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.

Figure 1.

Heat map of the expression levels of the 798 DEGs. ‘M’ means metastatic cutaneous melanoma; ‘N’ means non-metastatic cutaneous melanoma. X-axis is samples and Y-axis is genes expression level. Green color means higher gene expression level; red color means lower gene expression level. DEGs, differentially expressed genes.

Figure 2.

The protein-protein interaction network of the differentially expressed genes (DEGs). Up-regulated genes in metastatic melanoma are in red, down-regulated genes in metastatic melanoma are in green.

Figure 3.

Distribution of degree. X-axis is Log transformed degree and Y-axis is number of node.

Figure 4.

Accuracy rate with various numbers of differentially expressed genes (A) for the training dataset. Scatter plot showing prediction result of the training dataset (B). Metastatic cutaneous melanoma samples are in red and non-metastatic cutaneous melanoma samples are in black. The receiver operating characteristic (ROC) curve of for the training dataset (C).

Figure 5.

Scatter plots and ROC curves for dataset GSE46517 (A and C) and GSE7553 (B and D). Metastatic cutaneous melanoma samples are in red and non-metastatic cutaneous melanoma samples are in black. ROC, receiver operating characteristic.

Figure 6.

KEGG pathways and GO biological pathways significantly over-represented in the 110 feature genes. X-axis indicates number of gene and color of the bar indicates-lg (P-value). KEGG, Kyoto Encyclopedia of Genes and Genomes; GO, gene ontology.