Detection and application of CagA sequence markers for assessing risk factor of gastric cancer caused by Helicobacter pylori

2010 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) Pub Date : 2010-12-01 DOI:10.1109/BIBM.2010.5706614

Chao Zhang, Shunfu Xu, Dong Xu

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引用次数: 1

Abstract

As a marker of Helicobacter pylori, Cytotoxin-associated gene A (CagA) has been revealed to be the major virulence factor to cause gastroduodenal diseases. However, the molecular mechanisms that underlie the development of different gastroduodenal diseases caused by cagA-positive H. pylori infection remain unknown. Current studies are mainly limited to the relationship between EPIYA motifs in the CagA strain and diseases, but such a relationship is insufficient to explain the diversity of diseases. We propose a new and systematic method to analyze the relationship between the whole CagA sequence patterns and diseases. For this purpose, we introduced entropy calculation to detect key residues of CagA as the gastric cancer biomarkers, and then employed a supervised learning procedure to classify the cancer and non-cancer related CagA strains by using the key residues. We achieved 76% and 71% classification accuracy for Western and East Asian subtypes, respectively. Our study may help establish H. pylori biomarkers for predicting gastroduodenal disease outcome.

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CagA序列标记物在幽门螺杆菌致胃癌危险因素评估中的检测及应用

细胞毒素相关基因a (Cytotoxin-associated gene a, CagA)作为幽门螺杆菌的标志物，是引起胃十二指肠疾病的主要毒力因子。然而，由caga阳性幽门螺杆菌感染引起的不同胃十二指肠疾病发展的分子机制尚不清楚。目前的研究主要局限于CagA菌株中EPIYA基序与疾病的关系，但这种关系不足以解释疾病的多样性。我们提出了一种新的系统的方法来分析整个CagA序列模式与疾病之间的关系。为此，我们引入熵计算来检测CagA关键残基作为胃癌生物标志物，然后利用关键残基采用监督学习方法对胃癌和非癌症相关的CagA菌株进行分类。我们对西亚和东亚亚型的分类准确率分别达到76%和71%。我们的研究可能有助于建立预测胃十二指肠疾病预后的幽门螺杆菌生物标志物。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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2010 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

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