Alternative splicing allows a single gene to generate multiple RNA transcripts which can be translated into functionally diverse protein isoforms. Current knowledge of splicing is derived mainly from RNA transcripts, with very little known about the expression level, 3D structures, and functional differences of the proteins. Splicing is a remarkable phenomenon of molecular and biological evolution. Studies which simply report up-regulation or down-regulation of protein or mRNA expression are confounded by the effects of mixtures of these isoforms. Besides understanding the net biological effects of the mixtures, we may be able to develop biomarker tests based on the observable differential expression of particular splice variants or combinations of splice variants in specific disease states. Here we review our work on differential expression of splice variant proteins in cancers and the feasibility of integrating proteomic analysis with structure-based conformational predictions of the differences between such isoforms.
Keywords: Cancer biomarkers; Computational modeling; Differential expression; Splicing.
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