ArticleChild's nervous system : ChNS : official journal of the International Society for Pediatric Neurosurgery2008
Identification of candidate cancer genes involved in human retinoblastoma by data mining.
Article in Child's nervous system : ChNS : official journal of the International Society for Pediatric Neurosurgery, 2008. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed.
- Comparison of Oxidative and Hypoxic Stress Responsive Genes from Meta-Analysis of Public Transcriptomes.Biomedicines · 2021Article
- Errors in CGAP xProfiler and cDNA DGED: the importance of library parsing and gene selection algorithms.BMC bioinformatics · 2011Article
- Discovery of novel serum biomarkers for prenatal Down syndrome screening by integrative data mining.PloS one · 2009Article
- Knockdown of Derlin-1 Represses Cellular Oncogenic Activities in Uveal Melanoma, Enhances its Chemosensitivity to Cisplatin and Reduces Cancer Stem Cell Potential.Cancer genomics & proteomicsArticle
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6 authors.
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Abstract
objectiveThe objective of this study was to discover potential cancer-related genes involved in retinoblastoma (RB) tumorigenesis. MATERIALS AND
methodsUsing 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.
resultsA 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.
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