粪便隐血对结直肠癌患者肠道微生物群落结构的影响。

IF 4 2区 生物学 Q2 MICROBIOLOGY
Wu Guodong, Wu Yinhang, Wu Xinyue, Shen Hong, Chu Jian, Qu Zhanbo, Han Shuwen
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引用次数: 0

摘要

背景:肠道微生物已被用于预测结直肠癌的风险。粪便隐血试验(FOBT)已被推荐用于人群CRC筛查。目的:分析粪便潜血试验(FOBT)对肠道微生物的影响。方法:选取107例健康人(fobt阴性)和111例结直肠癌患者(fobt阴性39例,fobt阳性72例)的粪便样本进行16s核糖体RNA测序。根据不同FOBT的结果,分析健康个体和结直肠癌患者肠道细菌的群落结构和多样性。筛选特征肠道细菌,并应用各种机器学习算法构建CRC风险预测模型。结果:绘制了健康人及不同粪便隐血结直肠癌患者的肠道菌群图谱。FOBT阴性和FOBT阳性CRC患者的多样性无统计学差异。拟杆菌、Blautia和Escherichia-Shigella与健康个体的相关性更高,而链球菌与FOBT阴性的CRC患者的相关性更高。基于支持向量机(SVM)算法的CRC风险预测模型准确率最高(89.71%)。随后,在模型构建中加入FOBT作为特征元素,模型的预测精度均有所提高。同样,基于SVM算法的CRC风险预测模型准确率最高(92%)。结论:FOB影响肠道微生物群落组成。在基于肠道微生物组预测结直肠癌风险时,考虑FOBT的影响有望提高结直肠癌风险预测的准确性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Fecal occult blood affects intestinal microbial community structure in colorectal cancer.

Background: Gut microbes have been used to predict CRC risk. Fecal occult blood test (FOBT) has been recommended for population screening of CRC.

Objective: To analyze the effects of fecal occult blood test (FOBT) on gut microbes.

Methods: Fecal samples from 107 healthy individuals (FOBT-negative) and 111 CRC patients (39 FOBT-negative and 72 FOBT-positive) were included for 16 S ribosomal RNA sequencing. Based on the results of different FOBT, the community structure and diversity of intestinal bacteria in healthy individuals and CRC patients were analyzed. Characteristic gut bacteria were screened, and various machine learning algorithms were applied to construct CRC risk prediction models.

Results: The gut microbiota of healthy people and CRC patients with different fecal occult blood were mapped. There was no statistical difference in diversity between CRC patients with negative FOBT and positive FOBT. Bacteroides, Blautia and Escherichia-Shigella were more correlated to healthy individuals, while Streptococcus showed higher correlation with CRC patients with negative FOBT. The accuracy of CRC risk prediction model based on the support vector machines (SVM) algorithm was the highest (89.71%). Subsequently, FOBT was included as a characteristic element in the model construction, and the prediction accuracy of the model was all increased. Similarly, the CRC risk prediction model based on SVM algorithm had the highest accuracy (92%).

Conclusion: FOB affects the community composition of gut microbes. When predicting CRC risk based on gut microbiome, considering the influence of FOBT is expected to improve the accuracy of CRC risk prediction.

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来源期刊
BMC Microbiology
BMC Microbiology 生物-微生物学
CiteScore
7.20
自引率
0.00%
发文量
280
审稿时长
3 months
期刊介绍: BMC Microbiology is an open access, peer-reviewed journal that considers articles on analytical and functional studies of prokaryotic and eukaryotic microorganisms, viruses and small parasites, as well as host and therapeutic responses to them and their interaction with the environment.
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