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Development And Validation Of A Cancer-associated Fibroblast Gene Signature-based Model For Predicting Immunotherapy Response In Colon Cancer.

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Development And Validation Of A Cancer-associated Fibroblast Gene Signature-based Model For Predicting Immunotherapy Response In Colon Cancer.

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Development and validation of a cancer-associated fibroblast gene signature-based model for predicting immunotherapy

Daoyang Zou¹, Xi Xin², Huangzhen Xu¹

¹The Second Affiliated Hospital of Fujian Medical University, Quanzhou, China.

Scientific Reports

|May 13, 2025

View abstract on PubMed

Summary

This summary is machine-generated.

This study identifies a new risk model for predicting colon cancer immunotherapy response based on cancer-associated fibroblast (CAF) infiltration. The model accurately predicts treatment efficacy and guides clinical decisions for better patient outcomes.

Keywords:

Cancer-associated fibroblasts (CAF)Colon cancer Immunotherapy response prediction Risk stratification model Tumor microenvironment

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Area of Science:

Oncology
Immunotherapy
Bioinformatics

Background:

Immune checkpoint inhibitors show efficacy in colon cancer, but new molecular markers are needed to guide immunotherapy decisions.
Cancer-associated fibroblasts (CAFs) play a role in the tumor microenvironment and can influence treatment response.

Purpose of the Study:

To develop and validate a novel risk model for predicting colon cancer immunotherapy efficacy based on CAF infiltration.
To identify CAF-related genes and assess their association with immunotherapy response and clinical outcomes.

Main Methods:

Utilized TCGA and GEO databases for colon cancer samples.
Employed R packages 'EPIC' and 'MCPcounter' for CAF infiltration scoring.
Performed Weighted Gene Co-expression Network Analysis (WGCNA) and LASSO regression to construct a risk model.

Conducted comprehensive bioinformatics analyses, including Gene Set Enrichment Analysis (GSEA) and drug sensitivity analysis.

Main Results:

The constructed risk model accurately reflected CAF infiltration abundance (correlation coefficients of 0.91 and 0.88 in training and validation cohorts, respectively).
The model effectively predicted immunotherapy response, with high-risk groups showing significantly poorer response (e.g., 24% vs. 68% effective rate in training cohort).
AUC values for predicting immunotherapy response were 0.780 and 0.774 in the training and validation cohorts, respectively.
CAF infiltration was linked to extracellular matrix remodeling, immunosuppression, and drug resistance.

Conclusions:

Predicting colon cancer immunotherapy efficacy using CAF infiltration abundance is a feasible approach.
The developed risk model can stratify patients based on predicted immunotherapy response.
High-risk patients may benefit from prioritizing non-immunotherapy treatments to avoid potential risks and improve outcomes.