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It is evident that gene-gene interactions are pervasive in the determination of the susceptibility of human diseases. Polymorphisms in nucleotide excision repair pathway (NER) genes can cause variations in the repair capacity and therefore, might lead to increase in susceptibility towards lung cancer through complex gene-gene and gene-smoking interactions. Logistic regression analysis, along with high order genetic interaction were analyzed using data mining tools such as multifactor dimensionality reduction (MDR) and classification and regression tree analysis (CART). Overall, a protective effect was reported when a combinatorial effect of SNPs were studied by applying logistic regression analysis. Multifactor dimensionality reduction (MDR) analysis, revealed that the four factor model i.e. XPC K939Q, XPA 5'UTR, XPG F670W and XPG D1104H had the best ability to predict lung cancer risk (CVC = 100, p < 0.0001). While a two factor model, including smoking and XPG F670W suggested smoking was associated with the risk of developing lung cancer (CVC = 100, p < 0.0001). Individually XPG F670W was identified as the primary risk factor. In classification and regression tree analysis (CART), we observed a 6-fold risk for SCLC patients carrying XPA 5'UTR (M), XPD K751Q (W) (
2.40-16.01, p = 0.0001).Polymorphic NER genes might jointly modulate lung cancer risk through gene-gene and gene-smoking interaction.
This article was published in the following journal.
Name: Mutation research
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