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Purpose <p>Colorectal Cancer (CRC) exhibits considerable heterogeneity. Circadian Rhythm (CR) disruption is increasingly implicated in tumorigenesis and cancer progression.</p> Methods <p>We identified Circadian Rhythm-Related Genes (CRRGs) significantly associated with prognosis through differential expression analysis and univariate Cox regression from 1,184 samples, and established the molecular subtypes based on unsupervised clustering. Employing a combination of ten machine learning algorithms to 101 model configurations, we developed and validated a high-predictive-performance risk-scoring model (CRRGscore).</p> Results <p>CRC patients were stratified into two distinct molecular subtypes (Cluster A vs. B). Cluster B had worse prognosis, and tumor microenvironment of Cluster B was characterized by enhanced immune suppression and stromal activation. The RSF model demonstrated the best performance (C-index = 0.707) and was used to build the CRRGscore. This risk model showed outstanding predictive ability in the TCGA training set, with 1-, 3-, 5-year AUCs of 0.982, 0.978, 0.991, respectively. Its robustness was maintained across three independent validation sets. Patients in the High-risk group had significantly poorer overall survival (<i>P</i> &lt; 0.001) and the risk score was significantly correlated with adverse clinical characteristics, like advanced tumor stage and recurrence/metastasis (<i>P</i> &lt; 0.01). Further analysis revealed an immune-excluded/immunosuppressive subtype phenotype in the High-risk group. Drug sensitivity analysis indicated that the High-risk group was less sensitive to conventional chemotherapeutics and targeted agents, and identifying several potential alternative drugs.</p> Conclusion <p>This model demonstrated robust predictive capability for patient survival outcomes, tumor microenvironment status, and chemotherapeutic response in the integrated cohorts, providing a crucial clinical tool for prognostic assessment in CRC.</p>

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Integrative Machine-Learning Molecular Subtyping and Risk Scores of Circadian Rhythm-Related Prognostic Signatures in Colorectal Cancer

  • 期刊论文

  • 发表日期：

    2026年4月6日

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  • Zeyu Lai,
  • Rusong Li,
  • Ye Mao,
  • Biqin Zhang,
  • Yaoqiang Du

摘要

Purpose

Colorectal Cancer (CRC) exhibits considerable heterogeneity. Circadian Rhythm (CR) disruption is increasingly implicated in tumorigenesis and cancer progression.

Methods

We identified Circadian Rhythm-Related Genes (CRRGs) significantly associated with prognosis through differential expression analysis and univariate Cox regression from 1,184 samples, and established the molecular subtypes based on unsupervised clustering. Employing a combination of ten machine learning algorithms to 101 model configurations, we developed and validated a high-predictive-performance risk-scoring model (CRRGscore).

Results

CRC patients were stratified into two distinct molecular subtypes (Cluster A vs. B). Cluster B had worse prognosis, and tumor microenvironment of Cluster B was characterized by enhanced immune suppression and stromal activation. The RSF model demonstrated the best performance (C-index = 0.707) and was used to build the CRRGscore. This risk model showed outstanding predictive ability in the TCGA training set, with 1-, 3-, 5-year AUCs of 0.982, 0.978, 0.991, respectively. Its robustness was maintained across three independent validation sets. Patients in the High-risk group had significantly poorer overall survival (P < 0.001) and the risk score was significantly correlated with adverse clinical characteristics, like advanced tumor stage and recurrence/metastasis (P < 0.01). Further analysis revealed an immune-excluded/immunosuppressive subtype phenotype in the High-risk group. Drug sensitivity analysis indicated that the High-risk group was less sensitive to conventional chemotherapeutics and targeted agents, and identifying several potential alternative drugs.

Conclusion

This model demonstrated robust predictive capability for patient survival outcomes, tumor microenvironment status, and chemotherapeutic response in the integrated cohorts, providing a crucial clinical tool for prognostic assessment in CRC.