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On Selecting mRNA Isoform Features for Profiling Prostate Cancer
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2009
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Computational Biology and Chemistry
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Abstract
Alternative splicing of human pre-mRNA is a very common phenomenon and is a major contributor to proteome diversity. mRNA isoforms that arise as a result of alternative splicing also provide a more complete picture of the transcriptome as they reflect the additional processing a pre-mRNA undergoes before being translated into a functional product. It has been reported that molecular alterations of cells can occur as a result of the differential expression of mRNA isoforms, resulting in cancerous or normal tissue. Quantification of mRNA isoforms can thus be used as a better indicator in distinguishing a normal tissue from a cancerous tissue. In our earlier study we had used mRNA isoforms expression to identify biomarkers for prostate cancer (Li et. al, 2006. Cancer Res. 66 (8) 4079–4088). Here we have used statistical methods of multiple comparison and have developed a simple scoring scheme to extract isoform features. Further, we have rigorously analyzed the isoform expression data to understand the variability and heterogeneity associated with the expression levels between (i) prostate cancer cell lines and non-prostate cancer cell lines and (ii) normal prostate tissue and prostate cancer tissue. We found that there were several isoforms that showed significant difference in expression within the same class. We were also able to successfully identify isoforms with similar changes in expression levels, that when used as features for classification was able to provide robust class separation. The features selected using the multiple comparison methods had subsets that were common and disparate with those that were selected using statistical t-tests. This reveals the importance of selecting features using a combination of complementary methods. Keywords: mRNA isoforms, Splicing arrays, Multiple comparison, Prostate cancer
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