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Developing Genetic Epidemiological Models to Predict Risk for Nasopharyngeal Carcinoma in High-Risk Population of China

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  • Hong-Lian Ruan
  • Hai-De Qin
  • Yin Yao Shugart
  • Jin-Xin Bei
  • Fu-Tian Luo
  • Yi-Xin Zeng
  • Wei-Hua Jia

Abstract

To date, the only established model for assessing risk for nasopharyngeal carcinoma (NPC) relies on the sero-status of the Epstein-Barr virus (EBV). By contrast, the risk assessment models proposed here include environmental risk factors, family history of NPC, and information on genetic variants. The models were developed using epidemiological and genetic data from a large case-control study, which included 1,387 subjects with NPC and 1,459 controls of Cantonese origin. The predictive accuracy of the models were then assessed by calculating the area under the receiver-operating characteristic curves (AUC). To compare the discriminatory improvement of models with and without genetic information, we estimated the net reclassification improvement (NRI) and integrated discrimination index (IDI). Well-established environmental risk factors for NPC include consumption of salted fish and preserved vegetables and cigarette smoking (in pack years). The environmental model alone shows modest discriminatory ability (AUC = 0.68; 95% CI: 0.66, 0.70), which is only slightly increased by the addition of data on family history of NPC (AUC = 0.70; 95% CI: 0.68, 0.72). With the addition of data on genetic variants, however, our model’s discriminatory ability rises to 0.74 (95% CI: 0.72, 0.76). The improvements in NRI and IDI also suggest the potential usefulness of considering genetic variants when screening for NPC in endemic areas. If these findings are confirmed in larger cohort and population-based case-control studies, use of the new models to analyse data from NPC-endemic areas could well lead to earlier detection of NPC.

Suggested Citation

  • Hong-Lian Ruan & Hai-De Qin & Yin Yao Shugart & Jin-Xin Bei & Fu-Tian Luo & Yi-Xin Zeng & Wei-Hua Jia, 2013. "Developing Genetic Epidemiological Models to Predict Risk for Nasopharyngeal Carcinoma in High-Risk Population of China," PLOS ONE, Public Library of Science, vol. 8(2), pages 1-8, February.
  • Handle: RePEc:plo:pone00:0056128
    DOI: 10.1371/journal.pone.0056128
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    References listed on IDEAS

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    1. A Cecile J W Janssens & John P A Ioannidis & Cornelia M van Duijn & Julian Little & Muin J Khoury & for the GRIPS Group, 2011. "Strengthening the Reporting of Genetic Risk Prediction Studies: The GRIPS Statement," PLOS Medicine, Public Library of Science, vol. 8(3), pages 1-4, March.
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    Cited by:

    1. Mariana S Silva-Alves & Rodrigo Secolin & Benilton S Carvalho & Clarissa L Yasuda & Elizabeth Bilevicius & Marina K M Alvim & Renato O Santos & Claudia V Maurer-Morelli & Fernando Cendes & Iscia Lopes, 2017. "A Prediction Algorithm for Drug Response in Patients with Mesial Temporal Lobe Epilepsy Based on Clinical and Genetic Information," PLOS ONE, Public Library of Science, vol. 12(1), pages 1-15, January.

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