A distinct class of pan-cancer susceptibility genes revealed by an alternative polyadenylation transcriptome-wide association study.


Journal

Nature communications
ISSN: 2041-1723
Titre abrégé: Nat Commun
Pays: England
ID NLM: 101528555

Informations de publication

Date de publication:
26 Feb 2024
Historique:
received: 09 08 2023
accepted: 12 02 2024
medline: 27 2 2024
pubmed: 27 2 2024
entrez: 26 2 2024
Statut: epublish

Résumé

Alternative polyadenylation plays an important role in cancer initiation and progression; however, current transcriptome-wide association studies mostly ignore alternative polyadenylation when identifying putative cancer susceptibility genes. Here, we perform a pan-cancer 3' untranslated region alternative polyadenylation transcriptome-wide association analysis by integrating 55 well-powered (n > 50,000) genome-wide association studies datasets across 22 major cancer types with alternative polyadenylation quantification from 23,955 RNA sequencing samples across 7,574 individuals. We find that genetic variants associated with alternative polyadenylation are co-localized with 28.57% of cancer loci and contribute a significant portion of cancer heritability. We further identify 642 significant cancer susceptibility genes predicted to modulate cancer risk via alternative polyadenylation, 62.46% of which have been overlooked by traditional expression- and splicing- studies. As proof of principle validation, we show that alternative alleles facilitate 3' untranslated region lengthening of CRLS1 gene leading to increased protein abundance and promoted proliferation of breast cancer cells. Together, our study highlights the significant role of alternative polyadenylation in discovering new cancer susceptibility genes and provides a strong foundational framework for enhancing our understanding of the etiology underlying human cancers.

Identifiants

pubmed: 38409266
doi: 10.1038/s41467-024-46064-7
pii: 10.1038/s41467-024-46064-7
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

1729

Subventions

Organisme : National Natural Science Foundation of China (National Science Foundation of China)
ID : 32100533

Informations de copyright

© 2024. The Author(s).

Références

Sud, A., Kinnersley, B. & Houlston, R. S. Genome-wide association studies of cancer: current insights and future perspectives. Nat. Rev. Cancer 17, 692–704 (2017).
pubmed: 29026206
Park, S. L., Cheng, I. & Haiman, C. A. Genome-wide association studies of cancer in diverse populations. Cancer Epidemiol. Biomark. Prev. 27, 405–417 (2018).
Michailidou, K. et al. Association analysis identifies 65 new breast cancer risk loci. Nature 551, 92–94 (2017).
pubmed: 29059683 pmcid: 5798588
Schumacher, F. R. et al. Association analyses of more than 140,000 men identify 63 new prostate cancer susceptibility loci. Nat. Genet 50, 928–936 (2018).
pubmed: 29892016 pmcid: 6568012
Rashkin, S. R. et al. Pan-cancer study detects genetic risk variants and shared genetic basis in two large cohorts. Nat. Commun. 11, 4423 (2020).
pubmed: 32887889 pmcid: 7473862
Phelan, C. M. et al. Identification of 12 new susceptibility loci for different histotypes of epithelial ovarian cancer. Nat. Genet. 49, 680–691 (2017).
pubmed: 28346442 pmcid: 5612337
Al Olama, A. A. et al. A meta-analysis of 87,040 individuals identifies 23 new susceptibility loci for prostate cancer. Nat. Genet. 46, 1103–1109 (2014).
pubmed: 25217961 pmcid: 4383163
Maurano, M. T. et al. Systematic localization of common disease-associated variation in regulatory DNA. Science 337, 1190–1195 (2012).
pubmed: 22955828 pmcid: 3771521
Gamazon, E. R. et al. Using an atlas of gene regulation across 44 human tissues to inform complex disease- and trait-associated variation. Nat. Genet. 50, 956–967 (2018).
pubmed: 29955180 pmcid: 6248311
Barbeira, A. N. et al. Exploiting the GTEx resources to decipher the mechanisms at GWAS loci. Genome Biol. 22, 49 (2021).
pubmed: 33499903 pmcid: 7836161
Zhu, Z. et al. Integration of summary data from GWAS and eQTL studies predicts complex trait gene targets. Nat. Genet. 48, 481–487 (2016).
pubmed: 27019110
Walker, R. L. et al. Genetic control of expression and splicing in developing human brain informs disease mechanisms. Cell 179, 750–771 e22 (2019).
pubmed: 31626773 pmcid: 8963725
Qi, T. et al. Genetic control of RNA splicing and its distinct role in complex trait variation. Nat. Genet 54, 1355–1363 (2022).
pubmed: 35982161 pmcid: 9470536
Yao, D. W., O’Connor, L. J., Price, A. L. & Gusev, A. Quantifying genetic effects on disease mediated by assayed gene expression levels. Nat. Genet. 52, 626–633 (2020).
pubmed: 32424349 pmcid: 7276299
Tian, B. & Manley, J. L. Alternative polyadenylation of mRNA precursors. Nat. Rev. Mol. Cell Biol. 18, 18–30 (2017).
pubmed: 27677860
Gruber, A. J. & Zavolan, M. Alternative cleavage and polyadenylation in health and disease. Nat. Rev. Genet 20, 599–614 (2019).
pubmed: 31267064
Mitschka, S. & Mayr, C. Context-specific regulation and function of mRNA alternative polyadenylation. Nat. Rev. Mol. Cell Biol. 23, 779–796 (2022).
pubmed: 35798852 pmcid: 9261900
Mayr, C. Regulation by 3’-untranslated regions. Annu. Rev. Genet. 51, 171–194 (2017).
pubmed: 28853924
Lianoglou, S., Garg, V., Yang, J. L., Leslie, C. S. & Mayr, C. Ubiquitously transcribed genes use alternative polyadenylation to achieve tissue-specific expression. Genes Dev. 27, 2380–2396 (2013).
pubmed: 24145798 pmcid: 3828523
Masamha, C. P. et al. CFIm25 links alternative polyadenylation to glioblastoma tumour suppression. Nature 510, 412–416 (2014).
pubmed: 24814343 pmcid: 4128630
Xia, Z. et al. Dynamic analyses of alternative polyadenylation from RNA-seq reveal a 3’-UTR landscape across seven tumour types. Nat. Commun. 5, 5274 (2014).
pubmed: 25409906
Stacey, S. N. et al. A germline variant in the TP53 polyadenylation signal confers cancer susceptibility. Nat. Genet 43, 1098–1103 (2011).
pubmed: 21946351 pmcid: 3263694
Li, L. et al. An atlas of alternative polyadenylation quantitative trait loci contributing to complex trait and disease heritability. Nat. Genet. 53, 994–1005 (2021).
pubmed: 33986536
Consortium, G. T. The GTEx Consortium atlas of genetic regulatory effects across human tissues. Science 369, 1318–1330 (2020).
Cancer Genome Atlas Research, N. et al. The cancer genome atlas pan-cancer analysis project. Nat. Genet 45, 1113–1120 (2013).
Feng, X., Li, L., Wagner, E. J. & Li, W. TC3A: The Cancer 3’ UTR Atlas. Nucleic Acids Res. 46, D1027–D1030 (2018).
pubmed: 30053266
Shabalin, A. A. Matrix eQTL: ultra fast eQTL analysis via large matrix operations. Bioinformatics 28, 1353–1358 (2012).
pubmed: 22492648 pmcid: 3348564
Buniello, A. et al. The NHGRI-EBI GWAS Catalog of published genome-wide association studies, targeted arrays and summary statistics 2019. Nucleic Acids Res 47, D1005–D1012 (2019).
pubmed: 30445434
Bycroft, C. et al. The UK Biobank resource with deep phenotyping and genomic data. Nature 562, 203–209 (2018).
pubmed: 30305743 pmcid: 6786975
Kanai, M. et al. Genetic analysis of quantitative traits in the Japanese population links cell types to complex human diseases. Nat. Genet 50, 390–400 (2018).
pubmed: 29403010
Kurki, M. I. et al. FinnGen provides genetic insights from a well-phenotyped isolated population. Nature 613, 508–518 (2023).
pubmed: 36653562 pmcid: 9849126
Toufektchan, E. et al. Germline mutation of MDM4, a major p53 regulator, in a familial syndrome of defective telomere maintenance. Sci. Adv. 6, eaay3511 (2020).
pubmed: 32300648 pmcid: 7148086
Stacey, S. N. et al. Common variants on chromosome 5p12 confer susceptibility to estrogen receptor-positive breast cancer. Nat. Genet. 40, 703–706 (2008).
pubmed: 18438407
Bulik-Sullivan, B. K. et al. LD Score regression distinguishes confounding from polygenicity in genome-wide association studies. Nat. Genet. 47, 291–295 (2015).
pubmed: 25642630 pmcid: 4495769
Jiang, X. et al. Shared heritability and functional enrichment across six solid cancers. Nat. Commun. 10, 431 (2019).
pubmed: 30683880 pmcid: 6347624
Pickrell, J. K. Joint analysis of functional genomic data and genome-wide association studies of 18 human traits. Am. J. Hum. Genet. 94, 559–573 (2014).
pubmed: 24702953 pmcid: 3980523
Finucane, H. K. et al. Partitioning heritability by functional annotation using genome-wide association summary statistics. Nat. Genet. 47, 1228–1235 (2015).
pubmed: 26414678 pmcid: 4626285
Gazal, S. et al. Linkage disequilibrium-dependent architecture of human complex traits shows action of negative selection. Nat. Genet. 49, 1421–1427 (2017).
pubmed: 28892061 pmcid: 6133304
Giambartolomei, C. et al. Bayesian test for colocalisation between pairs of genetic association studies using summary statistics. PLoS Genet 10, e1004383 (2014).
pubmed: 24830394 pmcid: 4022491
de Goede, O. M. et al. Population-scale tissue transcriptomics maps long non-coding RNAs to complex disease. Cell 184, 2633–2648 e19 (2021).
pubmed: 33864768 pmcid: 8651477
Galluzzi, L. et al. Mitochondrial gateways to cancer. Mol. Asp. Med. 31, 1–20 (2010).
Gusev, A. et al. Integrative approaches for large-scale transcriptome-wide association studies. Nat. Genet. 48, 245–252 (2016).
pubmed: 26854917 pmcid: 4767558
Li, Y. I., Wong, G., Humphrey, J. & Raj, T. Prioritizing Parkinson’s disease genes using population-scale transcriptomic data. Nat. Commun. 10, 994 (2019).
pubmed: 30824768 pmcid: 6397174
Lindstrom, S. et al. Genome-wide association study identifies multiple loci associated with both mammographic density and breast cancer risk. Nat. Commun. 5, 5303 (2014).
pubmed: 25342443
Stupack, D. G. Caspase-8 as a therapeutic target in cancer. Cancer Lett. 332, 133–140 (2013).
pubmed: 20817393
Muller, I. et al. Cancer cells employ nuclear caspase-8 to overcome the p53-dependent G2/M checkpoint through cleavage of USP28. Mol. Cell 77, 970–984 e7 (2020).
pubmed: 31982308 pmcid: 7060810
Meyers, R. M. et al. Computational correction of copy number effect improves specificity of CRISPR-Cas9 essentiality screens in cancer cells. Nat. Genet 49, 1779–1784 (2017).
pubmed: 29083409 pmcid: 5709193
Dhanasekaran, R. et al. The MYC oncogene - the grand orchestrator of cancer growth and immune evasion. Nat. Rev. Clin. Oncol. 19, 23–36 (2022).
pubmed: 34508258
McDonald, E. R. 3rd et al. Project DRIVE: a compendium of cancer dependencies and synthetic lethal relationships uncovered by large-scale, deep rnai screening. Cell 170, 577–592 e10 (2017).
pubmed: 28753431
Gong, J. et al. PancanQTL: systematic identification of cis-eQTLs and trans-eQTLs in 33 cancer types. Nucleic Acids Res. 46, D971–D976 (2018).
pubmed: 29036324
Tian, J. et al. CancerSplicingQTL: a database for genome-wide identification of splicing QTLs in human cancer. Nucleic Acids Res. 47, D909–D916 (2019).
pubmed: 30329095
Yao, C. et al. Transcriptome-wide analyses of CstF64-RNA interactions in global regulation of mRNA alternative polyadenylation. Proc. Natl Acad. Sci. USA 109, 18773–18778 (2012).
pubmed: 23112178 pmcid: 3503179
Bailey, M. H. et al. Comprehensive characterization of cancer driver genes and mutations. Cell 173, 371–385 e18 (2018).
pubmed: 29625053 pmcid: 6029450
Dietlein, F. et al. Identification of cancer driver genes based on nucleotide context. Nat. Genet. 52, 208–218 (2020).
pubmed: 32015527 pmcid: 7031046
Lyu, J. et al. DORGE: Discovery of Oncogenes and tumoR suppressor genes using Genetic and Epigenetic features. Sci. Adv. 6, eaba6784 (2020).
pubmed: 33177077 pmcid: 7673741
Sondka, Z. et al. The COSMIC Cancer Gene Census: describing genetic dysfunction across all human cancers. Nat. Rev. Cancer 18, 696–705 (2018).
pubmed: 30293088 pmcid: 6450507
Liberzon, A. et al. The Molecular Signatures Database (MSigDB) hallmark gene set collection. Cell Syst. 1, 417–425 (2015).
pubmed: 26771021 pmcid: 4707969
Szklarczyk, D. et al. Correction to ‘The STRING database in 2021: customizable protein-protein networks, and functional characterization of user-uploaded gene/measurement sets’. Nucleic Acids Res. 49, 10800 (2021).
pubmed: 34530444 pmcid: 8501959
Sneeggen, M., Guadagno, N. A. & Progida, C. Intracellular transport in cancer metabolic reprogramming. Front Cell Dev. Biol. 8, 597608 (2020).
pubmed: 33195279 pmcid: 7661548
Liu, Z. G. & Jiao, D. Necroptosis, tumor necrosis and tumorigenesis. Cell Stress 4, 1–8 (2019).
pubmed: 31922095 pmcid: 6946014
Xiang, Y. et al. Comprehensive characterization of alternative polyadenylation in human cancer. J. Natl Cancer Inst. 110, 379–389 (2018).
pubmed: 29106591
Mayr, C. & Bartel, D. P. Widespread shortening of 3’UTRs by alternative cleavage and polyadenylation activates oncogenes in cancer cells. Cell 138, 673–684 (2009).
pubmed: 19703394 pmcid: 2819821
Rieger, M. A. et al. CLIP and Massively parallel functional analysis of CELF6 reveal a role in destabilizing synaptic gene mrnas through interaction with 3’ UTR elements. Cell Rep. 33, 108531 (2020).
pubmed: 33357440 pmcid: 7780154
Chen, H. F., Hsu, C. M. & Huang, Y. S. CPEB2-dependent translation of long 3’-UTR Ucp1 mRNA promotes thermogenesis in brown adipose tissue. EMBO J. 37, e99071 (2018).
pubmed: 30177570 pmcid: 6187220
Tang, H. W. et al. The TORC1-regulated CPA complex rewires an RNA processing network to drive autophagy and metabolic reprogramming. Cell Metab. 27, 1040–1054 e8 (2018).
pubmed: 29606597 pmcid: 6100782
Zhu, Y. et al. Molecular mechanisms for CFIm-mediated regulation of mRNA alternative polyadenylation. Mol. Cell 69, 62–74 e4 (2018).
pubmed: 29276085
Day, F. R. et al. Large-scale genomic analyses link reproductive aging to hypothalamic signaling, breast cancer susceptibility and BRCA1-mediated DNA repair. Nat. Genet. 47, 1294–1303 (2015).
pubmed: 26414677 pmcid: 4661791
O’Mara, T. A. et al. Identification of nine new susceptibility loci for endometrial cancer. Nat. Commun. 9, 3166 (2018).
pubmed: 30093612 pmcid: 6085317
Zhao, H. et al. CrossMap: a versatile tool for coordinate conversion between genome assemblies. Bioinformatics 30, 1006–1007 (2014).
pubmed: 24351709
Purcell, S. et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am. J. Hum. Genet 81, 559–575 (2007).
pubmed: 17701901 pmcid: 1950838
Krzywinski, M. et al. Circos: an information aesthetic for comparative genomics. Genome Res. 19, 1639–1645 (2009).
pubmed: 19541911 pmcid: 2752132
Wang, K., Li, M. & Hakonarson, H. ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data. Nucleic Acids Res. 38, e164 (2010).
pubmed: 20601685 pmcid: 2938201
Watanabe, K. et al. A global overview of pleiotropy and genetic architecture in complex traits. Nat. Genet 51, 1339–1348 (2019).
pubmed: 31427789
Schaid, D. J., Chen, W. & Larson, N. B. From genome-wide associations to candidate causal variants by statistical fine-mapping. Nat. Rev. Genet 19, 491–504 (2018).
pubmed: 29844615 pmcid: 6050137
Kichaev, G. et al. Improved methods for multi-trait fine mapping of pleiotropic risk loci. Bioinformatics 33, 248–255 (2017).
pubmed: 27663501
Chen, W. et al. Fine mapping causal variants with an approximate bayesian method using marginal test statistics. Genetics 200, 719–736 (2015).
pubmed: 25948564 pmcid: 4512539
Benner, C. et al. FINEMAP: efficient variable selection using summary data from genome-wide association studies. Bioinformatics 32, 1493–1501 (2016).
pubmed: 26773131 pmcid: 4866522
Zheng, J. et al. LD Hub: a centralized database and web interface to perform LD score regression that maximizes the potential of summary level GWAS data for SNP heritability and genetic correlation analysis. Bioinformatics 33, 272–279 (2017).
pubmed: 27663502
Stegle, O., Parts, L., Piipari, M., Winn, J. & Durbin, R. Using probabilistic estimation of expression residuals (PEER) to obtain increased power and interpretability of gene expression analyses. Nat. Protoc. 7, 500–507 (2012).
pubmed: 22343431 pmcid: 3398141
Wallace, C. Eliciting priors and relaxing the single causal variant assumption in colocalisation analyses. PLoS Genet 16, e1008720 (2020).
pubmed: 32310995 pmcid: 7192519
Pruim, R. J. et al. LocusZoom: regional visualization of genome-wide association scan results. Bioinformatics 26, 2336–2337 (2010).
pubmed: 20634204 pmcid: 2935401
Barbeira, A. N. et al. Exploring the phenotypic consequences of tissue specific gene expression variation inferred from GWAS summary statistics. Nat. Commun. 9, 1825 (2018).
pubmed: 29739930 pmcid: 5940825
Chen, M. et al. 3’ UTR lengthening as a novel mechanism in regulating cellular senescence. Genome Res 28, 285–294 (2018).
pubmed: 29440281 pmcid: 5848608
Zhao, Z. et al. Comprehensive characterization of somatic variants associated with intronic polyadenylation in human cancers. Nucleic Acids Res. 49, 10369–10381 (2021).
pubmed: 34508351 pmcid: 8501991
Sherman, B. T. et al. DAVID: a web server for functional enrichment analysis and functional annotation of gene lists (2021 update). Nucleic Acids Res. 50, W216–W221 (2022).
pubmed: 35325185 pmcid: 9252805
Szklarczyk, D. et al. The STRING database in 2023: protein-protein association networks and functional enrichment analyses for any sequenced genome of interest. Nucleic Acids Res. 51, D638–D646 (2023).
pubmed: 36370105
Shannon, P. et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 13, 2498–2504 (2003).
pubmed: 14597658 pmcid: 403769

Auteurs

Hui Chen (H)

Institute of Systems and Physical Biology, Shenzhen Bay Laboratory, Shenzhen, 518055, China.

Zeyang Wang (Z)

Institute of Systems and Physical Biology, Shenzhen Bay Laboratory, Shenzhen, 518055, China.

Lihai Gong (L)

Institute of Systems and Physical Biology, Shenzhen Bay Laboratory, Shenzhen, 518055, China.

Qixuan Wang (Q)

Institute of Systems and Physical Biology, Shenzhen Bay Laboratory, Shenzhen, 518055, China.

Wenyan Chen (W)

Institute of Systems and Physical Biology, Shenzhen Bay Laboratory, Shenzhen, 518055, China.

Jia Wang (J)

Institute of Molecular Physiology, Shenzhen Bay Laboratory, Shenzhen, 518055, China.

Xuelian Ma (X)

Institute of Systems and Physical Biology, Shenzhen Bay Laboratory, Shenzhen, 518055, China.

Ruofan Ding (R)

Institute of Systems and Physical Biology, Shenzhen Bay Laboratory, Shenzhen, 518055, China.

Xing Li (X)

Institute of Systems and Physical Biology, Shenzhen Bay Laboratory, Shenzhen, 518055, China.

Xudong Zou (X)

Institute of Systems and Physical Biology, Shenzhen Bay Laboratory, Shenzhen, 518055, China.

Mireya Plass (M)

Gene Regulation of Cell Identity Group, Regenerative Medicine Program, Bellvitge Institute for Biomedical Research (IDIBELL), L'Hospitalet de Llobregat, Barcelona, 08908, Spain.
Program for Advancing Clinical Translation of Regenerative Medicine of Catalonia, P-CMR[C], L'Hospitalet de Llobregat, Barcelona, 08908, Spain.
Center for Networked Biomedical Research on Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Madrid, 28029, Spain.

Cheng Lian (C)

Department of Biochemistry and Molecular Biology of School of Basic Medical Sciences, Shanghai Medical College of Fudan University, Shanghai, 200032, China.

Ting Ni (T)

State Key Laboratory of Genetic Engineering, Collaborative Innovation Center of Genetics and Development, Human Phenome Institute, School of Life Sciences and Huashan Hospital, Fudan University, Shanghai, 200438, China.

Gong-Hong Wei (GH)

Department of Biochemistry and Molecular Biology of School of Basic Medical Sciences, Shanghai Medical College of Fudan University, Shanghai, 200032, China.
Disease Networks Research Unit, Faculty of Biochemistry and Molecular Medicine & Biocenter Oulu, University of Oulu, Oulu, 90410, Finland.

Wei Li (W)

Division of Computational Biomedicine, Department of Biological Chemistry, School of Medicine, The University of California, Irvine, CA, 92697, USA. wei.li@uci.edu.

Lin Deng (L)

Institute of Molecular Physiology, Shenzhen Bay Laboratory, Shenzhen, 518055, China. denglin@szbl.ac.cn.

Lei Li (L)

Institute of Systems and Physical Biology, Shenzhen Bay Laboratory, Shenzhen, 518055, China. lei.li@szbl.ac.cn.

Classifications MeSH