Department of Epidemiology and Biostatistics and MOE Key Lab of Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China.
Carcinogenesis. 2011 Mar;32(3):336-42. doi: 10.1093/carcin/bgq264. Epub 2010 Dec 10.
Single genetic variation may only have a modest effect on risk of gastric cardia adenocarcinoma (GCA) because this malignancy is believed to result from complex interactions among multiple genetic and environmental factors. However, it has been a challenge to characterize multiple interactions using parametric analytic approaches. This study utilized a multi-analytic strategy combining logistic regression (LR), multifactor dimensionality reduction (MDR) and classification and regression tree (CART) approaches to explore high-order interactions among smoking and 12 polymorphisms involved in different processes of carcinogenesis in 344 GCA patients and 324 controls. LR, MDR and CART analyses consistently suggested MMP-2 C-1306T polymorphism as the strongest individual factor for GCA risk. Intriguingly, a high-order interaction was consistently identified by MDR, LR and CART analyses. In MDR analysis, the three-factor model including MMP-2 C-1306T, FASL T-844C and FAS G-1377A yielded the highest testing accuracy of 0.632. When analysing combined effect of these three polymorphisms by LR, a significant gene dose effect was observed with the odds ratios (ORs) being increased with increasing numbers of risk genotypes (P(trend) = 4.736 × 10⁻¹²). In CART analysis, individuals carrying the combined genotypes of MMP-2 -1306CC, FASL-844TT or TC and FAS -1377AA had the highest risk for GCA (OR = 4.58; 95% confidence interval, 2.07-10.14) compared with the lowest risk carriers of the MMP-2 -1306CT or TT genotype. These results suggest that MMP-2 C-1306T polymorphism is an important risk factor for GCA and the multifactor interactions among polymorphisms in MMP-2, FASL and FAS play more important role in the development of GCA.
单个遗传变异可能仅对胃贲门腺癌（GCA）的风险产生适度影响，因为这种恶性肿瘤被认为是多种遗传和环境因素复杂相互作用的结果。然而，使用参数分析方法来描述多种相互作用一直是一个挑战。本研究采用了一种多分析策略，结合逻辑回归（LR）、多因子降维（MDR）和分类回归树（CART）方法，在 344 例 GCA 患者和 324 例对照中探讨了吸烟和参与癌变过程的 12 个多态性之间的高阶相互作用。LR、MDR 和 CART 分析一致表明 MMP-2 C-1306T 多态性是 GCA 风险的最强个体因素。有趣的是，MDR、LR 和 CART 分析一致发现了高阶相互作用。在 MDR 分析中，包括 MMP-2 C-1306T、FASL T-844C 和 FAS G-1377A 在内的三因素模型得出了最高的测试准确性，为 0.632。当通过 LR 分析这些三个多态性联合作用时，观察到显著的基因剂量效应，随着风险基因型数量的增加，比值比（ORs）增加（P(trend) = 4.736×10⁻¹²）。在 CART 分析中，与 MMP-2-1306CC、FASL-844TT 或 TC 和 FAS-1377AA 联合基因型携带个体相比，携带 MMP-2-1306CT 或 TT 基因型的个体患 GCA 的风险最高（OR=4.58；95%置信区间，2.07-10.14）。这些结果表明，MMP-2 C-1306T 多态性是 GCA 的重要危险因素，MMP-2、FASL 和 FAS 中的多态性之间的多因素相互作用在 GCA 的发生发展中起更重要的作用。
