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Higher order genes interaction in DNA repair and cytokine genes polymorphism and risk to lung cancer in North Indians

1 Department of Biochemistry, University of Allahabad, Allahabad, Uttar Pradesh, India
2 Department of Zoology, University of Lucknow, Lucknow, Uttar Pradesh, India
3 Department of Radiation Oncology, Kamala Nehru Memorial Hospital, Allahabad, Uttar Pradesh, India
4 Narayana Translational Research Centre, Narayana Medical College, Nellore, Andhra Pradesh, India
5 Department of Biological Sciences, Cleveland State University, Cleveland, Ohio, United States of America

Correspondence Address:
Munish Kumar,
Assistant Professor, Department of Biochemistry, University of Allahabad, Allahabad - 211 002, Uttar Pradesh
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Source of Support: None, Conflict of Interest: None

DOI: 10.4103/jcrt.JCRT_51_20

Context: Lung cancer pathological process involves cumulative effects exerted by gene polymorphism(s), epigenetic modifications, and alterations in DNA repair machinery. Further, DNA damage due to oxidative stress, chronic inflammation, and the interplay between genetic and environmental factors is also an etiologic milieu of this malignant disease. Aims: The present study aims to assess the prognostic value of DNA repair, cytokines, and GST gene polymorphism in lung cancer patients who had not received any neoadjuvant therapy. Materials and Methods: In this case–control study, 127 cases and 120 controls were enrolled. DNA from the blood samples of both patients and controls was used to genotype XRCC1Arg399Gln, XPDLys751Gln, and interleukin-1 (IL-1β) genes by polymerase chain reaction (PCR)-restriction fragment length polymorphism method, whereas multiplex PCR was performed to genotype GSTT1 and GSTM1. Results: Binary logistic regression analysis showed that XRCC1Arg399Gln-mutant genotype (Gln/Gln, odds ratio [OR] = 4.6, 95% confidence interval [CI]: 2.2–9.6) and GSTT1 null (OR = 2.7, 95% CI: 1.6–4.5) were linked to cancer susceptibility. Generalized multidimensional reduction analysis of higher order gene–gene interaction using cross-validation testing (CVT) accuracy showed that GSTT1 (CVT 0.62, P = 0.001), XPD751 and IL- (CVT 0.6, P = 0.001), and XRCC1399, XPD751, and interleukin-1 receptor antagonists (IL-1RN) (CVT 0.98, P = 0.001) were single-, two-, and three-factor best model predicted, respectively, for lung cancer risk. Classification and regression tree analysis results showed that terminal nodes which contain XRCC1399-mutant genotype (AA) had increased the risk to lung cancer. Conclusion: The present study demonstrated that XRCC1399 (Gln/Gln), GSTT1, and IL-1RN allele I, I/II served as the risk genotypes. These genes could serve as the biomarkers to predict lung cancer risk.

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