Improved diagnostic efficiency of CRC subgroups revealed using machine learning based on intestinal microbes

Autor: Guang Liu, Lili Su, Cheng Kong, Liang Huang, Xiaoyan Zhu, Xuanping Zhang, Yanlei Ma, Jiayin Wang
Jazyk: angličtina
Rok vydání: 2024
Předmět:
Zdroj: BMC Gastroenterology, Vol 24, Iss 1, Pp 1-12 (2024)
Druh dokumentu: article
ISSN: 1471-230X
DOI: 10.1186/s12876-024-03408-3
Popis: Abstract Background Colorectal cancer (CRC) is a common cancer that causes millions of deaths worldwide each year. At present, numerous studies have confirmed that intestinal microbes play a crucial role in the process of CRC. Additionally, studies have shown that CRC can be divided into several consensus molecular subtypes (CMS) based on tumor gene expression, and CRC microbiomes have been reported related to CMS. However, most previous studies on intestinal microbiome of CRC have only compared patients with healthy controls, without classifying of CRC patients based on intestinal microbial composition. Results In this study, a CRC cohort including 339 CRC samples and 333 healthy controls was selected as the discovery set, and the CRC samples were divided into two subgroups (234 Subgroup1 and 105 Subgroup2) using PAM clustering algorithm based on the intestinal microbial composition. We found that not only the microbial diversity was significantly different (Shannon index, p-value
Databáze: Directory of Open Access Journals
Nepřihlášeným uživatelům se plný text nezobrazuje