Screening and identification of potential prognostic biomarkers in metastatic skin cutaneous melanoma by bioinformatics analysis

Skin cutaneous melanoma (SKCM) is a multifactorial disease that presents a poor prognosis due to its rapid progression towards metastasis. This study focused on the identification of prognostic differentially expressed genes (DEGs) between primary and metastatic SKCM. DEGs were obtained using three chip data sets from the Gene Expression Omnibus database. The protein-protein interaction network was described by STRING and Cytoscape. Kaplan-Meier curves were implemented to evaluate survival benefits within distinct groups. A total of 258 DEGs were distinguished as possible candidate biomarkers. Besides, survival curves indicated thatDSG3,DSC3,PKP1,EVPL,IVL,FLG,SPRR1AandSPRR1Bwere of significant value to predict the metastatic transformation of melanoma. To further validate our hypotheses, functional enrichment and significant pathways of the hub genes were performed to indicate that the most involved considerable path. In summary, this study identified substantial DEGs participating in melanoma metastasis.DGS3,DSC3,PKP1,EVPL,IVL,FLG,SPRR1AandSPRR1Bmay be considered as new biomarkers in the therapeutics of metastatic melanoma, which might help us predict the potential metastatic capability of SKCM patients, thus provide earlier precautionary treatments. However, further experiments are still required to support the specific mechanisms of these hub genes.