International Journal of Molecular Medical Science, 2024, Vol.14, No.5, 293-304 http://medscipublisher.com/index.php/ijmms 295 2.3 Impact of TME on cancer progression and treatment response The TME significantly impacts colon cancer progression and response to treatment. The interactions between cancer cells and the surrounding stromal cells, ECM, and signaling molecules create a supportive niche that promotes tumor growth, invasion, and resistance to therapy. For instance, hypoxia within the TME can lead to the activation of hypoxia-inducible factors (HIFs), which drive angiogenesis and metabolic adaptation of cancer cells. Additionally, the immune landscape of the TME, characterized by the presence of immunosuppressive cells and cytokines, can inhibit effective anti-tumor immune responses and contribute to immune evasion. Understanding the complex dynamics of the TME is crucial for developing targeted therapies that can disrupt these interactions and improve treatment outcomes for colon cancer patients (Romero-López et al., 2017; Pearce et al., 2018; Wei et al., 2020; Price et al., 2022). By mapping the spatial organization and molecular characteristics of the TME using advanced techniques such as spatial transcriptomics, researchers can gain deeper insights into the heterogeneity and functional states of different cell types within the tumor. This knowledge can inform the development of novel therapeutic strategies aimed at modulating the TME to enhance anti-tumor immunity and overcome resistance to conventional treatments (Ospina et al., 2022; Price et al., 2022; Franses et al., 2022). 3 Spatial Transcriptomics: An Overview 3.1 Definition and principles of spatial transcriptomics Spatial transcriptomics (ST) is an innovative technique that allows for the measurement of gene expression within the spatial context of intact tissue sections. Unlike traditional transcriptomics, which often loses spatial information, ST retains the spatial localization of gene expression, providing a more comprehensive understanding of the tissue architecture and cellular interactions within the tumor microenvironment (TME) (Franses et al., 2022; Hu et al., 2022; Yu et al., 2022). This technique involves capturing RNA from tissue sections, followed by sequencing and mapping the gene expression data back to the tissue's spatial coordinates, thus enabling the visualization of gene expression patterns in situ (Ahmed et al., 2022; Wang et al., 2022). 3.2 Technological advances in spatial transcriptomics Recent advancements in ST technologies have significantly enhanced their resolution and accuracy. Platforms such as 10x Visium and Molecular Cartography have been developed to provide high sensitivity and single-cell resolution, allowing for detailed mapping of gene expression within specific tissue regions (Chen et al., 2022; Franses et al., 2022). These technologies have been applied to various cancer types, including prostate cancer, liver cancer, and melanoma, revealing intricate details of the TME and uncovering novel interactions between cancer cells and their surrounding stromal and immune cells (Berglund et al., 2018; Thrane et al., 2018; Wu et al., 2021). Additionally, the integration of ST with other omics techniques, such as single-cell RNA sequencing and multiplexed imaging, has further expanded the capabilities of spatial profiling, enabling a multidimensional analysis of the TME (Ahmed et al., 2022; Hu et al., 2022). 3.3 Comparison with other omics techniques While traditional omics techniques, such as bulk RNA sequencing and single-cell RNA sequencing, provide valuable insights into gene expression and cellular heterogeneity, they often lack spatial context. Bulk RNA sequencing averages gene expression across a large number of cells, potentially masking important spatial variations (Ahmed et al., 2022). Single-cell RNA sequencing offers high-resolution data at the individual cell level but typically loses spatial information during tissue dissociation (Wang et al., 2022). In contrast, ST preserves the spatial arrangement of cells, allowing for the study of gene expression in relation to tissue architecture and cellular interactions (Hu et al., 2022; Yu et al., 2022). This spatial context is crucial for understanding the complex dynamics of the TME, including the spatial heterogeneity of cancer cells and the distribution of immune and stromal cells (Thrane et al., 2018; Chen et al., 2022; Ospina et al., 2022). By combining the strengths of traditional omics techniques with spatial information, ST provides a more holistic view of the molecular landscape within tumors, facilitating the identification of novel therapeutic targets and improving cancer prognosis and treatment strategies (Franses et al., 2022; Hu et al., 2022; Yu et al., 2022).
RkJQdWJsaXNoZXIy MjQ4ODYzNQ==