TY - JOUR
T1 - Integrative analysis to select cancer candidate biomarkers to targeted validation
AU - Kawahara, Rebeca
AU - Meirelles, Gabriela V.
AU - Heberle, Henry
AU - Domingues, Romenia R.
AU - Granato, Daniela C.
AU - Yokoo, Sami
AU - Canevarolo, Rafael R.
AU - Winck, Flavia V.
AU - Ribeiro, Ana Carolina P.
AU - Brandão, Thaís Bianca
AU - Filgueiras, Paulo R.
AU - Cruz, Karen S. P.
AU - Barbuto, José Alexandre
AU - Poppi, Ronei J.
AU - Minghim, Rosane
AU - Telles, Guilherme P.
AU - Fonseca, Felipe Paiva
AU - Fox, Jay W.
AU - Santos-Silva, Alan R.
AU - Coletta, Ricardo D.
AU - Sherman, Nicholas E.
AU - Paes Leme, Adriana F.
N1 - Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.
PY - 2015/12/22
Y1 - 2015/12/22
N2 - 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.
AB - 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.
KW - proteomics
KW - discovery
KW - targeted
KW - candidate biomarker
KW - integrative analysis
UR - http://www.scopus.com/inward/record.url?scp=84952905857&partnerID=8YFLogxK
U2 - 10.18632/oncotarget.6018
DO - 10.18632/oncotarget.6018
M3 - Article
C2 - 26540631
SN - 1949-2553
VL - 6
SP - 43635
EP - 43652
JO - Oncotarget
JF - Oncotarget
IS - 41
ER -