Computational Molecular Biology 2025, Vol.15, No.4, 183-192 http://bioscipublisher.com/index.php/cmb 183 Feature Review Open Access Computational Frameworks for Spatial Transcriptomics in Tumor Microenvironment Jianhui Li Institute of Life Science, Jiyang College of Zhejiang A&F University, Zhuji, 311800, China Corresponding author: jianhui.li@jicat.org 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 Copyright © 2025 Li, 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.6 Preferred citation for this article: 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) Abstract 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. Keywords Tumor microenvironment; Spatial transcriptomics; Spatial heterogeneity; Computational modeling; Multi-omics integration; Precision medicine 1 Introduction Tumor cells do not exist in isolation. They are surrounded by a whole "small world" - immune cells, fibroblasts, blood vessels, and various extracellular substances are all active within it. This complex environment is called the tumor microenvironment. It is not like a fixed background but more like a participant: constant "dialogue" between cells and between cells and the matrix, and the result will affect whether the tumor grows slowly, spreads rapidly, or becomes sluggish to treatment. Sometimes, the development of a tumor does not entirely depend on the cancer cells themselves; rather, it is the attitudes of these "neighbors" around that determine its fate (He et al., 2025). In recent years, immunotherapy has once again drawn people's attention to the role of this microenvironment - it is not always helpful; more often than not, it interferes with the immune response in a "suppressive" manner, and thus has become an indispensable key link in research and treatment (Cao et al., 2023). Previous studies on gene expression, such as single-cell RNA sequencing, although they could clearly see what was happening inside the cells, could not see the "position" of these cells in the tissue. Sometimes, knowing "who said what" is not enough; one also needs to know "where they said it". The emergence of spatial transcriptomics has precisely filled this gap - simultaneously measuring gene expression on tissue sections and marking spatial positions, just like drawing a map of gene activity (Li et al., 2022). In the past few years, this technology has developed rapidly and can be seen in various fields, from neuroscience to tumor research. Especially in the study of tumors, it enables us to more clearly observe the molecular differences in different regions, understand the complexity within tumors, and also provides new clues for individualized treatment (Huang et al., 2024). This research mainly focuses on the spatial transcriptome data of the tumor microenvironment, aiming to understand the hidden spatial differences and biological patterns through computational modeling. We attempt to establish a systematic analytical framework, using graph theory and spatial statistics methods to reconstruct the organizational structure, while integrating multi-omics information into the model to make the results closer to the
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