Tarlarini Claudia, Penco Silvana, Conio Massimo, Grossi Enzo
Department of Laboratory Medicine, Medical Genetics, Niguarda Ca' Granda Hospital, Milan, Italy.
Clin Exp Gastroenterol. 2012;5:159-66. doi: 10.2147/CEG.S32610. Epub 2012 Aug 8.
Barrett's esophagus (BE), a metaplastic premalignant disorder, represents the primary risk factor for the development of esophageal adenocarcinoma. Chronic gastroesophageal reflux disease and central obesity have been associated with BE and esophageal adenocarcinoma, but relatively little is known about the specific genes that confer susceptibility to BE carcinogenesis.
A total of 74 patients with BE and 67 controls coming from six gastrointestinal Italian units were evaluated for six polymorphisms in four genes: XPC, XPD nucleotide excision repair (NER) genes, XRCC1 (BER gene), and glutathione S-transferase P1. Smoking status was analyzed together with the genetic data. Statistical analysis was performed through Artificial Neural Networks.
Distributions of sex, smoking history, and polymorphisms among BE cases and controls did not show statistically significant differences. The r-value from linear correlation allowed us to identify possible protective factors as well as possible risk factors. The application of advanced intelligent systems allowed for the selection of a subgroup of nine variables. Artificial Neural Networks applied on the final data set reached mean global accuracy of 60%, reaching as high as 65.88%.
We report here results from an exploratory study. Results from this study failed to find an association among the tested single nucleotide polymorphisms and BE phenotype through classical statistical methods. On the contrary, advanced intelligent systems are really able to handle the disease complexity, not treating the data with reductionist approaches unable to detect multiple genes of smaller effect in predisposing to the disease.
To detect multiple genes of smaller effects in predisposing individuals to Barrett's esophagus.
巴雷特食管（BE）是一种化生的癌前病变，是食管腺癌发生的主要危险因素。慢性胃食管反流病和中心性肥胖与BE及食管腺癌有关，但对于赋予BE致癌易感性的特定基因知之甚少。
对来自意大利六个胃肠病单元的74例BE患者和67例对照进行了四个基因中六个多态性的评估：XPC、XPD核苷酸切除修复（NER）基因、XRCC1（碱基切除修复基因）和谷胱甘肽S-转移酶P1。将吸烟状况与基因数据一起进行分析。通过人工神经网络进行统计分析。
BE病例和对照之间的性别、吸烟史和多态性分布没有显示出统计学上的显著差异。线性相关的r值使我们能够识别可能的保护因素和可能的风险因素。先进智能系统的应用允许选择九个变量的一个亚组。应用于最终数据集的人工神经网络平均全局准确率达到60%，最高可达65.88%。
我们在此报告一项探索性研究的结果。本研究结果未能通过经典统计方法发现所检测的单核苷酸多态性与BE表型之间的关联。相反，先进智能系统确实能够处理疾病的复杂性，而不是用无法检测到在疾病易感性中起较小作用的多个基因的简化方法来处理数据。
检测在个体易患巴雷特食管中起较小作用的多个基因。
