Genotype-Based Gene Expression in Colon Tissue-Prediction Accuracy and Relationship with the Prognosis of Colorectal Cancer Patients
Heike Deutelmoser, Justo Lorenzo Bermejo, Axel Benner, Korbinian Weigl, Hanla A Park, Mariam Haffa, Esther Herpel, Martin Schneider, Cornelia M Ulrich, Michael Hoffmeister, Jenny Chang-Claude, Hermann Brenner, Dominique Scherer, Heike Deutelmoser, Justo Lorenzo Bermejo, Axel Benner, Korbinian Weigl, Hanla A Park, Mariam Haffa, Esther Herpel, Martin Schneider, Cornelia M Ulrich, Michael Hoffmeister, Jenny Chang-Claude, Hermann Brenner, Dominique Scherer
Abstract
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 ARID3B and CRC-specific survival for patients with a body mass index greater than or equal to 30 kg/m2 (HR (hazard ratio) = 0.66 for an expression higher vs. lower than the median, p = 0.005). This association was validated using genotype and clinical data from the UK Biobank (HR = 0.74, p = 0.04). In addition to the identification of ARID3B 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.
Keywords: PrediXcan; colorectal cancer; genotype-based gene expression; survival.
Conflict of interest statement
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
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References
- Bray F., Ferlay J., Soerjomataram I., Siegel R.L., Torre L.A., Jemal A. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J. Clin. 2018;68:394–424. doi: 10.3322/caac.21492.
- Ferlay J., Soerjomataram I., Dikshit R., Eser S., Mathers C., Rebelo M., Parkin D.M., Forman D., Bray F. Cancer incidence and mortality worldwide: Sources, methods and major patterns in GLOBOCAN 2012. Int. J. Cancer. 2015;136:E359–E386. doi: 10.1002/ijc.29210.
- Brenner H., Chen C. The colorectal cancer epidemic: Challenges and opportunities for primary, secondary and tertiary prevention. Br. J. Cancer. 2018;119:785–792. doi: 10.1038/s41416-018-0264-x.
- Song N., Kim K., Shin A., Park J.W., Chang H.J., Shi J., Cai Q., Kim D.Y., Zheng W., Oh J.H. Colorectal cancer susceptibility loci and influence on survival. Genes. Chromosomes Cancer. 2018;57:630–637. doi: 10.1002/gcc.22674.
- Dimberg J., Shamoun L., Landerholm K., Andersson R.E., Kolodziej B., Wågsäter D. Genetic Variants of the IL2 Gene Related to Risk and Survival in Patients with Colorectal Cancer. Anticancer Res. 2019;39:4933–4940. doi: 10.21873/anticanres.13681.
- Summers M.G., Maughan T.S., Kaplan R., Law P.J., Houlston R.S., Escott-Price V., Cheadle J.P. Comprehensive analysis of colorectal cancer-risk loci and survival outcome: A prognostic role for CDH1 variants. Eur. J. Cancer. 2020;124:56–63. doi: 10.1016/j.ejca.2019.09.024.
- Jiraskova K., Hughes D.J., Brezina S., Gumpenberger T., Veskrnova V., Buchler T., Schneiderova M., Levy M., Liska V., Vodenkova S., et al. Functional Polymorphisms in DNA Repair Genes Are Associated with Sporadic Colorectal Cancer Susceptibility and Clinical Outcome. Int. J. Mol. Sci. 2018;20:97. doi: 10.3390/ijms20010097.
- Scherer D., Deutelmoser H., Balavarca Y., Toth R., Habermann N., Buck K., Kap E.J., Botma A., Seibold P., Jansen L., et al. Polymorphisms in the Angiogenesis-Related Genes EFNB2, MMP2 and JAG1 Are Associated with Survival of Colorectal Cancer Patients. Int. J. Mol. Sci. 2020;21:5395. doi: 10.3390/ijms21155395.
- Westra H.J., Franke L. From genome to function by studying eQTLs. Biochim. Biophys. Acta. 2014;1842:1896–1902. doi: 10.1016/j.bbadis.2014.04.024.
- Consortium G.T. The Genotype-Tissue Expression (GTEx) project. Nat. Genet. 2013;45:580–585. doi: 10.1038/ng.2653.
- Consortium E.P. An integrated encyclopedia of DNA elements in the human genome. Nature. 2012;489:57–74. doi: 10.1038/nature11247.
- Boyle A.P., Hong E.L., Hariharan M., Cheng Y., Schaub M.A., Kasowski M., Karczewski K.J., Park J., Hitz B.C., Weng S., et al. Annotation of functional variation in personal genomes using RegulomeDB. Genome Res. 2012;22:1790–1797. doi: 10.1101/gr.137323.112.
- Variant Effect Predictor. [(accessed on 14 September 2020)]; Available online: .
- Gamazon E.R., Wheeler H.E., Shah K.P., Mozaffari S.V., Aquino-Michaels K., Carroll R.J., Eyler A.E., Denny J.C., Consortium G.T., Nicolae D.L., et al. A gene-based association method for mapping traits using reference transcriptome data. [(accessed on 23 September 2019)];Nat. Genet. 2015 47:1091. doi: 10.1038/ng.3367. Available online: .
- Buniello A., MacArthur J.A.L., Cerezo M., Harris L.W., Hayhurst J., Malangone C., McMahon A., Morales J., Mountjoy E., Sollis E., et al. The NHGRI-EBI GWAS Catalog of published genome-wide association studies, targeted arrays and summary statistics 2019. Nucleic Acids Res. 2019;47:D1005–D1012. doi: 10.1093/nar/gky1120.
- Cherlin S., Lewis M.J., Plant D., Nair N., Goldmann K., Tzanis E., Barnes M.R., McKeigue P., Barrett J.H., Pitzalis C., et al. Investigation of genetically regulated gene expression and response to treatment in rheumatoid arthritis highlights an association between IL18RAP expression and treatment response. Ann. Rheum. Dis. 2020;79:1446–1452. doi: 10.1136/annrheumdis-2020-217204.
- Portella A.K., Papantoni A., Paquet C., Moore S., Rosch K.S., Mostofsky S., Lee R.S., Smith K.R., Levitan R., Silveira P.P., et al. Predicted DRD4 prefrontal gene expression moderates snack intake and stress perception in response to the environment in adolescents. PLoS ONE. 2020;15:e0234601. doi: 10.1371/journal.pone.0234601.
- Wheeler H.E., Ploch S., Barbeira A.N., Bonazzola R., Andaleon A., Fotuhi Siahpirani A., Saha A., Battle A., Roy S., Im H.K. Imputed gene associations identify replicable trans-acting genes enriched in transcription pathways and complex traits. Genet. Epidemiol. 2019;43:596–608. doi: 10.1002/gepi.22205.
- Hu Y., Graff M., Haessler J., Buyske S., Bien S.A., Tao R., Highland H.M., Nishimura K.K., Zubair N., Lu Y., et al. Minority-centric meta-analyses of blood lipid levels identify novel loci in the Population Architecture using Genomics and Epidemiology (PAGE) study. PLoS Genet. 2020;16:e1008684. doi: 10.1371/journal.pgen.1008684.
- Fiorica P.N., Wheeler H.E. Transcriptome association studies of neuropsychiatric traits in African Americans implicate PRMT7 in schizophrenia. PeerJ. 2019;7:e7778. doi: 10.7717/peerj.7778.
- Bien S.A., Su Y.R., Conti D.V., Harrison T.A., Qu C., Guo X., Lu Y., Albanes D., Auer P.L., Banbury B.L., et al. Genetic variant predictors of gene expression provide new insight into risk of colorectal cancer. Hum. Genet. 2019;138:307–326. doi: 10.1007/s00439-019-01989-8.
- Xia Z., Su Y.R., Petersen P., Qi L., Kim A.E., Figueiredo J.C., Lin Y., Nan H., Sakoda L.C., Albanes D., et al. Functional informed genome-wide interaction analysis of body mass index, diabetes and colorectal cancer risk. Cancer Med. 2020;9:3563–3573. doi: 10.1002/cam4.2971.
- Ioannidis N.M., Wang W., Furlotte N.A., Hinds D.A., Me Research Team. Bustamante C.D., Jorgenson E., Asgari M.M., Whittemore A.S. Gene expression imputation identifies candidate genes and susceptibility loci associated with cutaneous squamous cell carcinoma. Nat. Commun. 2018;9:4264. doi: 10.1038/s41467-018-06149-6.
- Pattee J., Zhan X., Xiao G., Pan W. Integrating germline and somatic genetics to identify genes associated with lung cancer. Genet. Epidemiol. 2020;44:233–247. doi: 10.1002/gepi.22275.
- Li B., Verma S.S., Veturi Y.C., Verma A., Bradford Y., Haas D.W., Ritchie M.D. Evaluation of PrediXcan for prioritizing GWAS associations and predicting gene expression. Pac. Symp. Biocomput. 2018;23:448–459.
- Mikhaylova A.V., Thornton T.A. Accuracy of Gene Expression Prediction From Genotype Data With PrediXcan Varies Across and Within Continental Populations. Front. Genet. 2019;10:261. doi: 10.3389/fgene.2019.00261.
- Wheeler H.E., Shah K.P., Brenner J., Garcia T., Aquino-Michaels K., Consortium G.T., Cox N.J., Nicolae D.L., Im H.K. Survey of the Heritability and Sparse Architecture of Gene Expression Traits across Human Tissues. PLoS Genet. 2016;12:e1006423. doi: 10.1371/journal.pgen.1006423.
- Deutelmoser H., Scherer T., Brenner H., Waldenberger M., Study I., Suhre K., Kastenmüller G., Lorenzo Bermejo J. Robust Huber-LASSO for improved prediction of protein, metabolite, and gene expression levels relying on individual genotype data. Brief. Bioinform. 2020 doi: 10.1093/bib/bbaa230. in press.
- Novembre J., Johnson T., Bryc K., Kutalik Z., Boyko A.R., Auton A., Indap A., King K.S., Bergmann S., Nelson M.R., et al. Genes mirror geography within Europe. Nature. 2008;456:98–101. doi: 10.1038/nature07331.
- Chirshev E., Oberg K.C., Ioffe Y.J., Unternaehrer J.J. Let-7 as biomarker, prognostic indicator, and therapy for precision medicine in cancer. Clin. Transl. Med. 2019;8:24. doi: 10.1186/s40169-019-0240-y.
- Liao T.T., Lin C.C., Jiang J.K., Yang S.H., Teng H.W., Yang M.H. Harnessing stemness and PD-L1 expression by AT-rich interaction domain-containing protein 3B in colorectal cancer. Theranostics. 2020;10:6095–6112. doi: 10.7150/thno.44147.
- Kobayashi K., Era T., Takebe A., Jakt L.M., Nishikawa S. ARID3B induces malignant transformation of mouse embryonic fibroblasts and is strongly associated with malignant neuroblastoma. Cancer Res. 2006;66:8331–8336. doi: 10.1158/0008-5472.CAN-06-0756.
- Joseph S., Deneke V.E., Cowden Dahl K.D. ARID3B induces tumor necrosis factor alpha mediated apoptosis while a novel ARID3B splice form does not induce cell death. PLoS ONE. 2012;7:e42159. doi: 10.1371/journal.pone.0042159.
- Akhavantabasi S., Sapmaz A., Tuna S., Erson-Bensan A.E. miR-125b Targets ARID3B in Breast Cancer Cells. Cell Struct. Funct. 2012;37:27–38. doi: 10.1247/csf.11025.
- Roy L., Samyesudhas S.J., Carrasco M., Li J., Joseph S., Dahl R., Cowden Dahl K.D. ARID3B increases ovarian tumor burden and is associated with a cancer stem cell gene signature. Oncotarget. 2014;5:8355. doi: 10.18632/oncotarget.2247.
- Lilla C., Verla-Tebit E., Risch A., Jäger B., Hoffmeister M., Brenner H., Chang-Claude J. Effect of NAT1 and NAT2 Genetic Polymorphisms on Colorectal Cancer Risk Associated with Exposure to Tobacco Smoke and Meat Consumption. Cancer Epidemiol. Biomark. Prev. 2006;15:99–107. doi: 10.1158/1055-9965.EPI-05-0618.
- Brenner H., Chang-Claude J., Seiler C.M., Sturmer T., Hoffmeister M. Does a negative screening colonoscopy ever need to be repeated? Gut. 2006;55:1145–1150. doi: 10.1136/gut.2005.087130.
- Peters U., Jiao S., Schumacher F.R., Hutter C.M., Aragaki A.K., Baron J.A., Berndt S.I., Bezieau S., Brenner H., Butterbach K., et al. Identification of Genetic Susceptibility Loci for Colorectal Tumors in a Genome-Wide Meta-analysis. Gastroenterology. 2013;144:799–807.e24. doi: 10.1053/j.gastro.2012.12.020.
- Ulrich C.M., Gigic B., Bohm J., Ose J., Viskochil R., Schneider M., Colditz G.A., Figueiredo J.C., Grady W.M., Li C.I., et al. The ColoCare Study: A Paradigm of Transdisciplinary Science in Colorectal Cancer Outcomes. Cancer Epidemiol. Prev. Biomark. 2019;28:591–601. doi: 10.1158/1055-9965.EPI-18-0773.
- Haffa M., Holowatyj A.N., Kratz M., Toth R., Benner A., Gigic B., Habermann N., Schrotz-King P., Bohm J., Brenner H., et al. Transcriptome Profiling of Adipose Tissue Reveals Depot-Specific Metabolic Alterations Among Patients with Colorectal Cancer. J. Clin. Endocrinol. Metab. 2019;104:5225–5237. doi: 10.1210/jc.2019-00461.
- Sudlow C., Gallacher J., Allen N., Beral V., Burton P., Danesh J., Downey P., Elliott P., Green J., Landray M., et al. UK Biobank: An Open Access Resource for Identifying the Causes of a Wide Range of Complex Diseases of Middle and Old Age. PLoS Med. 2015;12:e1001779. doi: 10.1371/journal.pmed.1001779.
- Bycroft C., Freeman C., Petkova D., Band G., Elliott L.T., Sharp K., Motyer A., Vukcevic D., Delaneau O., O’Connell J., et al. The UK Biobank resource with deep phenotyping and genomic data. Nature. 2018;562:203–209. doi: 10.1038/s41586-018-0579-z.
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