Development of a novel splice array platform and its application in the identification of alternative splice variants in lung cancer

BMC Genomics

Methodology Article

Angel Rubio¹  Miguel Angel Anton¹  Jose M López-Picazo²  Maria D Lozano³  Javier Gomez-Roman⁴  Teresa Ezponda⁵  Ruben Pio⁶  Maria Jose Pajares⁷  Luis M Montuenga⁷  David Blanco⁷  Jackeline Agorreta⁷  Olga Durany⁸  Tamara Maes⁸  Elena Aibar⁸  Francesc Subirada⁸ 

[1] CEIT and TECNUN, University of Navarra, San Sebastian, Spain;Department of Oncology, Clínica Universidad de Navarra, Pamplona, Spain;Department of Pathology, Clínica Universidad de Navarra, Pamplona, Spain;Department of Pathology, Marques de Valdecilla University Hospital, School of Medicine, University of Cantabria, Santander, Spain;Division of Oncology, Center for Applied Medical Research, Pamplona, Spain;Division of Oncology, Center for Applied Medical Research, Pamplona, Spain;Department of Biochemistry, School of Medicine, University of Navarra, Pamplona, Spain;Division of Oncology, Center for Applied Medical Research, Pamplona, Spain;Department of Histology and Pathology, School of Medicine, University of Navarra, Pamplona, Spain;Oryzon Genomics, Scientific Parc University of Barcelona, Barcelona, Spain;

关键词: Alternative Splice;    Normal Lung Tissue;    Splice Form;    Alternative Splice Event;    Intron Retention;   

DOI  :  10.1186/1471-2164-11-352

 received in 2009-12-21, accepted in 2010-06-03,  发布年份 2010

来源: Springer

【 摘 要 】

BackgroundMicroarrays strategies, which allow for the characterization of thousands of alternative splice forms in a single test, can be applied to identify differential alternative splicing events. In this study, a novel splice array approach was developed, including the design of a high-density oligonucleotide array, a labeling procedure, and an algorithm to identify splice events.ResultsThe array consisted of exon probes and thermodynamically balanced junction probes. Suboptimal probes were tagged and considered in the final analysis. An unbiased labeling protocol was developed using random primers. The algorithm used to distinguish changes in expression from changes in splicing was calibrated using internal non-spliced control sequences. The performance of this splice array was validated with artificial constructs for CDC6, VEGF, and PCBP4 isoforms. The platform was then applied to the analysis of differential splice forms in lung cancer samples compared to matched normal lung tissue. Overexpression of splice isoforms was identified for genes encoding CEACAM1, FHL-1, MLPH, and SUSD2. None of these splicing isoforms had been previously associated with lung cancer.ConclusionsThis methodology enables the detection of alternative splicing events in complex biological samples, providing a powerful tool to identify novel diagnostic and prognostic biomarkers for cancer and other pathologies.

【 授权许可】

CC BY   
© Pio et al; licensee BioMed Central Ltd. 2010