Luan WY, Zhao Q, Zhang Z, Xu ZX, Lin SX, Miao YD. Multidimensional decoding of colorectal cancer heterogeneity: Artificial intelligence-enabled precision exploration of single-cell and spatial transcriptomics. World J Gastrointest Oncol 2025; 17(10): 110661 [PMID: 41114099 DOI: 10.4251/wjgo.v17.i10.110661]
Corresponding Author of This Article
Yan-Dong Miao, MD, Doctor, Cancer Center, Yantai Affiliated Hospital of Binzhou Medical University, The Second Medical College of Binzhou Medical University, No. 717 Jinbu Street, Muping District, Yantai 264100, Shandong Province, China. miaoyd_22@bzmc.edu.cn
Research Domain of This Article
Gastroenterology & Hepatology
Article-Type of This Article
Review
Open-Access Policy of This Article
This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (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: http://creativecommons.org/licenses/by-nc/4.0/
World J Gastrointest Oncol. Oct 15, 2025; 17(10): 110661 Published online Oct 15, 2025. doi: 10.4251/wjgo.v17.i10.110661
Multidimensional decoding of colorectal cancer heterogeneity: Artificial intelligence-enabled precision exploration of single-cell and spatial transcriptomics
Wen-Yu Luan, Qi Zhao, Zheng Zhang, Zhen-Xi Xu, Si-Xiang Lin, Yan-Dong Miao, Cancer Center, Yantai Affiliated Hospital of Binzhou Medical University, The Second Medical College of Binzhou Medical University, Yantai 264100, Shandong Province, China
Si-Xiang Lin, Yan-Dong Miao, Research and Translational Center for Immunological Disorders, Binzhou Medical University, Yantai 264100, Shandong Province, China
Yan-Dong Miao, Guangdong Provincial Key Laboratory of Medical Biomechanics, National Key Discipline of Human Anatomy, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510000, Guangdong Province, China
Yan-Dong Miao, Department of Oncology, Xinhui District People’s Hospital, Jiangmen 529100, Guangdong Province, China
Co-corresponding authors: Si-Xiang Lin and Yan-Dong Miao.
Author contributions: Luan WY performed the literature retrieval, wrote the manuscript, and performed the images drawing; Zhao Q, Zhang Z and Xu ZX performed the data analysis; Lin SX and Miao YD were designated as co-corresponding authors; Lin SX was responsible for the evolution of overarching research goals and aims, specifically critical review, management and coordination responsibility for the research activity planning and execution, acquisition of the financial support for the project leading to this publication; Miao YD was responsible for review and editing the draft, oversight, and leadership responsibility for the research activity planning and execution, including mentorship external to the core team; All authors approved the final manuscript.
Supported by the Shandong Province Medical and Health Science and Technology Development Plan Project, No. 202203030713; Yantai Science and Technology Program, No. 2024YD005, No. 2024YD007 and No. 2024YD010; and Science and Technology Program of Yantai Affiliated Hospital of Binzhou Medical University, No. YTFY2022KYQD06.
Conflict-of-interest statement: The authors declare that they have no conflict of interest.
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: Yan-Dong Miao, MD, Doctor, Cancer Center, Yantai Affiliated Hospital of Binzhou Medical University, The Second Medical College of Binzhou Medical University, No. 717 Jinbu Street, Muping District, Yantai 264100, Shandong Province, China. miaoyd_22@bzmc.edu.cn
Received: June 12, 2025 Revised: July 16, 2025 Accepted: August 22, 2025 Published online: October 15, 2025 Processing time: 124 Days and 22 Hours
Abstract
As a common malignant tumor, the heterogeneity of colorectal cancer plays an important role in tumor progression and treatment response. In recent years, the rapid development of single-cell transcriptomics and spatial transcriptomics technologies has provided new perspectives for resolving the heterogeneity of colorectal cancer. These techniques can reveal the complexity of cellular composition and their interactions in the tumor microenvironment, and thus facilitate a deeper understanding of tumor biology. However, in practical applications, researchers still face technical challenges such as data processing and result interpretation. The aim of this paper is to explore how to use artificial intelligence (AI) technology to enhance the research efficiency of single-cell and spatial transcriptomics, analyze the current research progress and its limitations, and explore how combining AI approaches can provide new ideas for decoding the heterogeneity of colorectal cancer, and ultimately provide theoretical basis and practical guidance for the clinical precision treatment.
Core Tip: Colorectal cancer remains a major global health threat with rising incidence in younger populations and limited response to immunotherapy in most patients, largely due to its complex tumor microenvironment and high cellular heterogeneity. Recent advances in single-cell transcriptomics and spatial transcriptomics have opened new avenues for decoding this heterogeneity, offering unprecedented resolution into tumor biology and immune interactions. However, the massive and multidimensional nature of these datasets poses significant analytical challenges. This paper explores how the integration of artificial intelligence (AI), particularly machine learning and deep learning techniques, can enhance data interpretation in single-cell and spatial transcriptomics, improve the identification of novel biomarkers and tumor subtypes, and ultimately support personalized treatment strategies. By systematically reviewing current progress and proposing AI-driven solutions, this study aims to bridge the gap between complex omics data and clinically actionable insights in colorectal cancer precision medicine.
