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Retrospective Cohort Study
Copyright ©The Author(s) 2026. Published by Baishideng Publishing Group Inc. All rights reserved.
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
Core Tip

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.