Decoding Senescence-Driven Heterogeneity in Early-Onset Colorectal Cancer for Prognostic and Therapeutic Stratification
Dec 4, 2025·
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Du CAI
1st Author
Ming-Ru MAI
Co-1st Author
Ren-De HUANG
Co-1st Author
Hao-Ning QI
Xingzhi Feng
Qianling Gao
Yin-Meng ZHANG
Cheng-Hang LI
Xiaojian Wu
Co-Corresponding Author
,Yize Mao
Co-Corresponding Author
,Zihuan Yang
Co-Corresponding Author
Feng GAO
Corresponding Author
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0 min readAbstract
Early-onset colorectal cancer (EOCRC) is clinically aggressive and lacks precise treatment stratification tools. This work integrates multi-omics data from 2961 patients and reveals distinct senescence-driven EOCRC subtypes with markedly different prognosis and tumor microenvironment characteristics. The proposed EO-Senscore model quantifies senescence states and helps identify patients more likely to benefit from immunotherapy, chemotherapy, or anti-senescence strategies.
Type
Publication
Cancer Science
Authors
Postdoc
I focus on leveraging explainable AI and large foundation models to advance medical imaging and digital pathology in colorectal cancer research.
Authors
Research Student
I am a research student interested in colorectal cancer and bioinformatics, with a focus on data-driven biomedical analysis.
Authors
Research Student
I am a research student working on AI methods for colorectal cancer analysis and clinical decision support.
Authors
Postdoc
I am a postdoctoral researcher working on AI for colorectal cancer with a focus on clinically actionable models.
Authors
PhD Student
I am a PhD student working on AI methods for colorectal cancer research and translational clinical applications.
Authors
Research Assistant
I am a research assistant focusing on deep learning, multimodal feature fusion, and medical AI system development.
Authors
Professor
My research leverages AI and big data to improve diagnostics, prognostics, and ultimately, outcomes in cancer and other biomedical fields.