A multigene support vector machine predictor for metastasis of cutaneous melanoma.

Abstract

Gene expression profiles of cutaneous melanoma were analyzed to identify critical genes associated with metastasis. Two gene expression datasets were downloaded from Gene Expression Omnibus (GEO) and another dataset was obtained from The Cancer Genome Atlas (TCGA). Differentially expression genes (DEGs) between metastatic and non-metastatic melanoma were identified by meta-analysis. A protein-protein interaction (PPI) network was constructed for the DEGs using information from BioGRID, HPRD and DIP. Betweenness centrality (BC) was calculated for each node in the network and the top feature genes ranked by BC were selected to construct the support vector machine (SVM) classifier using the training set. The SVM classifier was then validated in another independent dataset. Pathway enrichment analysis was performed for the feature genes using Fisher's exact test. A total of 798 DEGs were identified and a PPI network including 337 nodes and 466 edges was then constructed. Top 110 feature genes ranked by BC were included in the SVM classifier. The prediction accuracies for the three datasets were 96.8, 100 and 94.4%, respectively. A total of 11 KEGG pathways and 13 GO biological pathways were significantly over-represented in the 110 feature genes, including endometrial cancer, regulation of actin cytoskeleton, focal adhesion, ubiquitin mediated proteolysis, regulation of apoptosis and regulation of cell proliferation. A SVM classifier of high prediction accuracy was acquired. Several critical genes implicated in melanoms metastasis were also revealed. These results may advance understanding of the molecular mechanisms underlying metastasis, and also provide potential therapeutic targets.