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Colorectal Cancer Cell Surface Protein Profiling Using an Antibody Microarray and Fluorescence Multiplexing

Colorectal Cancer Cell Surface Protein Profiling Using an Antibody Microarray and Fluorescence Multiplexing

Published on: September 25, 2011

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

Guang Liu^1,2, Lili Su^1,2, Cheng Kong^3,4

¹School of Computer Science and Technology, Xi'an Jiaotong University, Xi'an, 710049, China.

BMC Gastroenterology

|September 17, 2024

Summary

Subgrouping colorectal cancer (CRC) patients by gut microbiome composition improves diagnostic accuracy. This classification reveals distinct microbial profiles and enhances disease detection efficiency.

Keywords:

Fusobacterium CRC Diagnostic efficiency Intestinal microbes PAM clustering Random forest Subgroup

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Evaluation of Colorectal Cancer Risk and Prevalence by Stool DNA Integrity Detection

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Published on: June 8, 2020

Area of Science:

Microbiology
Oncology
Bioinformatics

Background:

Colorectal cancer (CRC) is a leading cause of cancer-related deaths globally.
Intestinal microbes are increasingly recognized for their critical role in CRC development.
Previous studies often compared CRC patients to healthy controls, overlooking intra-tumor microbial heterogeneity.

Purpose of the Study:

To investigate the impact of classifying colorectal cancer (CRC) patients based on intestinal microbial composition.
To identify distinct subgroups within CRC based on microbiome profiles.
To enhance the diagnostic efficiency for CRC using microbial data.

Main Methods:

A CRC cohort (339 samples) and healthy controls (333) were analyzed.
Partitioning Around Medoids (PAM) clustering was used to divide CRC samples into two subgroups based on microbial composition.
Random forest algorithm was employed to build diagnostic models for CRC detection.

Main Results:

Significant differences in microbial diversity (Shannon index) and 129 altered genera (e.g., Fusobacterium, Bacteroides) were observed between the two CRC subgroups.
Diagnostic models incorporating subgroup classification demonstrated significantly higher efficiency.
Validation in an independent cohort (187 CRC samples) confirmed these findings.

Conclusions:

Classifying colorectal cancer (CRC) patients into subgroups based on intestinal microbial composition improves diagnostic efficiency.
Distinct microbial compositions characterize these CRC subgroups.
This approach offers a promising avenue for more accurate CRC diagnosis.