Interactive potential of genetic polymorphism in Xenobiotic metabolising and DNA repair genes for predicting lung cancer predisposition and overall survival in North Indians.

ABSTRACT

INTRODUCTION: Cancer, a multi-step, multifactorial and multi-gene disease, not only damages the genomic integrity of the cell but also hinders the DNA repair mechanisms of the body. Gene-gene and gene environment interactions amongst the genetic polymorphisms together modulate the susceptibility towards a cancer. We have studied the high order gene interactions between the genetic polymorphism of detoxifying genes (CYP1A1, Ahr, XRCC and GST1) that play a key role in the metabolism of the xenobiotics and have been proved to be prognostic markers for lung cancer METHODS: 237 cases and 250 controls have been genotyped using PCR-RFLP technique. In order to find out the association, unconditional logistic regression approach was used and to analyse high order interactions MDR and CART was used. RESULTS: In the MDR analysis, the best model was one factor model which included GSTM1 (CVC 10/10, Prediction error = 0.43, p < .001). The best three factor model comprised of XRCC1 632, XRCC1 206, GSTM1 (CVC 10/10, Prediction error = 0.45, p < .0001). The CART analysis exhibited that Node 1 carrying mutant type of GSTM1 imposed the highest risk towards lung cancer (OR = 11.0, 95%C.I. = 6.05-20.03, p = .000001). Wild type of GSTM1 when combined with mutant type of CYP1A1 M2 and XRCC1 632, an 8 fold risk towards lung cancer was observed (95%C.I. = 4.07-16.29, p = .00001). The high order interactions were used to predict the prognosis of lung cancer patients. Of all the genetic variants, XRCC1 632, GSTM1 and AhR rs2066853 was the most important determinant of overall survival of lung cancer patients CONCLUSION: Through the study we introduced the concept of polygenic approach to get an insight about the various polymorphic variants in determining cancer susceptibility. Lesser number of subjects were found in the high risk subgroups. Further studies with larger sample size are required to warranty the above findings.