Revealing differential modules in uveal melanoma by analyzing differential networks

Abstract

The aim of the present study was to investigate differential modules (DMs) between uveal melanoma (UM) and normal conditions by examining differential networks. Based on a gene expression profile collected from the ArrayExpress database, the inference of DMs involved three steps: The first step was construction of a differential co-expression network (DCN); second, the module algorithm was adapted to identify the DMs presented in DCN; finally, the statistical significance of DMs were assessed based on the null score distribution of DMs generated using randomized networks. A DCN with 309 nodes and 3,729 edges was obtained, and 30 seed genes from the DCN were examined. Subsequently, one DM, which had 179 nodes and 3,068 edges, was investigated. By utilizing randomized networks, the P-value for DM was 0.034, therefore, the DM was statically significant between UM and baseline conditions. In conclusion, the present study successfully identified one DM in UM based on DCN and module algorithm, and this DM may be beneficial in revealing the pathological mechanism of UM and provide insight for future investigation of UM.