@Article{or.2025.066193,
AUTHOR = {Jun Li, Kangmin Yu, Zhiyong Chen, Dan Xing, Binshan Zha, Wentao Xie, Huan Ouyang, Changjun Yu},
TITLE = {A Machine-Learning Prognostic Model for Colorectal Cancer Using a Complement-Related Risk Signature},
JOURNAL = {Oncology Research},
VOLUME = {33},
YEAR = {2025},
NUMBER = {11},
PAGES = {3469--3492},
URL = {http://www.techscience.com/or/v33n11/64065},
ISSN = {1555-3906},
ABSTRACT = { <b>Objectives:</b> 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. <b>Methods:</b> 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. <b>Results:</b> 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. <b>Conclusion:</b> CRRS is a critical modulator of the CRC immune microenvironment. The developed model enables precise risk prediction and supports individualized therapeutic decisions in CRC.},
DOI = {10.32604/or.2025.066193}
}