Abstract
The within-sample relative expression orderings (REOs) of genes, which are stable qualitative transcriptional characteristics, can provide abundant information for a disease. Methods based on REO comparisons have been proposed for identifying differentially expressed genes (DEGs) at the individual level and for detecting disease-associated genes based on one-phenotype disease data by reusing data of normal samples from other sources. Here, we evaluated the effects of common potential confounding factors, including age, cigarette smoking, sex, and race, on the REOs of gene pairs within normal lung tissues transcriptome. Our results showed that age has little effect on REOs within lung tissues. We found that about 0.23% of the significantly stable REOs of gene pairs in nonsmokers' lung tissues are reversed in smokers' lung tissues, introduced by 344 DEGs between the two groups of samples (RankCompV2, FDR <0.05), which are enriched in metabolism of xenobiotics by cytochrome P450, glutathione metabolism, and other pathways (hypergeometric test, FDR <0.05). Comparison between the normal lung tissue samples of males and females revealed fewer reversal REOs introduced by 24 DEGs between the sex groups, among which 19 DEGs are located on sex chromosomes and 5 DEGs involving in spermatogenesis and regulation of oocyte are located on autosomes. Between the normal lung tissue samples of white and black people, we identified 22 DEGs (RankCompV2, FDR <0.05) which introduced a few reversal REOs between the two races. In summary, the REO-based study should take into account the confounding factors of cigarette smoking, sex, and race.
Highlights
We have revealed an important biological phenomenon that, despite high variations of gene expression levels among different individuals, the within-sample relative expression orderings (REOs) of genes are highly stable in a particular type of normal human tissue, which might be an intrinsic mechanism to keep genes functioning coordinately in the normal tissues
We have proposed a REO-based algorithm, named DRFunc [19], to identify disease-associated pathways based on one-phenotype data through comparing the stable REO in the one-phenotype disease samples with the normal stable REOs background predetermined in previously accumulated normal samples from other studies
The samples were divided into two groups based on the REO pattern of each gene pair, and we test whether there is a significant difference in age between the two groups of samples based on the Mann–Whitney U-test
Summary
We have revealed an important biological phenomenon that, despite high variations of gene expression levels among different individuals, the within-sample relative expression orderings (REOs) of genes are highly stable in a particular type of normal human tissue, which might be an intrinsic mechanism to keep genes functioning coordinately in the normal tissues. As the qualitative characteristics of transcriptomes, the within-sample relative expression orderings (REOs) of genes are highly robust against measurement variations and experimental batch effects [4,5,6]. Taking these unique advantages of the REOs, some REObased methods such as TSP [7], K-TSP [8] and others [9, 10] have been developed for discriminating cancer subtypes. Based on the REOs analysis, we have proposed an algorithm named RankComp [1] to detect differentially expressed genes (DEGs) for an individual disease sample compared with its previous normal state through analyzing Many REO-based prognostic signatures have been proposed for specific medical issues for various cancers such as nonsmall cell lung cancer [3, 11], colorectal cancer [4, 12], and other cancers [13,14,15].
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
