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

TL;DR

This study creates a machine-learning model to predict colorectal cancer outcomes using a complement-related risk signature and explores its impact on the tumor's immune environment.

Contribution

A novel six-gene complement-related risk signature model for accurate prognosis and treatment prediction in colorectal cancer.

Findings

The six-gene CRRS model effectively stratifies patients by survival outcomes.

Low-risk patients show higher immune infiltration and better predicted treatment response.

FAM84A promotes cancer cell proliferation and migration in CRC.

Abstract

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.

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.

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.

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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