Literature DB >> 27015455

Prediction of Melanoma Risk in a Southern European Population Based on a Weighted Genetic Risk Score.

Katerina P Kypreou1, Irene Stefanaki1, Kyriaki Antonopoulou1, Fani Karagianni1, Georgios Ntritsos2, Alexios Zaras1, Vasiliki Nikolaou1, Iro Kalfa3, Vasiliki Chasapi1, Dorothea Polydorou1, Helen Gogas4, George M Spyrou5, Lars Bertram6, Christina M Lill7, John P A Ioannidis8, Christina Antoniou1, Evangelos Evangelou9, Alexander I Stratigos10.   

Abstract

Many single nucleotide polymorphisms (SNPs) have been described as putative risk factors for melanoma. The aim of our study was to validate the most prominent genetic risk loci in an independent Greek melanoma case-control dataset and to assess their cumulative effect solely or combined with established phenotypic risk factors on individualized risk prediction. We genotyped 59 SNPs in 800 patients and 800 controls and tested their association with melanoma using logistic regression analyses. We constructed a weighted genetic risk score (GRSGWS) based on SNPs that showed genome-wide significant (GWS) association with melanoma in previous studies and assessed their impact on risk prediction. Fifteen independent SNPs from 12 loci were significantly associated with melanoma (P < 0.05). Risk score analysis yielded an odds ratio of 1.36 per standard deviation increase of the GRSGWS (P = 1.1 × 10(-7)). Individuals in the highest 20% of the GRSGWS had a 1.88-fold increase in melanoma risk compared with those in the middle quintile. By adding the GRSGWS to a phenotypic risk model, the C-statistic increased from 0.764 to 0.775 (P = 0.007). In summary, the GRSGWS is associated with melanoma risk and achieves a modest improvement in risk prediction when added to a phenotypic risk model.
Copyright © 2015 The Authors. Published by Elsevier Inc. All rights reserved.

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Year:  2015        PMID: 27015455     DOI: 10.1016/j.jid.2015.12.007

Source DB:  PubMed          Journal:  J Invest Dermatol        ISSN: 0022-202X            Impact factor:   8.551


  14 in total

1.  Combining common genetic variants and non-genetic risk factors to predict risk of cutaneous melanoma.

Authors:  Fangyi Gu; Ting-Huei Chen; Ruth M Pfeiffer; Maria Concetta Fargnoli; Donato Calista; Paola Ghiorzo; Ketty Peris; Susana Puig; Chiara Menin; Arcangela De Nicolo; Monica Rodolfo; Cristina Pellegrini; Lorenza Pastorino; Evangelos Evangelou; Tongwu Zhang; Xing Hua; Curt T DellaValle; D Timothy Bishop; Stuart MacGregor; Mark I Iles; Matthew H Law; Anne Cust; Kevin M Brown; Alexander J Stratigos; Eduardo Nagore; Stephen Chanock; Jianxin Shi; Melanoma Meta-Analysis Consortium; MelaNostrum Consortium; Maria Teresa Landi
Journal:  Hum Mol Genet       Date:  2018-12-01       Impact factor: 6.150

Review 2.  Genome-wide association studies and polygenic risk scores for skin cancer: clinically useful yet?

Authors:  M R Roberts; M M Asgari; A E Toland
Journal:  Br J Dermatol       Date:  2019-07-07       Impact factor: 9.302

3.  Using the Prediction Model Risk of Bias Assessment Tool (PROBAST) to Evaluate Melanoma Prediction Studies.

Authors:  Isabelle Kaiser; Sonja Mathes; Annette B Pfahlberg; Wolfgang Uter; Carola Berking; Markus V Heppt; Theresa Steeb; Katharina Diehl; Olaf Gefeller
Journal:  Cancers (Basel)       Date:  2022-06-20       Impact factor: 6.575

4.  Genomic Risk Score for Melanoma in a Prospective Study of Older Individuals.

Authors:  Andrew Bakshi; Mabel Yan; Moeen Riaz; Galina Polekhina; Suzanne G Orchard; Jane Tiller; Rory Wolfe; Amit Joshi; Yin Cao; Aideen M McInerney-Leo; Tatiane Yanes; Monika Janda; H Peter Soyer; Anne E Cust; Matthew H Law; Peter Gibbs; Catriona McLean; Andrew T Chan; John J McNeil; Victoria J Mar; Paul Lacaze
Journal:  J Natl Cancer Inst       Date:  2021-10-01       Impact factor: 11.816

5.  Dissecting the Mutational Landscape of Cutaneous Melanoma: An Omic Analysis Based on Patients from Greece.

Authors:  Georgia Kontogianni; Georgia Piroti; Ilias Maglogiannis; Aristotelis Chatziioannou; Olga Papadodima
Journal:  Cancers (Basel)       Date:  2018-03-29       Impact factor: 6.639

6.  Prediction of leprosy in the Chinese population based on a weighted genetic risk score.

Authors:  Na Wang; Zhenzhen Wang; Chuan Wang; Xi'an Fu; Gongqi Yu; Zhenhua Yue; Tingting Liu; Huimin Zhang; Lulu Li; Mingfei Chen; Honglei Wang; Guiye Niu; Dan Liu; Mingkai Zhang; Yuanyuan Xu; Yan Zhang; Jinghui Li; Zhen Li; Jiabao You; Tongsheng Chu; Furong Li; Dianchang Liu; Hong Liu; Furen Zhang
Journal:  PLoS Negl Trop Dis       Date:  2018-09-19

7.  Systematic evaluation of cancer-specific genetic risk score for 11 types of cancer in The Cancer Genome Atlas and Electronic Medical Records and Genomics cohorts.

Authors:  Zhuqing Shi; Hongjie Yu; Yishuo Wu; Xiaoling Lin; Quanwa Bao; Haifei Jia; Chelsea Perschon; David Duggan; Brian T Helfand; Siqun L Zheng; Jianfeng Xu
Journal:  Cancer Med       Date:  2019-04-09       Impact factor: 4.452

Review 8.  Fibroblast Growth Factor Receptor Signaling in Skin Cancers.

Authors:  Malgorzata Czyz
Journal:  Cells       Date:  2019-06-04       Impact factor: 6.600

9.  Assessing the Incremental Contribution of Common Genomic Variants to Melanoma Risk Prediction in Two Population-Based Studies.

Authors:  Anne E Cust; Martin Drummond; Peter A Kanetsky; Alisa M Goldstein; Jennifer H Barrett; Stuart MacGregor; Matthew H Law; Mark M Iles; Minh Bui; John L Hopper; Myriam Brossard; Florence Demenais; John C Taylor; Clive Hoggart; Kevin M Brown; Maria Teresa Landi; Julia A Newton-Bishop; Graham J Mann; D Timothy Bishop
Journal:  J Invest Dermatol       Date:  2018-06-08       Impact factor: 8.551

10.  CDKN2A/CDK4 Status in Greek Patients with Familial Melanoma and Association with Clinico-epidemiological Parameters.

Authors:  Fani Karagianni; Ching-Ni Njauw; Katerina P Kypreou; Aravela Stergiopoulou; Michaela Plaka; Dorothea Polydorou; Vasiliki Chasapi; Leontios Pappas; Ioannis A Stratigos; Gregory Champsas; Peter Panagiotou; Helen Gogas; Evangelos Evangelou; Hensin Tsao; Alexander J Stratigos; Irene Stefanaki
Journal:  Acta Derm Venereol       Date:  2018-10-10       Impact factor: 4.437

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