1 Introduction

The tumor microenvironment (TME) is a complex and dynamic ecosystem composed of cancer cells, stromal cells, immune cells, and extracellular matrix components. It plays a crucial role in tumor progression, metastasis, and response to therapy. Understanding the TME is essential for developing effective cancer treatments and improving patient outcomes. Recent advancements in spatial transcriptomics (ST) have provided unprecedented insights into the spatial organization and heterogeneity of the TME, enabling researchers to map gene expression patterns within the tissue context (Hu et al., 2022; Ospina et al., 2022; Yu et al., 2022).

Spatial transcriptomics technologies have been instrumental in characterizing the spatial heterogeneity of various cancers, including colon cancer. These technologies allow for the simultaneous capture of gene expression data and spatial information, facilitating the identification of distinct cellular neighborhoods and their interactions within the TME (Andersson et al., 2021; Elosua-Bayes et al., 2021; Price et al., 2022). By integrating spatial transcriptomics with other high-throughput sequencing methods, researchers can gain a comprehensive understanding of the molecular and cellular landscape of tumors, which is critical for identifying novel therapeutic targets and biomarkers (Berglund et al., 2018; Wang et al., 2022; Thrane et al., 2018; Wang et al., 2021).

This study aims to provide a valuable resource for researchers and clinicians interested in leveraging spatial transcriptomics to advance our understanding of the TME in colon cancer and improve therapeutic strategies.

2 Colon Cancer and Tumor Microenvironment

2.1 Overview of colon cancer pathogenesis

Colon cancer, also known as colorectal cancer (CRC), is a major health concern worldwide. It arises from the epithelial cells lining the colon or rectum and progresses through a series of well-defined stages, from benign adenomas to malignant carcinomas. The pathogenesis of colon cancer involves a complex interplay of genetic mutations, epigenetic alterations, and environmental factors. Key genetic mutations often include alterations in the APC, KRAS, and TP53 genes (Figure 1), which drive the transformation of normal colonic epithelium into adenomatous polyps and eventually invasive cancer. Additionally, the tumor microenvironment (TME) plays a crucial role in the progression and metastasis of colon cancer by influencing tumor cell behavior and response to therapy (Wei et al., 2020; Price et al., 2022).

2.2 Components of the tumor microenvironment

2.2.1 Cellular components

The TME of colon cancer is composed of a diverse array of cellular components, including cancer cells, immune cells, fibroblasts, endothelial cells, and other stromal cells (Figure 1). Immune cells, such as T cells, macrophages, and dendritic cells, infiltrate the tumor and can either promote or inhibit tumor growth depending on their activation state and the cytokine milieu. Fibroblasts, particularly cancer-associated fibroblasts (CAFs), secrete extracellular matrix (ECM) components and growth factors that support tumor growth and angiogenesis. Endothelial cells form the blood vessels that supply the tumor with nutrients and oxygen, facilitating tumor growth and metastasis (Romero-López et al., 2017; Wei et al., 2020; Price et al., 2022).

2.2.2 Extracellular matrix and signaling molecules

The ECM is a critical component of the TME, providing structural support and biochemical signals that influence tumor cell behavior. The ECM is composed of various proteins, including collagen, elastin, laminin, and fibronectin, as well as proteoglycans and glycoproteins. These components are dynamically remodeled during cancer progression, leading to changes in tissue stiffness and the creation of a pro-tumorigenic environment. Signaling molecules such as cytokines, chemokines, and growth factors are also abundant in the TME and play key roles in modulating immune responses, promoting angiogenesis, and facilitating tumor cell invasion and metastasis (Romero-López et al., 2017; Kim et al., 2021; Lu et al., 2012).

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).

In summary, spatial transcriptomics represents a significant advancement in the field of cancer research, offering unparalleled insights into the spatial organization of gene expression within the TME. Its integration with other omics technologies holds great promise for advancing our understanding of tumor biology and developing more effective cancer therapies.

4 Application of Spatial Transcriptomics in Colon Cancer Research

4.1 Mapping cellular heterogeneity in TME

Spatial transcriptomics (ST) has significantly advanced our understanding of the tumor microenvironment (TME) by enabling the mapping of cellular heterogeneity within tumors. This technology allows for the visualization and quantification of gene expression across different spatial regions of the tumor, providing insights into the complex cellular composition and interactions within the TME. For instance, the development of tools like spatialGE facilitates the quantification and visualization of tumor heterogeneity, offering spatial heterogeneity statistics and spot-level cell deconvolution to better understand the TME (Ospina et al., 2022). Additionally, ST has been used to profile spatial heterogeneity in various cancers, revealing unique tumor microenvironments and aiding in the identification of novel prognostic factors (Yu et al., 2022).

4.2 Identifying spatial gene expression patterns

Identifying spatial gene expression patterns is crucial for understanding the functional architecture of tumors. ST technology enables the detection of gene expression gradients and spatially distinct gene expression profiles within the tumor. For example, studies have shown that spatial transcriptomics can delineate the extent of cancer foci more accurately than traditional pathologist annotations, highlighting gene expression changes during cancer progression (Berglund et al., 2018). Moreover, spatial transcriptomics has been applied to various cancer types to uncover spatially correlated patterns in gene expression, which are essential for understanding tumor growth dynamics and cancer hallmarks (Berglund et al., 2022).

4.3 Deciphering cell-cell interactions

Deciphering cell-cell interactions within the TME is vital for understanding tumor progression and response to treatment. Spatial transcriptomics provides a powerful tool to infer molecular changes resulting from tumor and immune cell interactions. For instance (Figure 2), novel bioinformatics pipelines have been developed to infer biological patterns from ST data, enabling the identification of molecular interactions within the TME (Berglund et al., 2018). These interactions can be further analyzed to understand the pathways involved in tumorigenesis and resistance to immune attack, providing insights into the key drivers of cancer progression (Berglund et al., 2018).

Additionally, spatial transcriptomics has been used to study the interactions between tumor cells and the extracellular matrix, revealing spatial patterns that impact tumor growth and immune modulation (Berglund et al., 2022).

4.4 Case studies in colon cancer

Several case studies have demonstrated the application of spatial transcriptomics in colon cancer research. For example, spatialGE has been used to visualize and quantify the heterogeneity of the colon cancer TME (Figure 3), providing insights into the spatial distribution of gene expression and its association with clinical data (Ospina et al., 2022). 

Another study highlighted the use of spatial transcriptomics to profile the spatial heterogeneity of immune cell infiltration in colon cancer, revealing distinct immune cell patterns that correlate with tumor regions (Yu et al., 2022).

These case studies underscore the potential of spatial transcriptomics to enhance our understanding of colon cancer biology and improve diagnostic and therapeutic strategies.

5 Technological and Methodological Considerations

5.1 Sample preparation and tissue handling

Effective sample preparation and tissue handling are critical for the success of spatial transcriptomics in mapping the tumor microenvironment (TME) in colon cancer. Proper fixation and embedding of tissue samples are essential to preserve the spatial context of gene expression. Techniques such as formalin-fixed paraffin-embedded (FFPE) tissue processing are commonly used, allowing for the simultaneous assessment of gene and protein expression (Wang et al., 2021). Additionally, the use of high-resolution imaging and staining methods, such as multiplex immunohistochemistry, ensures accurate identification and localization of various cell types within the TME (Trigos et al., 2020).

5.2 Data acquisition and processing

Data acquisition in spatial transcriptomics involves capturing spatially resolved gene expression data from tissue sections. Technologies like 10X Genomics Visium and GeoMx Digital Spatial Profiler (DSP) are widely used for this purpose. These platforms enable high-plex RNA and protein profiling while maintaining spatial information (Wang et al., 2021). The integration of imaging techniques, such as MERFISH and Slide-seq, further enhances the resolution and depth of spatial transcriptomic data (Wang et al., 2021; Price et al., 2022). Accurate data processing, including image alignment and spot-level expression quantification, is crucial for downstream analyses.

5.3 Computational Tools for Data Analysis

The analysis of spatial transcriptomic data requires sophisticated computational tools to handle the complexity and volume of the data. These tools facilitate the identification of spatial patterns, cell types, and gene expression gradients within the TME.

5.3.1 Image-Based transcriptomics

Image-based transcriptomics combines high-resolution imaging with transcriptomic data to map gene expression at the cellular level. Techniques such as MERFISH and FISSEQ allow for the visualization and quantification of RNA molecules within tissue sections, providing insights into the spatial organization of the TME (Wang et al., 2021; Price et al., 2022). Deep learning models, trained on histological images and spatial transcriptomic data, can predict cell type distributions and gene expression patterns, enhancing the accuracy of spatial mapping (Choi et al., 2022; Fatemi et al., 2023).

5.3.2 Statistical and machine learning approaches

Statistical and machine learning approaches are essential for analyzing spatial transcriptomic data. Tools like spatialGE provide visualizations and quantification of TME heterogeneity through gene expression surfaces and spatial heterogeneity statistics (Ospina et al., 2022). Machine learning algorithms, such as convolutional neural networks, can infer cell types and spatial relationships from histological images, enabling the integration of spatial transcriptomic data with other omics data (Choi et al., 2022; Fatemi et al., 2023). Additionally, R packages like SPIAT offer a suite of tools for spatial data processing, quality control, and analysis, facilitating the extraction of meaningful insights from spatial transcriptomic datasets (Trigos et al., 2020).

5.4 Integration with other omics data

Integrating spatial transcriptomics with other omics data, such as proteomics and single-cell RNA sequencing, provides a comprehensive view of the TME. Combined spatial transcriptomics and proteomics approaches enable the simultaneous analysis of RNA and protein expression, revealing the complex interactions between tumor cells and their microenvironment (Wang et al., 2021; Femel et al., 2023). This multi-omic integration enhances our understanding of tumor heterogeneity, immune cell infiltration, and the molecular mechanisms driving cancer progression and therapy resistance (Hu et al., 2022; Wang et al., 2021; Femel et al., 2023).

6 Insights into Tumor Microenvironment Dynamics

6.1 Spatial distribution of immune cells

The spatial distribution of immune cells within the tumor microenvironment (TME) is a critical factor influencing cancer progression and therapeutic outcomes. In colorectal cancer (CRC), spatially resolved transcriptomic analyses have revealed distinct patterns of immune cell infiltration, particularly between mismatch repair deficient (MMRd) and proficient (MMRp) tumors. MMRd tumors exhibit higher cytolytic T cell infiltrates compared to MMRp tumors, indicating a more robust immune response (Price et al., 2022). Similarly, in triple-negative breast cancer (TNBC), spatial heterogeneity of immune markers such as CD3, CD4, CD8, CD20, and FoxP3 has been quantified, showing that the invasive front (IF) of tumors tends to have higher densities of immune cells compared to central tumor (CT) and normal tissue (N) regions (Mi et al., 2020). These findings underscore the importance of spatial organization in understanding immune dynamics within the TME.

6.2 Stromal and cancer cell interactions

Stromal cells within the TME play a pivotal role in cancer progression through their interactions with cancer cells. Single-cell RNA sequencing of nasopharyngeal carcinoma (NPC) has identified multiple stromal subpopulations and their genetic profiles, highlighting the complex interplay between stromal and cancer cells (Gong et al., 2021). These interactions are further elucidated by 3D in vitro models, which mimic the TME and allow for the study of tumor-stroma interactions in a controlled environment (Rodrigues et al., 2021). The reprogramming of stromal cells by cancer cells through the secretion of cytokines and chemokines is a key mechanism that supports tumor survival and metastasis (Hinshaw and Shevde, 2019). Understanding these interactions is crucial for developing targeted therapies that disrupt the supportive role of stromal cells in cancer progression.

6.3 Vascular and hypoxic niches

The TME is characterized by the presence of vascular and hypoxic niches, which contribute to tumor heterogeneity and resistance to therapy. In glioblastoma (GBM), spatial analysis has revealed that genetically distinct subpopulations of tumor cells are influenced by the hypoxic and angiogenic niches within the TME. These niches create selective pressures that drive the evolution of cancer cells, leading to the enrichment of specific clones with advantageous mutations (Onubogu et al., 2022). The presence of hypoxic regions within tumors is associated with poor prognosis and therapeutic resistance, making it a critical area of study for improving cancer treatment outcomes.

6.4 Evolution of TME during cancer progression

The TME evolves dynamically during cancer progression, with significant implications for disease prognosis and treatment response. In colon cancer, a TME-related gene signature has been identified that correlates with patient survival and response to immunotherapy. This signature includes genes involved in immune-related pathways and highlights the prognostic value of TME composition (Chen et al., 2021). Additionally, the spatial organization of cells within the TME changes over time, as seen in CRC, where the distribution of immune and stromal cells shifts between MMRd and MMRp tumors (Price et al., 2022). These evolutionary changes in the TME underscore the need for longitudinal studies to fully understand the temporal dynamics of tumor-stroma interactions and their impact on cancer progression.

By integrating spatial transcriptomics and single-cell sequencing technologies, researchers can gain deeper insights into the complex dynamics of the TME, paving the way for the development of more effective cancer therapies. The spatial distribution of immune cells, stromal and cancer cell interactions, vascular and hypoxic niches, and the evolution of the TME during cancer progression are all critical factors that influence tumor behavior and patient outcomes. Understanding these dynamics is essential for advancing cancer research and improving therapeutic strategies.

7 Clinical Implications of Spatial Transcriptomics

7.1 Biomarker discovery and validation

Spatial transcriptomics (ST) has revolutionized the field of biomarker discovery by providing high-resolution spatial data that can be correlated with clinical outcomes. This technology allows for the identification of spatially-resolved gene expression patterns within the tumor microenvironment (TME), which can serve as potential biomarkers for cancer prognosis and treatment response (Figure 1). For instance, the spatial domain analysis platform SpAn has demonstrated the ability to predict the 5-year risk of colorectal cancer recurrence with high accuracy, significantly outperforming current methods (Uttam et al., 2020). Additionally, the classification of tumor microenvironment subtypes through transcriptomic analysis has identified distinct TME subtypes that correlate with patient response to immunotherapy, suggesting their potential as generalized immunotherapy biomarkers across multiple cancer types (Bagaev et al., 2021).

7.2 Precision medicine and personalized treatment strategies

The integration of spatial transcriptomics into clinical practice holds great promise for precision medicine. By providing a detailed map of gene expression within the TME, ST enables the identification of specific molecular and cellular interactions that drive tumor progression and response to therapy. This information can be used to tailor treatment strategies to the unique molecular profile of each patient's tumor. For example, the quantitative characterization of CD8+ T cell clustering and spatial heterogeneity in solid tumors has been shown to correlate with treatment outcomes, indicating that spatial metrics can be used to match patients to the most appropriate therapies (Gong et al., 2019). Furthermore, the WINTHER precision medicine clinical trial demonstrated that the use of transcriptome analysis, in combination with genomic profiling, increased the number of targetable molecular alterations, thereby improving treatment matching and patient outcomes (Tsimberidou et al., 2022).

7.3 Predictive models for treatment response

Spatial transcriptomics provides critical data for the development of predictive models that can forecast treatment response. By capturing the spatial heterogeneity of the TME, ST allows for the creation of models that account for the complex interactions between tumor cells and their microenvironment. For instance, the spatial profiling of non-small cell lung carcinoma tissues revealed distinct spatial signatures associated with immunotherapy response, highlighting the potential of ST to inform predictive models for immunotherapy outcomes (Kulasinghe et al., 2022). Additionally, the conserved pan-cancer microenvironment subtypes identified through transcriptomic analysis have been shown to predict response to immunotherapy across multiple cancer types, further underscoring the utility of ST in developing robust predictive models (Bagaev et al., 2021).

7.4 Challenges and future directions in clinical translation

Despite the promising clinical implications of spatial transcriptomics, several challenges must be addressed to fully realize its potential in clinical settings. One major challenge is the standardization of ST technologies and data analysis methods to ensure reproducibility and comparability across studies. Additionally, the integration of ST data with other -omics data, such as genomics and proteomics, is necessary to provide a comprehensive understanding of tumor biology and improve biomarker discovery (Hu et al., 2022; Tsimberidou et al., 2022). Future research should also focus on the development of more sophisticated computational tools and algorithms to handle the large and complex datasets generated by ST. Finally, clinical validation of ST-based biomarkers and predictive models is essential to establish their utility in guiding treatment decisions and improving patient outcomes (Li et al., 2022; Yu et al., 2022). Addressing these challenges will pave the way for the successful translation of spatial transcriptomics into routine clinical practice, ultimately enhancing the precision and effectiveness of cancer therapies.

8 Future Perspectives and Research Directions

8.1 Emerging technologies and innovations

The field of spatial transcriptomics is rapidly evolving, with new technologies and methodologies continually being developed to enhance our understanding of the tumor microenvironment (TME) in colon cancer. Recent advancements include high-plex molecular profiling technologies such as 10X Visium, GeoMx Digital Spatial Profiler (DSP), and multiplex ion-beam imaging (MIBI), which allow for detailed spatial mapping of RNA and protein expression within the TME (Wang et al., 2021). Additionally, novel single-cell multiomics approaches, such as scTrio-seq, have been employed to simultaneously analyze mutations, transcriptome, and methylome within colorectal cancer tumors, providing deeper insights into tumor heterogeneity and evolution (Bian et al., 2018). These emerging technologies promise to revolutionize our ability to dissect the complex interactions within the TME and identify novel therapeutic targets.

8.2 Integration with single-cell and multi-omics data

Integrating spatial transcriptomics with single-cell and multi-omics data is a promising direction for future research. Single-cell RNA sequencing (scRNA-seq) techniques have already demonstrated their potential in revealing cellular diversity and interactions within the TME (Ahmed et al., 2022). Combining these techniques with spatial transcriptomics can provide a more comprehensive view of the spatial organization and functional states of different cell types within the tumor. For instance, the integration of scRNA-seq with spatially resolved transcriptomics has been shown to enhance our understanding of the spatial heterogeneity and molecular dynamics in colorectal cancer (Price et al., 2022). Furthermore, multi-omics approaches that combine genomic, transcriptomic, and proteomic data can offer a holistic view of the TME, facilitating the identification of key molecular drivers of cancer progression and resistance (Lewis et al., 2021; Hu et al., 2022).

8.3 Potential for real-time spatial mapping in clinical settings

The potential for real-time spatial mapping of the TME in clinical settings is an exciting prospect. Technologies such as MERFISH, which can simultaneously capture and measure the distribution of hundreds to thousands of RNA species within single cells, are paving the way for real-time spatial profiling in clinical diagnostics (Price et al., 2022). The ability to map the spatial organization of cells and their interactions in real-time could significantly improve the accuracy of cancer diagnosis and the monitoring of treatment responses. Additionally, software tools like spatialGE, which provide visualizations and quantification of tumor heterogeneity, could be integrated into clinical workflows to assist in the interpretation of spatial transcriptomics data and its correlation with clinical outcomes (Ospina et al., 2022).

8.4 Ethical and practical considerations

As spatial transcriptomics technologies advance, several ethical and practical considerations must be addressed. The collection and analysis of spatially resolved transcriptomic data involve handling large volumes of sensitive patient information, raising concerns about data privacy and security. Ensuring that patient consent is obtained and that data is anonymized and securely stored is crucial. Additionally, the high cost and technical complexity of these technologies may limit their accessibility and widespread adoption in clinical settings. Efforts should be made to develop cost-effective and user-friendly platforms to democratize access to spatial transcriptomics. Finally, the interpretation of spatial transcriptomics data requires specialized expertise, highlighting the need for interdisciplinary collaboration and training programs to equip researchers and clinicians with the necessary skills (Lewis et al., 2021; Yu et al., 2022).

In conclusion, the future of spatial transcriptomics in mapping the TME in colon cancer is promising, with emerging technologies, integration with multi-omics data, and potential clinical applications driving the field forward. Addressing ethical and practical challenges will be essential to fully realize the potential of these innovative approaches in improving cancer diagnosis and treatment.

9 Concluding Remarks

The application of spatial transcriptomics (ST) has significantly advanced our understanding of the tumor microenvironment (TME) in colon cancer. Key findings from various studies highlight the ability of ST to map gene expression with spatial context, revealing the heterogeneity and complex interactions within the TME. For instance, spatialGE provides tools for visualizing and quantifying TME heterogeneity, enabling comparisons with clinical data. Similarly, SPOTlight integrates ST with single-cell RNA sequencing to accurately map cell types and states within tissues, enhancing our understanding of tissue organization and function. Studies have also demonstrated the utility of ST in identifying spatial patterns of immune cell infiltration and their interactions with tumor cells, which are crucial for understanding immune responses and therapy outcomes.

The insights gained from spatial transcriptomics have profound implications for understanding the TME in colon cancer. By providing a high-resolution map of cellular interactions and gene expression profiles, ST technologies enable the identification of distinct cellular neighborhoods and interaction networks within tumors. This spatial information is critical for elucidating the roles of different cell types in tumor progression and response to therapies. For example, the identification of immune cell-rich regions and their spatial relationships with tumor cells can inform the development of targeted immunotherapies. Additionally, the ability to map gene expression gradients and heterogeneity within the TME can lead to the discovery of novel prognostic markers and therapeutic targets.

The integration of spatial transcriptomics into cancer research represents a paradigm shift in our approach to studying the TME. As these technologies continue to evolve, they will provide even more detailed and comprehensive views of the spatial organization and functional dynamics within tumors. Future research should focus on combining ST with other omics technologies, such as proteomics and metabolomics, to achieve a multi-dimensional understanding of the TME. Moreover, the development of more sophisticated computational tools for data integration and analysis will be essential for fully leveraging the potential of ST. Ultimately, these advancements will pave the way for more precise and personalized cancer therapies, improving outcomes for patients with colon cancer and other malignancies.

Acknowledgments

The authors extend sincere thanks to two anonymous peer reviewers for their feedback on the manuscript of this study.

Conflict of Interest Disclosure

The authors affirm that this research was conducted without any commercial or financial relationships that could be construed as a potential conflict of interest.

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