Identification of candidate cancer genes involved in human retinoblastoma by data mining

The objective of this study was to discover potential cancer-related genes involved in retinoblastoma (RB) tumorigenesis.

Using a data-mining tool called cDNA Digital Gene Expression Displayer (DGED) and serial analysis of gene expression DGED from the Cancer Genome Anatomy Project (CGAP) database, eight cDNA libraries and five serial analysis of gene expression libraries from retinoblastoma (RB) solid tumors and normal retina tissues were analyzed. The deregulated genes were classified into major families using information from Gene Ontology. Several candidate cancer-related genes were analyzed by real-time reverse transcription polymerase chain reaction (RT-PCR) and immunohistochemistry (IHC) on tissue microarrays (TMA) of RB and human normal retina samples.

A total of 260 genes with deregulated expression emerged when examined by DGED from the CGAP database. Functional classification of these genes not only provided an interesting insight into RB tumorigenesis but also facilitated target identification for RB therapeutics. Several candidate genes were confirmed by real-time RT-PCR and IHC analysis on TMA and were found to be associated with RB genesis through text-mining in Information Hyperlinked over Proteins. The results also implicated MCM7 and WIF1 as promising therapeutic targets for RB, but further validation is needed.