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ISSN: 0974-276X

Journal of Proteomics & Bioinformatics
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Research Article

Impact of the Selection Mechanism in the Identification and Validation of New "Omic" Biomarkers

Caroline Truntzer1*, Delphine Maucort-Boulch2,3,4 and Pascal Roy2,3,4

1Proteomics Platform CLIPP, CHU Dijon, Dijon, France

2CNRS, UMR 5558-Team Health Biostatistics, Villeurbanne, France

3Biostatistics Health Laboratory, University Claude Bernard Lyon 1-UMR 5558, Villeurbanne, France

4Department of Biostatistics, Hospices Civils de Lyon, Lyon, France

*Corresponding Author:
Caroline Truntzer
Proteomic Platform CLIPP
CHU Dijon, Dijon, France
E-mail: [email protected]

Received date: June 12, 2013; Accepted date: August 12, 2013; Published date: August 16, 2013

Citation: Truntzer C, Maucort-Boulch D, Roy P (2013) Impact of the Selection Mechanism in the Identification and Validation of New “Omic” Biomarkers. J Proteomics Bioinform 6: 164-170. doi: 10.4172/jpb.1000276

Copyright: © 2013 Truntzer C, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Abstract

Background: High throughput analysis like mass spectrometry dedicated to clinical proteomics offers new insights into clinical research. This promising technology generates high-dimensional datasets with a huge amount of biological input. Working with these high-dimensional datasets has created challenges for statistical methods and there are still weaknesses in current statistical analysis that have to be overcome to get an accurate interpretation of “omics” studies. The central question is that of a reliable identification of new prognostic and diagnostic biomarkers. Although observed in previous studies, these mechanisms of identification and validation of new markers have been inadequately explained and often dissociated.

Results: The aim of our study was therefore to show how candidate markers are sometimes selected in identification studies because of biased estimations of their effect. To achieve this goal, this work was conducted through the simulation of high-dimensional studies concerning survival. We showed how the selection mechanism involved in identification studies influences a mechanism called regression to the mean. This in turn leads to a biased estimation of the effect size and thus to optimism when considering validation studies.

Conclusions: This study demonstrated why the discovery of new robust markers is only possible through well-designed studies relying on consistent sample sizes for the identification step. Due to the above mentioned mechanisms of identification and validation, pertinent candidate biomarkers in high-dimensional clinical studies require non-biased estimation, and this right from the identification step. Only then will it lead to consistent studies and thus reach benefit in terms of health care.

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