Integrated analysis of numerous heterogeneous gene expression profiles for detecting robust disease-specific biomarkers and proposing drug targets

Abstract

Genome-wide expression profiling has revolutionized biomedical research; vast amounts of expression data from numerous studies of many diseases are now available. Making the best use of this resource in order to better understand disease processes and treatment remains an open challenge. In particular, disease biomarkers detected in case–control studies suffer from low reliability and are only weakly reproducible. Here, we present a systematic integrative analysis methodology to overcome these shortcomings. We assembled and manually curated more than 14 000 expression profiles spanning 48 diseases and 18 expression platforms. We show that when studying a particular disease, judicious utilization of profiles from other diseases and information on disease hierarchy improves classification quality, avoids overoptimistic evaluation of that quality, and enhances disease-specific biomarker discovery. This approach yielded specific biomarkers for 24 of the analyzed diseases. We demonstrate how to combine these biomarkers with large-scale interaction, mutation and drug target data, forming a highly valuable disease summary that suggests novel directions in disease understanding and drug repurposing. Our analysis also estimates the number of samples required to reach a desired level of biomarker stability. This methodology can greatly improve the exploitation of the mountain of expression profiles for better disease analysis.

Journal

Journal ID (nlm-ta): Nucleic Acids Res

Journal ID (iso-abbrev): Nucleic Acids Res

Journal ID (hwp): nar

Journal ID (publisher-id): nar

Title: Nucleic Acids Research

Publisher: Oxford University Press

ISSN (Print): 0305-1048

ISSN (Electronic): 1362-4962

Publication date (Print): 18 September 2015

Publication date (Electronic): 10 August 2015

Publication date PMC-release: 10 August 2015

Volume: 43

Issue: 16

Pages: 7779-7789

Affiliations

[1 ]The Blavatnik School of Computer Science, Tel Aviv University, Tel Aviv 69978, Israel

[2 ]Department of Pediatric Hematology-Oncology, Safra Children's Hospital, Sheba Medical Center, Tel Hashomer, Ramat Gan 52620, Israel

[3 ]Sackler School of Medicine, Tel-Aviv University, Tel Aviv 69978, Israel

Author notes

[* ]To whom correspondence should be addressed. Tel: [phone]; Fax: [phone]; Email: rshamir@ 123456tau.ac.il

Article

DOI: 10.1093/nar/gkv810

PMC ID: 4652780

PubMed ID: 26261215

Copyright statement: © The Author(s) 2015. Published by Oxford University Press on behalf of Nucleic Acids Research.

License:

This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@ 123456oup.com

Page count

Pages: 11

Product

Categories

Subject: 9

Subject: 24

Subject: Computational Biology

Custom metadata

cover-date 18 September 2015