Literature DB >> 32732251

Pathway Analysis of Renal Cell Carcinoma Genome-Wide Association Studies Identifies Novel Associations.

Mark P Purdue1, Lei Song2, Ghislaine Scélo3, Richard S Houlston4, Xifeng Wu5, Lori C Sakoda6, Khanh Thai6, Rebecca E Graff7, Nathaniel Rothman2, Paul Brennan8, Stephen J Chanock2, Kai Yu2.   

Abstract

BACKGROUND: Much of the heritable risk of renal cell carcinoma (RCC) associated with common genetic variation is unexplained. New analytic approaches have been developed to increase the discovery of risk variants in genome-wide association studies (GWAS), including multi-locus testing through pathway analysis.
METHODS: We conducted a pathway analysis using GWAS summary data from six previous scans (10,784 cases and 20,406 controls) and evaluated 3,678 pathways and gene sets drawn from the Molecular Signatures Database. To replicate findings, we analyzed GWAS summary data from the UK Biobank (903 cases and 451,361 controls) and the Genetic Epidemiology Research on Adult Health and Aging cohort (317 cases and 50,511 controls).
RESULTS: We identified 14 pathways/gene sets associated with RCC in both the discovery (P < 1.36 × 10-5, the Bonferroni correction threshold) and replication (P < 0.05) sets, 10 of which include components of the PI3K/AKT pathway. In tests across 2,035 genes in these pathways, associations (Bonferroni corrected P < 2.46 × 10-5 in discovery and replication sets combined) were observed for CASP9, TIPIN, and CDKN2C. The strongest SNP signal was for rs12124078 (P Discovery = 2.6 × 10-5; P Replication = 1.5 × 10-4; P Combined = 6.9 × 10-8), a CASP9 expression quantitative trait locus.
CONCLUSIONS: Our pathway analysis implicates genetic variation within the PI3K/AKT pathway as a source of RCC heritability and identifies several promising novel susceptibility genes, including CASP9, which warrant further investigation. IMPACT: Our findings illustrate the value of pathway analysis as a complementary approach to analyzing GWAS data. ©2020 American Association for Cancer Research.

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Year:  2020        PMID: 32732251      PMCID: PMC9438507          DOI: 10.1158/1055-9965.EPI-20-0472

Source DB:  PubMed          Journal:  Cancer Epidemiol Biomarkers Prev        ISSN: 1055-9965            Impact factor:   4.090


  38 in total

1.  Family history and the risk of kidney cancer: a multicenter case-control study in Central Europe.

Authors:  Rayjean J Hung; Lee Moore; Paolo Boffetta; Bing-Jian Feng; Jorge R Toro; Nathanial Rothman; David Zaridze; Marie Navratilova; Vladimir Bencko; Vladimir Janout; Helena Kollarova; Neonila Szeszenia-Dabrowska; Dana Mates; Wong-Ho Chow; Paul Brennan
Journal:  Cancer Epidemiol Biomarkers Prev       Date:  2007-06       Impact factor: 4.254

2.  Conditional eQTL analysis reveals allelic heterogeneity of gene expression.

Authors:  Rick Jansen; Jouke-Jan Hottenga; Michel G Nivard; Abdel Abdellaoui; Bram Laport; Eco J de Geus; Fred A Wright; Brenda W J H Penninx; Dorret I Boomsma
Journal:  Hum Mol Genet       Date:  2017-04-15       Impact factor: 6.150

3.  Exploring regulation in tissues with eQTL networks.

Authors:  Maud Fagny; Joseph N Paulson; Marieke L Kuijjer; Abhijeet R Sonawane; Cho-Yi Chen; Camila M Lopes-Ramos; Kimberly Glass; John Quackenbush; John Platig
Journal:  Proc Natl Acad Sci U S A       Date:  2017-08-29       Impact factor: 11.205

4.  Gene expression profiling of childhood adrenocortical tumors.

Authors:  Alina Nico West; Geoffrey A Neale; Stanley Pounds; Bonald C Figueredo; Carlos Rodriguez Galindo; Mara Albonei D Pianovski; Antonio G Oliveira Filho; David Malkin; Enzo Lalli; Raul Ribeiro; Gerard P Zambetti
Journal:  Cancer Res       Date:  2007-01-15       Impact factor: 12.701

5.  Network modeling links breast cancer susceptibility and centrosome dysfunction.

Authors:  Miguel Angel Pujana; Jing-Dong J Han; Lea M Starita; Kristen N Stevens; Muneesh Tewari; Jin Sook Ahn; Gad Rennert; Víctor Moreno; Tomas Kirchhoff; Bert Gold; Volker Assmann; Wael M Elshamy; Jean-François Rual; Douglas Levine; Laura S Rozek; Rebecca S Gelman; Kristin C Gunsalus; Roger A Greenberg; Bijan Sobhian; Nicolas Bertin; Kavitha Venkatesan; Nono Ayivi-Guedehoussou; Xavier Solé; Pilar Hernández; Conxi Lázaro; Katherine L Nathanson; Barbara L Weber; Michael E Cusick; David E Hill; Kenneth Offit; David M Livingston; Stephen B Gruber; Jeffrey D Parvin; Marc Vidal
Journal:  Nat Genet       Date:  2007-10-07       Impact factor: 38.330

6.  An atlas of genetic associations in UK Biobank.

Authors:  Oriol Canela-Xandri; Konrad Rawlik; Albert Tenesa
Journal:  Nat Genet       Date:  2018-10-22       Impact factor: 38.330

7.  Regulation of cell death protease caspase-9 by phosphorylation.

Authors:  M H Cardone; N Roy; H R Stennicke; G S Salvesen; T F Franke; E Stanbridge; S Frisch; J C Reed
Journal:  Science       Date:  1998-11-13       Impact factor: 47.728

8.  Characterization of the ZBTB42 gene in humans and mice.

Authors:  Stephanie A Devaney; Suzanne E Mate; Joseph M Devaney; Eric P Hoffman
Journal:  Hum Genet       Date:  2010-12-31       Impact factor: 4.132

Review 9.  Bioinformatics challenges for genome-wide association studies.

Authors:  Jason H Moore; Folkert W Asselbergs; Scott M Williams
Journal:  Bioinformatics       Date:  2010-01-06       Impact factor: 6.937

10.  A Powerful Procedure for Pathway-Based Meta-analysis Using Summary Statistics Identifies 43 Pathways Associated with Type II Diabetes in European Populations.

Authors:  Han Zhang; William Wheeler; Paula L Hyland; Yifan Yang; Jianxin Shi; Nilanjan Chatterjee; Kai Yu
Journal:  PLoS Genet       Date:  2016-06-30       Impact factor: 5.917

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  4 in total

Review 1.  The Role of Artificial Intelligence in the Diagnosis and Prognosis of Renal Cell Tumors.

Authors:  Matteo Giulietti; Monia Cecati; Berina Sabanovic; Andrea Scirè; Alessia Cimadamore; Matteo Santoni; Rodolfo Montironi; Francesco Piva
Journal:  Diagnostics (Basel)       Date:  2021-01-30

2.  Genomic Fabric Remodeling in Metastatic Clear Cell Renal Cell Carcinoma (ccRCC): A New Paradigm and Proposal for a Personalized Gene Therapy Approach.

Authors:  Dumitru A Iacobas; Victoria E Mgbemena; Sanda Iacobas; Kareena M Menezes; Huichen Wang; Premkumar B Saganti
Journal:  Cancers (Basel)       Date:  2020-12-08       Impact factor: 6.639

3.  Frequency of pathogenic germline variants in cancer susceptibility genes in 1336 renal cell carcinoma cases.

Authors:  Bryndis Yngvadottir; Avgi Andreou; Laia Bassaganyas; Alexey Larionov; Alex J Cornish; Daniel Chubb; Charlie N Saunders; Philip S Smith; Huairen Zhang; Yasemin Cole; Genomics England Research Consortium; James Larkin; Lisa Browning; Samra Turajlic; Kevin Litchfield; Richard S Houlston; Eamonn R Maher
Journal:  Hum Mol Genet       Date:  2022-08-25       Impact factor: 5.121

4.  Genetic Analysis Implicates Dysregulation of SHANK2 in Renal Cell Carcinoma Progression.

Authors:  Chi-Fen Chang; Shu-Pin Huang; Yu-Mei Hsueh; Jiun-Hung Geng; Chao-Yuan Huang; Bo-Ying Bao
Journal:  Int J Environ Res Public Health       Date:  2022-09-30       Impact factor: 4.614

  4 in total

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