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Multiple Gene-Environment Interactions on the Angiogenesis Gene-Pathway Impact Rectal Cancer Risk and Survival

Author

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  • Noha Sharafeldin

    (School of Public Health, University of Alberta, Edmonton, AB T6G 2R3, Canada
    Department of Medicine, School of Medicine, University of Alabama at Birmingham, Birmingham, AL 35294, USA)

  • Martha L. Slattery

    (Department of Internal Medicine, University of Utah Health Sciences Center, Salt Lake City, UT 84132, USA)

  • Qi Liu

    (School of Public Health, University of Alberta, Edmonton, AB T6G 2R3, Canada)

  • Conrado Franco-Villalobos

    (School of Public Health, University of Alberta, Edmonton, AB T6G 2R3, Canada)

  • Bette J. Caan

    (Division of Research, Kaiser Permanente Medical Care Program, Oakland, CA 94612, USA)

  • John D. Potter

    (Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA
    Department of Epidemiology, School of Public Health, University of Washington, Seattle, WA 98195, USA
    Centre for Public Health Research, Massey University, P.O. Box 756, Wellington 6140, New Zealand)

  • Yutaka Yasui

    (School of Public Health, University of Alberta, Edmonton, AB T6G 2R3, Canada
    Department of Epidemiology & Cancer Control, St. Jude Children’s Research Hospital, Memphis, TN 38105, USA)

Abstract

Characterization of gene-environment interactions (GEIs) in cancer is limited. We aimed at identifying GEIs in rectal cancer focusing on a relevant biologic process involving the angiogenesis pathway and relevant environmental exposures: cigarette smoking, alcohol consumption, and animal protein intake. We analyzed data from 747 rectal cancer cases and 956 controls from the Diet, Activity and Lifestyle as a Risk Factor for Rectal Cancer study. We applied a 3-step analysis approach: first, we searched for interactions among single nucleotide polymorphisms on the pathway genes; second, we searched for interactions among the genes, both steps using Logic regression; third, we examined the GEIs significant at the 5% level using logistic regression for cancer risk and Cox proportional hazards models for survival. Permutation-based test was used for multiple testing adjustment. We identified 8 significant GEIs associated with risk among 6 genes adjusting for multiple testing: TNF (OR = 1.85, 95% CI: 1.10, 3.11), TLR4 (OR = 2.34, 95% CI: 1.38, 3.98), and EGR2 (OR = 2.23, 95% CI: 1.04, 4.78) with smoking; IGF1R (OR = 1.69, 95% CI: 1.04, 2.72), TLR4 (OR = 2.10, 95% CI: 1.22, 3.60) and EGR2 (OR = 2.12, 95% CI: 1.01, 4.46) with alcohol; and PDGFB (OR = 1.75, 95% CI: 1.04, 2.92) and MMP1 (OR = 2.44, 95% CI: 1.24, 4.81) with protein. Five GEIs were associated with survival at the 5% significance level but not after multiple testing adjustment: CXCR1 (HR = 2.06, 95% CI: 1.13, 3.75) with smoking; and KDR (HR = 4.36, 95% CI: 1.62, 11.73), TLR2 (HR = 9.06, 95% CI: 1.14, 72.11), EGR2 (HR = 2.45, 95% CI: 1.42, 4.22), and EGFR (HR = 6.33, 95% CI: 1.95, 20.54) with protein. GEIs between angiogenesis genes and smoking, alcohol, and animal protein impact rectal cancer risk. Our results support the importance of considering the biologic hypothesis to characterize GEIs associated with cancer outcomes.

Suggested Citation

  • Noha Sharafeldin & Martha L. Slattery & Qi Liu & Conrado Franco-Villalobos & Bette J. Caan & John D. Potter & Yutaka Yasui, 2017. "Multiple Gene-Environment Interactions on the Angiogenesis Gene-Pathway Impact Rectal Cancer Risk and Survival," IJERPH, MDPI, vol. 14(10), pages 1-15, September.
    • Handle: RePEc:gam:jijerp:v:14:y:2017:i:10:p:1146-:d:113512
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    References listed on IDEAS

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    1. James Y. Dai & Charles Kooperberg & Michael Leblanc & Ross L. Prentice, 2012. "Two-stage testing procedures with independent filtering for genome-wide gene-environment interaction," Biometrika, Biometrika Trust, vol. 99(4), pages 929-944.
    2. Richard C. Grandison & Matthew D. W. Piper & Linda Partridge, 2009. "Amino-acid imbalance explains extension of lifespan by dietary restriction in Drosophila," Nature, Nature, vol. 462(7276), pages 1061-1064, December.
    3. Teri A. Manolio & Francis S. Collins & Nancy J. Cox & David B. Goldstein & Lucia A. Hindorff & David J. Hunter & Mark I. McCarthy & Erin M. Ramos & Lon R. Cardon & Aravinda Chakravarti & Judy H. Cho &, 2009. "Finding the missing heritability of complex diseases," Nature, Nature, vol. 461(7265), pages 747-753, October.
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