Abstract
BackgroundOwing to the explosion of information generated by human genomics, analysis of publicly available databases can help identify potential candidate genes relevant to the cancerous phenotype. The aim of this study was to scan for such genes by whole-genome in silico subtraction using Expressed Sequence Tag (EST) data.MethodsGenes differentially expressed in normal versus tumor tissues were identified using a computer-based differential display strategy. Bcl-xL, an anti-apoptotic member of the Bcl-2 family, was selected for confirmation by western blot analysis.ResultsOur genome-wide expression analysis identified a set of genes whose differential expression may be attributed to the genetic alterations associated with tumor formation and malignant growth. We propose complete lists of genes that may serve as targets for projects seeking novel candidates for cancer diagnosis and therapy. Our validation result showed increased protein levels of Bcl-xL in two different liver cancer specimens compared to normal liver. Notably, our EST-based data mining procedure indicated that most of the changes in gene expression observed in cancer cells corresponded to gene inactivation patterns. Chromosomes and chromosomal regions most frequently associated with aberrant expression changes in cancer libraries were also determined.ConclusionThrough the description of several candidates (including genes encoding extracellular matrix and ribosomal components, cytoskeletal proteins, apoptotic regulators, and novel tissue-specific biomarkers), our study illustrates the utility of in silico transcriptomics to identify tumor cell signatures, tumor-related genes and chromosomal regions frequently associated with aberrant expression in cancer.
Highlights
Owing to the explosion of information generated by human genomics, analysis of publicly available databases can help identify potential candidate genes relevant to the cancerous phenotype
The well-established differential screening technology, that allows for the simultaneous comparison of multiple gene expression levels between two samples differing in tissue type and pathological state, has been the more extensively applied
The in silico subtraction resulted in the identification of 181 and 336 genes predicted to be present or absent in the tumor types compared to normal tissues, respectively
Summary
Owing to the explosion of information generated by human genomics, analysis of publicly available databases can help identify potential candidate genes relevant to the cancerous phenotype. The well-established differential screening technology, that allows for the simultaneous comparison of multiple gene expression levels between two samples differing in tissue type and pathological state, has been the more extensively applied. This simple and powerful method could be performed either experimentally or, since late 1999, digitally using expression databases. The computer-based differential display methodology, referred to as 'in silico subtraction' or 'electronic northern' [2,3,4,5,6,7], could identify transcripts preferentially expressed or repressed in the tumor context by comparing cancerous libraries (present in publicly available databases) against the remaining libraries. Given the continuous expansion of the EST databases, both in terms of sequence and source diversity, updated and independent transcriptomic analyses are permanently needed
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