Department of Biochemistry, All India Institute of Medical Sciences (AIIMS), Rajkot, Gujarat, India.
Molecular Oncology Laboratory, Cancer Biology Department, The Gujarat Cancer and Research Institute, Asarwa, Ahmedabad, Gujarat, India.
Asian Pac J Cancer Prev. 2023 Apr 1;24(4):1231-1237. doi: 10.31557/APJCP.2023.24.4.1231.
The present study investigated the association of interactions between gene polymorphisms in metabolic 'caretaker' genes (Phase I: CYP1A1, CYP2E1; Phase II: GSTM1, GSTT1), the cell cycle regulatory gene, p53, along with its negative controller, MDM-2, and the environment variable (tobacco). A nonparametric model, multifactor dimensionality reduction (MDR), was applied to analyse these interactions.
This case-control study was carried out on 242 subjects. Genomic DNA was extracted from peripheral blood lymphocytes.11 gene variants with an exposure variable (tobacco use) were analysed using MDR to identify the best locus model for gene-gene and gene-environment interactions. Statistical significance was evaluated using a 1000-fold permutation test using MDR permutation testing software (version 1.0 beta 2). The value of p<0.05 was considered statistically significant.
The best three-locus model for gene-gene interaction included two of the p53 gene polymorphisms; rs17878362 (intron 3) and rs1042522 (exon 4) and rs6413432 in the Phase I gene, CYP2E1(DraI). The three-locus model to evaluate the gene-environment interaction included two intronic polymorphisms of the p53 gene, that is, rs17878362 (intron 3) and rs1625895 (intron 6), and rs4646903 in the Phase I gene CYP1A1*2C. The interaction graphs revealed independent main effects of the tobacco and p53 polymorphism, rs1042522 (exon 4), and a significant additive interaction effect between rs17878362 (intron 3) and rs1042522 (exon 4).
The nonparametric approach highlighted the potential role of tobacco use and variations in the p53 gene as significant contributors to oral cancer risk. The findings of the present study will help implement preventive strategies in both tobacco use and screening using a molecular pathology approach.
本研究调查了代谢“管家”基因(I 期:CYP1A1、CYP2E1;II 期:GSTM1、GSTT1)、细胞周期调节基因 p53 及其负调控因子 MDM-2 中的基因多态性与环境变量(烟草)之间相互作用的关系。采用非参数模型——多因子降维分析(MDR)来分析这些相互作用。
这是一项病例对照研究,共纳入 242 例研究对象。从外周血淋巴细胞中提取基因组 DNA。采用 MDR 分析了携带暴露变量(吸烟)的 11 个基因变异,以确定基因-基因和基因-环境相互作用的最佳基因座模型。使用 MDR 置换测试软件(版本 1.0 beta 2)的 1000 次置换检验评估统计学意义。p<0.05 为统计学意义。
基因-基因相互作用的最佳三基因模型包括两个 p53 基因多态性;rs17878362(内含子 3)和 rs1042522(外显子 4)以及 I 期基因 CYP2E1(DraI)中的 rs6413432。评估基因-环境相互作用的三基因模型包括 p53 基因的两个内含子多态性,即 rs17878362(内含子 3)和 rs1625895(内含子 6)以及 I 期基因 CYP1A1*2C 中的 rs4646903。相互作用图显示烟草和 p53 多态性 rs1042522(外显子 4)的独立主效应以及 rs17878362(内含子 3)和 rs1042522(外显子 4)之间显著的相加交互作用。
非参数方法强调了烟草使用和 p53 基因变异作为口腔癌风险的重要因素。本研究的发现将有助于实施烟草使用和分子病理学筛查的预防策略。