Published in

Oxford University Press (OUP), Bioinformatics, 23(27), p. 3300-3305

DOI: 10.1093/bioinformatics/btr559

Links

Tools

Export citation

Search in Google Scholar

Gathering insights on disease etiology from gene expression profiles of healthy tissues.

This paper is made freely available by the publisher.
This paper is made freely available by the publisher.

Full text: Download

Green circle
Preprint: archiving allowed
Green circle
Postprint: archiving allowed
Red circle
Published version: archiving forbidden
Data provided by SHERPA/RoMEO

Abstract

MOTIVATION: Gene expression profiles have been widely used to study disease states. It may be possible, however, to gather insights into human diseases by comparing gene expression profiles of healthy organs with different disease incidence or severity. We tested this hypothesis and developed an approach to identify candidate genes associated with disease development by focusing on cancer incidence since it varies greatly across human organs. RESULTS: We normalized organ-specific cancer incidence by organ weight and found that reproductive organs tend to have a higher mass-normalized cancer incidence, which could be due to evolutionary trade-offs. Next, we performed a genome-wide scan to identify genes whose expression across healthy organs correlates with organ-specific cancer incidence. We identified a large number of genes, including genes previously associated with tumorigenesis and new candidate genes. Most genes exhibiting a positive correlation with cancer incidence were related to ribosomal and transcriptional activity, translation and protein synthesis. Organs with enhanced transcriptional and translational activation may have higher cell proliferation and therefore be more likely to develop cancer. Organs with lower cancer incidence also tend to express lower levels of known cancer-associated genes. Overall, these results demonstrate how genes and processes that predispose organs to specific diseases can be identified using gene expression profiles from healthy tissues. Our approach can be applied to other diseases and serve as foundation for further oncogenomic analyses. CONTACT: jp@senescence.info SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.