Fecal occult blood affects intestinal microbial community structure in colorectal cancer

作     者：Guodong, Wu Yinhang, Wu Xinyue, Wu Hong, Shen Jian, Chu Zhanbo, Qu Shuwen, Han 

作者机构：Huzhou Univ Affiliated Cent Hosp Huzhou Cent Hosp 1558 Sanhuan North Rd Huzhou 313000 Zhejiang Peoples R China Zhejiang Chinese Med Univ Huzhou Cent Hosp Fifth Affiliated Clin Med Coll Huzhou Zhejiang Peoples R China Key Lab Multi Res & Clin Transformat Digest Canc H 1558 Sanhuan North Rd Huzhou 313000 Zhejiang Peoples R China ASIR Inst Assoc intelligent Syst & robot 14B Rue Henri Sainte ClaireDeville F-92500 RUEIL MALMAISON France 

出 版 物：《BMC MICROBIOLOGY》 (BMC Microbiol.)

年 卷 期：2025年第25卷第1期

页      面：1-16页

核心收录：

基　　金：Public Welfare Technology Application Research Program of Huzhou [2024GY05] Key research and development project of Science and Technology Department of Zhejiang Province [2022C03026] Zhejiang Province Traditional Chinese Medicine Science and Technology Project [2024ZL1018] 

主　　题：Colorectal cancer Gut microbes Fecal occult blood test 16S ribosomal RNA sequencing Artificial intelligence Risk prediction 

摘      要：BackgroundGut microbes have been used to predict CRC risk. Fecal occult blood test (FOBT) has been recommended for population screening of *** analyze the effects of fecal occult blood test (FOBT) on gut *** 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 *** 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%).ConclusionFOB 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.