BITS Meetings' Virtual Library:
Abstracts from Italian Bioinformatics Meetings from 1999 to 2013

766 abstracts overall from 11 distinct proceedings

1. Cazzola M, Cremona M, Monti L, Vignati F, Lavorgna G, Taramelli R, Acquati F, Guffanti A
Cancer and antisense transcription: a bioinformatic strategy for the identification of putative antisense-regulated tumor suppressor genes
Meeting: BITS 2005 - Year: 2005
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Topic: Medical Bioinformatics

Abstract: Computational analysis of genomic sequence databases has recently revealed a striking abundance of Natural Antisense Transcripts within the mouse and human genomes. Since antisense transcription is increasingly recognized as a molecular mechanism involved in the regulation of gene expression, a significant proportion of human disease genes could potentially display antisense-mediated abnormal patterns of gene expression. Cancer is a pathological phenotype that could represent a potential target for such gene regulation mechanism, given the high number of genes governing cancer-related cellular functions such as proliferation, differentiation and apoptosis. Preliminary experimental evidence has been reported recently for the occurrence of natural antisense transcript for several genes whose function has been implicated in cancer pathogenesis. Therefore, a targeted in silico survey of antisense transcription, coupled with a detailed inspection of annotated gene features, could represent a novel tool for the identification of candidate cancer-related genes. We have performed an in silico search for “sense-antisense gene clusters” within two regions from human chromosome 6 (6q21 and 6q27) that have long been reported to carry cancer-associated deletions and rearrangements, but for which no tumor suppressor genes has been unambiguously identified. Experimental validation of each sense-antisense cluster detected in this study, followed by definition of bona fide tumor suppressor candidates based on the available annotation features, confirmed the feasibility of this approach to better define candidate cancer-associated genes.

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