Alternative splicing in cancer: noise, functional, or systematic?
Pre-messenger RNA splicing is a fine-tuned process that generates multiple functional variants from individual genes. Various cell types and developmental stages regulate alternative splicing patterns differently in their generation of specific gene functions. In cancers, splicing is significantly altered, and understanding the underlying mechanisms and patterns in cancer will shed new light onto cancer biology. Cancer-specific transcript variants are promising biomarkers and targets for diagnostic, prognostic, and treatment purposes. In this review, we explore how alternative splicing cannot simply be considered as noise or an innocent bystander, but is actively regulated or deregulated in cancers. A special focus will be on aspects of cell biology and biochemistry of alternative splicing in cancer cells, addressing differences in splicing mechanisms between normal and malignant cells. The systems biology of splicing is only now applied to the field of cancer research. We explore functional annotations for some of the most intensely spliced gene classes, and provide a literature mining and clustering that reflects the most intensely investigated genes. A few well-established cancer-specific splice events, such as the CD44 antigen, are used to illustrate the potential behind the exploration of the mechanisms of their regulation. Accordingly, we describe the functional connection between the regulatory machinery (i.e., the spliceosome and its accessory proteins) and their global impact on qualitative transcript variation that are only now emerging from the use of genomic technologies such as microarrays. These studies are expected to open an entirely new level of genetic information that is currently still poorly understood.
Skotheim
Rolf I
RI
Department of Cancer Prevention, Institute for Cancer Research, Rikshospitalet-Radiumhospitalet Medical Center, Oslo, Norway.
Nees
Matthias
M
D017398:Alternative Splicing; D019295:Computational Biology / Q000379:methods*; D020869:Gene Expression Profiling; D006801:Humans; D009369:Neoplasms / Q000235:genetics / Q000378:metabolism*