Genotype-Based Gene Expression in Colon Tissue-Prediction Accuracy and Relationship with the Prognosis of Colorectal Cancer Patients
Colorectal cancer (CRC) survival has environmental and inherited components. The expression of specific genes can be inferred based on individual genotypes-so called expression quantitative trait loci. In this study, we used the PrediXcan method to predict gene expression in normal colon tissue usin...
Saved in:
Published in | International journal of molecular sciences Vol. 21; no. 21; p. 8150 |
---|---|
Main Authors | , , , , , , , , , , , , |
Format | Journal Article |
Language | English |
Published |
Switzerland
MDPI
31.10.2020
MDPI AG |
Subjects | |
Online Access | Get full text |
Cover
Loading…
Summary: | Colorectal cancer (CRC) survival has environmental and inherited components. The expression of specific genes can be inferred based on individual genotypes-so called expression quantitative trait loci. In this study, we used the PrediXcan method to predict gene expression in normal colon tissue using individual genotype data from 91 CRC patients and examined the correlation ρ between predicted and measured gene expression levels. Out of 5434 predicted genes, 58% showed a negative ρ value and only 16% presented a ρ higher than 0.10. We subsequently investigated the association between genotype-based gene expression in colon tissue for genes with ρ > 0.10 and survival of 4436 CRC patients. We identified an inverse association between the predicted expression of
and CRC-specific survival for patients with a body mass index greater than or equal to 30 kg/m
(HR (hazard ratio) = 0.66 for an expression higher vs. lower than the median,
= 0.005). This association was validated using genotype and clinical data from the UK Biobank (HR = 0.74,
= 0.04). In addition to the identification of
expression in normal colon tissue as a candidate prognostic biomarker for obese CRC patients, our study illustrates the challenges of genotype-based prediction of gene expression, and the advantage of reassessing the prediction accuracy in a subset of the study population using measured gene expression data. |
---|---|
Bibliography: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
ISSN: | 1422-0067 1661-6596 1422-0067 |
DOI: | 10.3390/ijms21218150 |