To obtain a screening tool for colorectal cancer (CRC) based on gut microbiota, we seek here to identify an optimal classifier for CRC detection as well as a novel nonlinear feature selection method for determining the most discriminative microbial species. In this study, the intestinal microflora in feces of 141 patients were modeled using general regression neural networks (GRNNs) combined with the proposed feature selection method. The proposed model led to slightly higher accuracy (AUC=0.911) than previous studies (AUC<0.87). The results show that the Clostridium scindens and Bifidobacterium angulatum are indicators of healthy gut flora and CRC happens to reduce these bacterial species. In addition, Fusobacterium gonidiaformans was found to be closely correlated with the CRC. The occurrence of colorectal adenoma was not sufficiently discriminatory based on fecal microbiota implicating that the change of colonic flora happens in the advanced phase of CRC development rather than initial adenoma. Integrating the proposed model with fecal occult blood test (FOBT), the CRC detection accuracy remained nearly unchanged (AUC=0.915). The performance of proposed method is validated using independent cohorts from America and Austria. Our results suggest that proposed feature selection method combined with GRNN is potentially an accurate method for CRC detection.

中文翻译：

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基于微生物群分析，广义回归神经网络和非线性特征选择的大肠癌检测。

为了获得基于肠道菌群的大肠癌（CRC）筛查工具，我们在这里寻求确定用于CRC检测的最佳分类器以及一种用于确定最具区分性的微生物物种的新型非线性特征选择方法。在这项研究中，使用通用回归神经网络（GRNN）结合提出的特征选择方法对141例患者粪便中的肠道菌群进行建模。与先前的研究（AUC <0.87）相比，所提出的模型导致了更高的准确性（AUC = 0.911）。结果表明，梭状梭状芽胞杆菌和双歧杆菌是健康肠道菌群的指标，CRC可以减少这些细菌。此外，已发现淋球菌Fusobacterium gonidiaformans与CRC密切相关。基于粪便微生物群，大肠腺瘤的发生没有足够的区分性，暗示结肠菌群的变化发生在CRC发展的晚期而不是最初的腺瘤。将建议的模型与粪便潜血测试（FOBT）集成在一起，CRC检测的准确性几乎保持不变（AUC = 0.915）。使用来自美国和奥地利的独立队列验证了所提出方法的性能。我们的结果表明，结合GRNN提出的特征选择方法可能是CRC检测的准确方法。CRC检测精度几乎保持不变（AUC = 0.915）。使用来自美国和奥地利的独立队列验证了所提出方法的性能。我们的结果表明，结合GRNN提出的特征选择方法可能是CRC检测的准确方法。CRC检测精度几乎保持不变（AUC = 0.915）。使用来自美国和奥地利的独立队列验证了所提出方法的性能。我们的结果表明，结合GRNN提出的特征选择方法可能是CRC检测的准确方法。