Multidimensional Analysis of the Impact of Gemmatimonas, Rhodothermus, and Sutterella on Drug and Treatment Response in Colorectal Cancer

Overview

Journal Front Cell Infect Microbiol

Specialties Cell Biology
Infectious Diseases
Microbiology

Date 2024 Oct 23

PMID 39439901

Authors

Shaowen Jin

Wa Zhong

Bo Li

Kaimei Wang

Dongming Lai

Affiliations

Soon will be listed here.

Abstract

Background: Colorectal cancer is the third most prevalent cancer across the globe. Despite a diversity of treatment methods, the recurrence and mortality rates of the disease remain high. Recent studies have revealed a close association of the gut microbiota with the occurrence, development, treatment response, and prognosis of colorectal cancer.

Objective: This study aims to integrate transcriptome and microbiome data to identify colorectal cancer subtypes associated with different gut microbiota and evaluate their roles in patient survival prognosis, tumor microenvironment (TME), and drug treatment response.

Methods: An integrated analysis of microbiome data was conducted on samples of colorectal cancer from public databases. Based on this, two tumor subtypes (C1 and C2) closely associated with patient survival prognosis were identified and a risk score model was constructed. The survival status, clinical parameters, immune scores, and other features were analyzed in-depth, and the sensitivity of various potential drugs was examined.

Results: A thorough examination of microbiome information obtained from colorectal cancer patients led to the identification of two primary tumor clusters (C1 and C2), exhibiting notable variations in survival outcomes. Patients with the C1 subtype were closely associated with better prognosis, while those with the C2 subtype had higher gut microbial richness and poorer survival prognosis. A predictive model utilizing the microbiome data was developed to accurately forecast the survival outcome of patients with colorectal cancer. The TME scores provided a biological basis for risk assessment in high-risk (similar to the C2 subtype) patient cohorts. Evaluation of the sensitivity of different subtypes to various potential drugs, indicated the critical importance of personalized treatment. Further analysis showed good potential of the developed risk-scoring model in predicting immune checkpoint functions and treatment response of patients, which may be crucial in guiding the selection of immunotherapy strategies for patients with colorectal cancer.

Conclusion: This study, through a comprehensive analysis of colorectal cancer microbiome, immune microenvironment, and drug sensitivity, enhances the current understanding of the multidimensional interactions of colorectal cancer and provides important clinical indications for improving future treatment strategies. The findings offer a new perspective on improving treatment response and long-term prognosis of patients with CRC through the regulation of microbiota or the utilization of biomarkers provided by it.

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