Your new experience awaits. Try the new design now and help us make it even better

ORIGINAL RESEARCH article

Front. Immunol.

Sec. Cancer Immunity and Immunotherapy

This article is part of the Research TopicUnlocking the Metabolic Dynamics of Tumor Microenvironment through Radiotherapy: Bridging Pre-Clinical Insights to Clinical ApplicationsView all 3 articles

Unveiling Tumor Senescence-Driven Prognostic Heterogeneity via MALISS in Stage II/III Colorectal Cancer

Provisionally accepted
Xinyu LiuXinyu Liu1Bingyao LiuBingyao Liu2Yuhao TongYuhao Tong3Xingyu ZhuXingyu Zhu1Yaodong SangYaodong Sang1Feng GaoFeng Gao3Xiangyun NiuXiangyun Niu3Youyong TangYouyong Tang1Kang XuKang Xu1Hao ChenHao Chen4*Wei ChongWei Chong1*Leping LiLeping Li1*
  • 1Shandong Provincial Hospital, Jinan, China
  • 2Shandong Second Medical University, Weifang, China
  • 3Shandong First Medical University, Jinan, China
  • 4Qilu Hospital of Shandong University, Jinan, China

The final, formatted version of the article will be published soon.

Select one of your emails

You have multiple emails registered with Frontiers:

Notify me on publication

Please enter your email address:

If you already have an account, please login

You don't have a Frontiers account ? You can register here

Prognostic heterogeneity in stage II/III colorectal cancer (CRC) challenges clinical management. This study developed a machine learning-based immunosenescence signature (MALISS) using transcriptomic data from 1296 patients. The final 30 genes are used to develop the model, derived by a CoxBoost-Lasso algorithm, effectively stratified patients into high-and low-risk groups with distinct progression-free survival in multiple validation cohorts. Functional analysis confirmed that a key gene, NR1D2, promotes tumor migration via cellular senescence. The high-risk group exhibited unique mutational landscapes, altered tumor microenvironments, and differential drug sensitivity. A nomogram integrating MALISS with clinical biomarkers further improved prognostic prediction. MALISS represents a robust tool for risk stratification and insights into tumor biology in stage II/III CRC.

Keywords: immunosenescence, machine learning, NR1D2, stage II/III colorectal cancer, Tumor Microenvironment

Received: 12 Nov 2025; Accepted: 16 Dec 2025.

Copyright: © 2025 Liu, Liu, Tong, Zhu, Sang, Gao, Niu, Tang, Xu, Chen, Chong and Li. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence:
Hao Chen
Wei Chong
Leping Li

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

Supplementary Material