Potential Prognostic Predictors and Molecular Targets for Skin Melanoma Screened by Weighted Gene Co-expression Network Analysis.

AIMS AND

OBJECTIVES

Among skin cancers, malignant skin melanoma is the leading cause of death. Identification of gene markers of malignant skin melanoma associated with survival may provide new clues for prognosis prediction and treatment. This research aimed to screen out potential prognostic predictors and molecular targets for malignant skin melanoma.

INTRODUCTION

Information regarding gene expression in skin melanoma and patients' clinical traits was obtained from the Gene Expression Omnibus database. Weighted gene co-expression network analysis (WGCNA) was applied to build co-expression modules and investigate the association between the modules and clinical traits. Moreover, functional enrichment analysis was performed for clinically significant co-expression modules. Hub genes of these modules were validated via Gene Expression Profiling Interactive Analysis (GEPIA) and the Human Protein Atlas (http:// www.proteinatlas.org).

METHODS

First, using WGCNA, 9 co-expression modules were constructed by the top 25% differentially expressed genes (4406 genes) from 77 human melanoma samples. Two co-expression modules (magenta and blue modules) were significantly correlated with survival months (r = -0.27, p = 0.02; r = 0.27, p = 0.02, respectively). The results of functional enrichment analysis demonstrated that the magenta module was mainly enriched in the cell cycle process and the blue module was mainly enriched in the immune response process. Additionally, the GEPIA and Human Protein Atlas results suggested that the hub genes CCNB2, ARHGAP30, and SEMA4D were associated with relapse-free survival and overall survival (all p-values < 0.05) and were differentially expressed in melanoma tumors and normal skin.

RESULTS AND

CONCLUSION

The results provided the framework of co-expression gene modules of skin melanoma and screened out CCNB2, ARHGAP30, and SEMA4D associated with survival as potential prognostic predictors and molecular targets of treatment.