Computational Molecular Biology 2025, Vol.15, No.4, 183-192 http://bioscipublisher.com/index.php/cmb 184 real biological processes. The overall design is not merely a stacking of algorithms, but rather aims to strike a balance between methods and biological significance. The article begins with the spatial characteristics of the tumor microenvironment and then transitions to the features and modeling ideas of spatial transcriptome data. Next, the algorithm framework, recognition strategy, and the practical application of the model in breast cancer data will be introduced. Finally, reflect on the current challenges and put forward prospects for the future development direction. 2 The Complexity and Spatial Heterogeneity of the Tumor Microenvironment 2.1 Spatial distribution characteristics of cell types, signaling pathways and molecular networks In solid tumors, cells are not randomly scattered. Cancer cells often cluster together, while immune cells prefer to "guard" at the edges and sometimes even form structures similar to lymph nodes (Di Mauro et al., 2024). If we look at the molecular level, different regions also have their own rhythms - certain signaling pathways, such as growth-promoting or inflammation-related signals, will have spatial differences in strength. The tumor center is often a place where proliferation is active, and related genes are frequently expressed there. But as soon as it reaches the edge, the signals of the immune response take the upper hand (Figure 1) (Du et al., 2023). In this way, the interior of the tumor presents a distinct "topographic" feature - cell types, signaling pathways, and molecular networks are interwoven at different positions, jointly forming its complex and hierarchical spatial structure. 2.2 The interaction between immune cells, fibroblasts and the vascular system in the microenvironment In the tumor microenvironment, various cells and blood vessels are like a complex web, none of which can do without the other. Fibroblasts are the most "worried". They secrete cytokines and adjust the extracellular matrix, restricting the movement of immune cells. Sometimes, they even "build walls" to prevent T cells from getting close (Mao et al., 2021). However, immune cells are not the passive party. The chemokines and growth factors they release can in turn stimulate fibroblasts and, incidentally, promote angiogenesis. The uneven distribution of blood vessels makes the situation even more complicated - some areas have abundant oxygen while others lack it, and as a result, the performance of cells varies greatly (Kim et al., 2022). All these seemingly chaotic interactions actually jointly maintain the delicate and unstable balance of the tumor microenvironment. 2.3 The role of spatial heterogeneity in tumorigenesis and drug resistance mechanisms A tumor is not a single entity but more like an ecosystem composed of different "terrains". The microenvironments at different locations vary greatly, with different levels of oxygen, nutrients, and cell types, which subject tumor cells to distinct survival pressures. Some subclones are more adapted in certain regions and gradually gain the upper hand, driving the tumor to evolve in new directions (Wang et al., 2024). However, this spatial disparity also brings trouble - drugs often fail to penetrate deep into the body, and the peripheral areas are frequently surrounded by fibroblasts and immunosuppressive cells, allowing cancer cells to take the opportunity to hide. The result is that the few cells that survive by chance will gradually develop drug resistance (Wu et al., 2025). Often, treatment failure and recurrence are not merely issues of drug efficacy, but rather the covert effect of this spatial heterogeneity. 3 Spatial Transcriptomics Technology and Its Data Characteristics 3.1 Review of mainstream spatial transcriptomics techniques (such as visium, slide-seq, MERFISH, etc.) There are currently several mainstream approaches to spatial transcriptomics, each with its own "temperament". Like 10x Visium and Slide-seq, they belong to the sequencing category, while MERFISH follows the imaging route. Visium is an array of spatial barcodes spread all over a slide to capture the transcripts in tissue sections. Slide-seq is finer. It marks positions with micron-sized beads, and the differences of individual cells can almost be seen (Rademacher et al., 2024). MERFISH is different. It relies on multiple rounds of fluorescence in situ hybridization to directly count RNA in tissues, capable of testing hundreds or even thousands of genes at a time (Liu et al., 2022). Some people care more about resolution, while others value gene coverage - from tens of micrometers to subcellular, from the entire transcriptome to specific gene sets, different technologies have their own trade-offs.
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