long-term goal of our study is to develop a gene signature biomarker of progression from nevi to melanoma. We found that a small number of genes can distinguish nevi from melanoma and identified shared genes and immune-related pathways that are associated with progression from nevi to melanoma across independent datasets. This study demonstrates (1) a novel approach to aid melanoma chemoprevention trials by using a gene signature as a surrogate endpoint and (2) the feasibility of determining a gene signature biomarker of melanoma progression. ABSTRACT: There is a need to identify molecular biomarkers of melanoma progression to assist the development of chemoprevention strategies to lower melanoma incidence. Using datasets containing gene expression for dysplastic nevi and melanoma or melanoma arising in a nevus, we performed differential gene expression analysis and regularized regression models to identify genes and pathways that were associated with progression from nevi to melanoma. A small number of genes distinguished nevi from melanoma. Differential expression of seven genes was identified between nevi and melanoma in three independent datasets. C1QB, CXCL9, CXCL10, DFNA5 (GSDME), FCGR1B, and PRAME were increased in melanoma, and SCGB1D2 was decreased in melanoma, compared to dysplastic nevi or nevi that progressed to melanoma. Further supporting an association with melanomagenesis, these genes demonstrated a linear change in expression from benign nevi to dysplastic nevi to radial growth phase melanoma to vertical growth phase melanoma. The genes associated with melanoma progression showed significant enrichment of multiple pathways related to the immune system. This study demonstrates (1) a novel application of bioinformatic approaches to aid clinical trials of melanoma chemoprevention and (2) the feasibility of determining a gene signature biomarker of melanomagenesis.