Screening and Identification of Key Biomarkers in Melanoma: Evidence from Bioinformatic Analyses,Journal of Computational Biology

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Screening and Identification of Key Biomarkers in Melanoma: Evidence from Bioinformatic Analyses
Journal of Computational Biology ( IF 1.7 ) Pub Date : 2021-03-04 , DOI: 10.1089/cmb.2019.0400
Yijun Xia ₁ , Juan Xie ₁ , Jun Zhao ₁ , Yin Lou ₁ , Dongsheng Cao ₁

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Melanoma is an extremely malignant and occult tumor. To identify candidate genes related to melanoma carcinogenesis and progression, the microarray data sets GSE83583, GSE130244, and GSE31879 were retrieved from the Gene Expression Omnibus (GEO) database using the GEO2R analytical tool provided by the National Center for Biotechnology Information (NCBI). Gene expression analysis was carried out using the DAVID database for Gene Ontology and Kyoto Encyclopedia of Genes and Genomes functional enrichment analyses of differentially expressed genes. A protein–protein interaction network was constructed with the STRING database, the interaction data were imported into Cytoscape software, and the network topology was analyzed to identify key genes. Hub gene expression was verified in the Gene Expression Profiling Interactive Analysis and Human Protein Atlas databases. In addition, Kaplan–Meier survival analysis was performed on hub genes. A total of 142 differentially expressed genes were identified in melanoma tissues, including 50 upregulated genes and 92 downregulated genes. Five central genes (CCNA2, EBP, GABBR2, TRIM32, and ADAM10) were found based on the degree of the nodes. These genes are mainly enriched in protein serine/threonine kinase activity and apoptosis pathways. Survival analysis showed CCNA2 to be related to the overall survival (OS) of patients, and increased expression of TRIM32 led to increased OS and disease-free survival risk. Bioinformatics methods can be used to effectively select key genes in melanoma, and CCNA2 and TRIM32 may be new targets for treatment of this disease.

更新日期：2021-03-05

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