Abstract

Worldwide, metastatic melanoma of the skin has an aggressive course with high morbidity and mortality. Therefore, an increased understanding of the pathogenesis of metastatic melanoma has gained increasing attention, including the role of epigenetic modification and competing endogenous RNA (ceRNA). This study aimed to used bioinformatics data to undertake an integrative analysis of long noncoding RNA (lncRNA), microRNA (miRNA) and mRNA expression to construct a ceRNA network in metastatic melanoma. Data from the Cancer Genome Atlas (TCGA), the Gene Ontology (GO) database, and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway were analyzed. There were 471 cases that included 103 primary solid tumors and 368 cases of metastatic melanoma that included transcriptome sequencing data (including lncRNA and mRNA); 452 cases had miRNA sequencing data. Analysis of chip data identified 85 6 mRNAs, 67 miRNAs, and 250 lncRNAs that were differentially expressed in cases of metastatic melanoma, of which 25 miRNAs, 18 lncRNAs, and 18 mRNAs participated in the formation of ceRNAs. Survival analysis identified seven differentially expressed mRNAs, five differentially expressed miRNAs (miRNA-29c, miRNA-100, miR-142-3p, miR-150, miR-516a-2), and six differentially expressed lncRNAs (AC068594.1, C7orf71, FAM41C, GPC5-AS1, MUC19, LINC00402) that were correlated with survival time in patients with metastatic melanoma. Bioinformatics data and integrative analysis identified lncRNA, miRNA, and mRNA expression to construct a ceRNA and patient survival network in metastatic melanoma. These findings support the need for further studies on the mechanisms involved in the regulation of metastatic melanoma by ceRNAs.

Keywords: 1-Acylglycerol-3-Phosphate O-Acyltransferase, MicroRNAs, NM23 Nucleoside Diphosphate Kinases, RNA Caps, RNA, Long Noncoding, Computational Biology, Databases, Genetic, gene ontology, Gene Regulatory Networks, Kaplan-Meier Estimate, Melanoma, Oligonucleotide Array Sequence Analysis, RNA, Messenger, Skin Neoplasms, Survival Analysis, transcriptome