Development and internal validation of an immune-based prognostic modeling of early-onset colorectal cancer via machine learning

doi:10.5306/wjco.v17.i1.114238

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Jan 24, 2026 (publication date) through Jan 28, 2026

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World Journal of Clinical Oncology

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World J Clin Oncol. Jan 24, 2026; 17(1): 114238
Published online Jan 24, 2026. doi: 10.5306/wjco.v17.i1.114238

Development and internal validation of an immune-based prognostic modeling of early-onset colorectal cancer via machine learning

Xiu Chen, Yong Wang, Heng-Yang Shen, Rui Wu, Zan Fu

Xiu Chen, Yong Wang, Heng-Yang Shen, Zan Fu, Department of General Surgery, The First Affiliated Hospital with Nanjing Medical University, Nanjing 210029, Jiangsu Province, China

Rui Wu, Department of General Surgery, Nanjing Qixia District Hospital, Nanjing 210000, Jiangsu Province, China

Co-first authors: Xiu Chen and Yong Wang.

Author contributions: Chen X contributed to writing-original draft, formal analysis, and project administration; Chen X, Wang Y, and Wu R contributed to methodology and investigation; Chen X and Shen HY contributed to data curation; Chen X and Fu Z contributed to conceptualization; Wang Y and Fu Z contributed to R resources; Shen HY contributed to writing - review and editing; Fu Z contributed to visualization and supervision. Chen X and Wang Y contributed equally to this manuscript and are co-first authors. All authors have read and approved the final version to be published.

Supported by National Natural Science Foundation of China, No. 82172956; and Jiangsu Province Capability Improvement Project through Science, Technology and Education (Jiangsu Provincial Medical Key Discipline), No. ZDXK202222.

Institutional review board statement: The study was approved by the Ethics Committee of the First Affiliated Hospital with Nanjing Medical University (Approval No. 2023-SR-206).

Informed consent statement: All participants, or their legal guardian, provided informed written consent prior to study enrollment.

Conflict-of-interest statement: All the authors report no relevant conflicts of interest for this article.

STROBE statement: The authors have read the STROBE Statement-checklist of items, and the manuscript was prepared and revised according to the STROBE Statement-checklist of items.

Data sharing statement: Technical appendix, analytical code and dataset available from the corresponding author.

Open Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/

Corresponding author: Zan Fu, MD, Professor, Department of General Surgery, The First Affiliated Hospital with Nanjing Medical University, No. 300 Guangzhou Road, Nanjing 210029, Jiangsu Province, China. fuzan1971@njmu.edu.cn

Received: September 18, 2025
Revised: October 12, 2025
Accepted: December 1, 2025
Published online: January 24, 2026
Processing time: 125 Days and 4.3 Hours

Abstract

BACKGROUND

Early-onset colorectal cancer (EOCRC) is an aggressive malignancy with rising incidence and poor prognosis in young adults. Circulating immune cells may hold prognostic value, yet their role in EOCRC outcomes remains unclear.

AIM

To develop machine learning-based prognostic models using peripheral immune markers in a retrospective cohort of EOCRC patients.

METHODS

A cohort of 123 EOCRC patients undergoing radical resection, from January 2017 to December 2020 was included. Data were extracted from medical records with a follow-up till July 2025. Blood samples were processed for flow cytometry to assess immune markers.

RESULTS

Univariable screening identified disease stage and CD16+CD56+ natural killer (NK) cell percentage as top predictors. A parsimonious Cox model integrating stage and high NK cells outperformed random survival forests (concordance index 0.693 vs 0.256). High-risk patients (stage III/IV, high NK cells) had inferior 5-year progression-free survival (61.2%; 95% confidence interval: 49.0-76.5) vs low-risk (86.4%; 95% confidence interval: 78.9-94.6; log-rank P = 0.001). Time-dependent areas under the curve ranged from 0.671 to 0.693, with robust calibration.

CONCLUSION

This two-factor model offers moderate accuracy for personalized EOCRC risk stratification, highlighting systemic NK cell dysfunction as a potential immunotherapy target. External validation is warranted.

Keywords: Early-onset colorectal cancer; Progression-free survival; Immune markers; Machine learning; Prognostic modeling

Core Tip: Early-onset colorectal cancer (EOCRC) represents a growing public health challenge, characterized by aggressive biology and poor prognosis in young adults. While circulating immune cells play a pivotal role in cancer progression, their prognostic utility in EOCRC remains underexplored. In this study, we leveraged machine learning techniques to develop and validate a novel prognostic model integrating disease stage with peripheral CD16+CD56+ natural killer cell percentages. Our parsimonious Cox model demonstrated moderate discriminatory accuracy and clear risk stratification, with high-risk patients exhibiting significantly inferior progression-free survival. These findings highlight systemic natural killer cell dysfunction as a potential biomarker and immunotherapy target for EOCRC.