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A Machine-Learning Prognostic Model for Colorectal Cancer Using a Complement-Related Risk Signature.

Jun Li1, Kangmin Yu1, Zhiyong Chen1

  • 1Department of Vascular Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, 230022, China.

Oncology Research
|November 3, 2025
PubMed
Summary
This summary is machine-generated.

A new six-gene complement-related risk signature (CRRS) model accurately predicts colorectal cancer (CRC) patient survival. This model aids in understanding the CRC immune microenvironment and guides personalized treatment decisions.

Keywords:
Colorectal cancercomplement responsecomplement-related risk signature (CRRS)prognostic modelthe cancer genome atlastumor microenvironment

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Area of Science:

  • Oncology
  • Computational Biology
  • Immunology

Background:

  • Colorectal cancer (CRC) presents significant global mortality and heterogeneous patient outcomes.
  • Understanding the CRC immune microenvironment is crucial for improving therapeutic strategies.

Purpose of the Study:

  • To develop a machine-learning prognostic model using a complement-related risk signature (CRRS) for colorectal cancer.
  • To analyze the relationship between the CRRS and the CRC immune microenvironment.

Main Methods:

  • Transcriptomic data from TCGA and GEO CRC cohorts were analyzed.
  • A random survival forest (RSF) model was trained and validated to identify prognostic CRRS genes.
  • Immune infiltration, mutational burden, pathway enrichment, and drug sensitivity were compared between risk groups.

Main Results:

  • The six-gene CRRS model effectively stratified CRC patients based on survival.
  • Low-risk patients showed increased immune cell infiltration and predicted better response to immunotherapy/chemotherapy.
  • High-risk patients exhibited complement activation and matrix remodeling pathway enrichment; FAM84A promoted CRC progression.

Conclusions:

  • The CRRS is a key factor influencing the colorectal cancer immune microenvironment.
  • The developed CRRS model offers precise risk prediction for individualized CRC therapy.