Open Access

ARTICLE

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

Jun Li¹, Kangmin Yu¹, Zhiyong Chen¹, Dan Xing², Binshan Zha¹, Wentao Xie¹, Huan Ouyang¹, Changjun Yu^3,*

1 Department of Vascular Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, 230022, China
2 Department of Medical Record Management, The First Affiliated Hospital of Anhui Medical University, Hefei, 230022, China
3 Department of Gastrointestinal Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, 230022, China

* Corresponding Author: Changjun Yu. Email:

(This article belongs to the Special Issue: New Insights in Drug Resistance of Cancer Therapy: A New Wine in an Old Bottle)

Oncology Research 2025, 33(11), 3469-3492. https://doi.org/10.32604/or.2025.066193

Received 01 April 2025; Accepted 21 August 2025; Issue published 22 October 2025

Abstract

Objectives: Colorectal cancer (CRC) remains a major contributor to global cancer mortality, ranking second worldwide for cancer-related deaths in 2022, and is characterized by marked heterogeneity in prognosis and therapeutic response. We sought to construct a machine-learning prognostic model based on a complement-related risk signature (CRRS) and to situate this signature within the CRC immune microenvironment. Methods: Transcriptomic profiles with matched clinical annotations from TCGA and GEO CRC cohorts were analyzed. Prognostic CRRS genes were screened using Cox proportional hazards modeling alongside machine-learning procedures. A random survival forest (RSF) predictor was trained and externally validated. Comparisons of immune infiltration, mutational burden, pathway enrichment, and drug sensitivity were made between risk groups. The function of FAM84A, a key model gene, was examined in CRC cell lines. Results: The six-gene CRRS model accurately stratified patients by survival outcomes. Low-risk patients exhibited greater immune cell infiltration and higher predicted response to immunotherapy and chemotherapy, while high-risk patients showed enrichment of complement activation and matrix remodeling pathways. FAM84A was shown to promote CRC cell proliferation, migration, and epithelial–mesenchymal transition. Conclusion: CRRS is a critical modulator of the CRC immune microenvironment. The developed model enables precise risk prediction and supports individualized therapeutic decisions in CRC.

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