Integrative analysis to select cancer candidate biomarkers to targeted validation
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Rebeca Kawahara1,*, Gabriela V. Meirelles1,*, Henry Heberle2, Romênia R. Domingues1, Daniela C. Granato1, Sami Yokoo1, Rafael R. Canevarolo1,3, Flavia V. Winck1, Ana Carolina P. Ribeiro4, Thaís Bianca Brandão4, Paulo R. Filgueiras5, Karen S. P. Cruz6, José Alexandre Barbuto6, Ronei J. Poppi5, Rosane Minghim2, Guilherme P. Telles7, Felipe Paiva Fonseca8, Jay W. Fox9, Alan R. Santos-Silva8, Ricardo D. Coletta8, Nicholas E. Sherman9, Adriana F. Paes Leme1
1Laboratório de Espectrometria de Massas, Laboratório Nacional de Biociências, LNBio, CNPEM, Campinas, Brazil
2Instituto de Ciências Matemáticas e de Computação, Universidade de São Paulo, USP, São Carlos, Brazil
3Centro Infantil Boldrini, Campinas, Brazil
4Instituto do Câncer do Estado de São Paulo, Octavio Frias de Oliveira, São Paulo, Brazil
5Instituto de Química, Universidade Estadual de Campinas, UNICAMP, Piracicaba, Brazil
6Instituto de Ciências Biomédicas, Departamento de Imunologia, Universidade de São Paulo, USP, São Paulo, Brazil
7Instituto de Computação, Universidade Estadual de Campinas, UNICAMP, Campinas, Brazil
8Faculdade de Odontologia de Piracicaba, Universidade Estadual de Campinas, UNICAMP, Piracicaba, Brazil
9W. M. Keck Biomedical Mass Spectrometry Lab. University of Virginia, Charlottesville, Virginia, USA
*These authors have contributed equally to this work
Adriana F. Paes Leme, e-mail: firstname.lastname@example.org
Keywords: candidate biomarker, integrative analysis, proteomics, discovery, targeted
Received: July 24, 2015 Accepted: October 17, 2015 Published: October 30, 2015
Targeted proteomics has flourished as the method of choice for prospecting for and validating potential candidate biomarkers in many diseases. However, challenges still remain due to the lack of standardized routines that can prioritize a limited number of proteins to be further validated in human samples. To help researchers identify candidate biomarkers that best characterize their samples under study, a well-designed integrative analysis pipeline, comprising MS-based discovery, feature selection methods, clustering techniques, bioinformatic analyses and targeted approaches was performed using discovery-based proteomic data from the secretomes of three classes of human cell lines (carcinoma, melanoma and non-cancerous). Three feature selection algorithms, namely, Beta-binomial, Nearest Shrunken Centroids (NSC), and Support Vector Machine-Recursive Features Elimination (SVM-RFE), indicated a panel of 137 candidate biomarkers for carcinoma and 271 for melanoma, which were differentially abundant between the tumor classes. We further tested the strength of the pipeline in selecting candidate biomarkers by immunoblotting, human tissue microarrays, label-free targeted MS and functional experiments. In conclusion, the proposed integrative analysis was able to pre-qualify and prioritize candidate biomarkers from discovery-based proteomics to targeted MS.
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