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

A. Sofia Silva, Shona H. Wood, Sipko van Dam, Sven Berres, Anne McArdle, João Pedro de Magalhães

    Research output: Contribution to journalArticlepeer-review

    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. Furthermore, we found that organs with lower cancer incidence 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. © The Author 2011. Published by Oxford University Press. All rights reserved.
    Original languageEnglish
    Article numberbtr559
    Pages (from-to)3300-3305
    Number of pages5
    JournalBioinformatics
    Volume27
    Issue number23
    DOIs
    Publication statusPublished - Dec 2011

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