Please use this identifier to cite or link to this item: http://repositorio.unicamp.br/jspui/handle/REPOSIP/235277
Type: Artigo
Title: Integrative analysis to select cancer candidate biomarkers to targeted validation
Author: Kawahara, Rebeca
Meirelles, Gabriela V.
Heberle, Henry
Domingues, Romênia R.
Granato, Daniela C.
Yokoo, Sami
Canevarolo, Rafael R.
Winck, Flavia V.
Ribeiro, Ana Carolina P.
Brandão, Thaís Bianca
Filgueiras, Paulo R.
Cruz, Karen S. P.
Barbuto, José Alexandre
Poppi, Ronei J.
Minghim, Rosane
Telles, Guilherme P.
Fonseca, Felipe Paiva
Fox, Jay W.
Santos-Silva, Alan R.
Coletta, Ricardo D.
Sherman, Nicholas E.
Paes Leme, Adriana F.
Abstract: 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.
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
Subject: Proteômica
Country: Estados Unidos
Editor: Impact Journals
Citation: Oncotarget. v. 6, n. 41, p. 43635-43652, 2015-Dec.
Rights: aberto
Identifier DOI: 10.18632/oncotarget.6018
Address: http://www.oncotarget.com/index.php?journal=oncotarget&page=article&op=view&path[]=6018
Date Issue: 2015
Appears in Collections:FOP - Artigos e Outros Documentos

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