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

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

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 < 0.05), but also 129 shared genera altered (p-value < 0.05) between the two CRC subgroups, including several marker genera in CRC, such as Fusobacterium and Bacteroides. A random forest algorithm was used to construct diagnostic models, which showed significantly higher efficiency when the CRC samples were divided into subgroups. Then an independent cohort including 187 CRC samples (divided into 153 Subgroup1 and 34 Subgroup2) and 123 healthy controls was chosen to validate the models, and confirmed the results. Conclusions: These results indicate that the divided CRC subgroups can improve the efficiency of disease diagnosis, with various microbial composition in the subgroups.

Original languageEnglish
Article number315
JournalBMC Gastroenterology
Volume24
Issue number1
DOIs
StatePublished - Dec 2024

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 3 - Good Health and Well-being
    SDG 3 Good Health and Well-being

Keywords

  • CRC
  • Diagnostic efficiency
  • Fusobacterium
  • Intestinal microbes
  • PAM clustering
  • Random forest
  • Subgroup

Fingerprint

Dive into the research topics of 'Improved diagnostic efficiency of CRC subgroups revealed using machine learning based on intestinal microbes'. Together they form a unique fingerprint.

Cite this