Jianhui Li

Institute of Life Science, Jiyang College of Zhejiang A&F University, Zhuji, 311800, China
Author    Correspondence author
Computational Molecular Biology, 2025, Vol. 15, No. 4   doi: 10.5376/cmb.2025.15.0018
Received: 26 May, 2025    Accepted: 08 Jul., 2025    Published: 29 Jul., 2025

This is an open access article published under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Li J.H., 2025, Computational frameworks for spatial transcriptomics in tumor microenvironment, Computational Molecular Biology, 15(4): 183-192 (doi: 10.5376/cmb.2025.15.0018)

 

The spatial heterogeneity of the tumor microenvironment (TME) has a significant impact on tumor progression and treatment response. The rise of spatial transcriptomics technology has provided a new perspective for the study of TME, but its high-dimensional data characteristics pose challenges to the analytical methods. This paper constructs a computational modeling framework for TME spatial transcriptome data, integrating graph theory and spatial statistical methods to mine spatial patterns and cellular communication networks in tissues. We systematically expounded the spatial heterogeneity of the tumor microenvironment, the mainstream spatial transcriptome techniques and data characteristics, and proposed corresponding algorithms to identify cell subpopulations, cell communications and differential gene patterns in space. Through the case of spatial transcriptome of breast cancer, we verified the effectiveness of this framework and revealed the significant differences in molecular characteristics and immune microenvironment between the core and margin of the tumor. Studies have shown that computational models of spatial transcriptomics can deeply analyze the structure and function of the tumor microenvironment, providing new support for precision medicine.

Tumor microenvironment; Spatial transcriptomics; Spatial heterogeneity; Computational modeling; Multi-omics integration; Precision medicine