Objective: Identification of colorectal cancer (CRC) metastasis genes is one of the most important issues in CRC research. For the purpose of mining CRC metastasis-associated genes, an integrated analysis of microarray data was presented, by combined with evidence acquired from comparative genomic hybridization (CGH) data.

Methods: Gene expression profile data of CRC samples were obtained at Gene Expression Omnibus (GEO) website. The 15 important chromosomal aberration sites detected by using CGH technology were used for integrated genomic and transcriptomic analysis. Significant Analysis of Microarray (SAM) was used to detect significantly differentially expressed genes across the whole genome. The overlapping genes were selected in their corresponding chromosomal aberration regions, and analyzed by using the Database for Annotation, Visualization and Integrated Discovery (DAVID). Finally, SVM-T-RFE gene selection algorithm was applied to identify metastasis-associated genes in CRC.

Results: A minimum gene set was obtained with the minimum number [14] of genes, and the highest classification accuracy (100%) in both PRI and META datasets. A fraction of selected genes are associated with CRC or its metastasis.

Conclusions: Our results demonstrated that integration analysis is an effective strategy for mining cancer-associated genes.