International Journal of Molecular Medical Science, 2024, Vol.14, No.5, 293-304 http://medscipublisher.com/index.php/ijmms 302 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. References Ahmed R., Zaman T., Chowdhury F., Mraiche F., Tariq M., Ahmad I.S., and Hasan A, 2022, Single-cell RNA sequencing with spatial transcriptomics of cancer tissues, International Journal of Molecular Sciences, 23(6): 3042. https://doi.org/10.3390/ijms23063042 Andersson A., Larsson L., Stenbeck L., Salmén F., Ehinger A., Wu S., Al-Eryani G., Roden D., Swarbrick A., Borg Å., Frisén J., Engblom C., and Lundeberg J., 2021, Spatial deconvolution of HER2-positive breast cancer delineates tumor-associated cell type interactions, Nature Communications, 12(1): 6012. https://doi.org/10.1038/s41467-021-26271-2 Bagaev A., Kotlov N., Nomie K., Svekolkin V., Gafurov A., Isaeva O., Osokin N., Kozlov I., Frenkel F., Gancharova O., Almog N., Tsiper M., Ataullakhanov R., and Fowler N., 2021, Conserved pan-cancer microenvironment subtypes predict response to immunotherapy, Cancer Cell, 39(6): 845-865. https://doi.org/10.1016/j.ccell.2021.04.014 Berglund E., Maaskola J., Schultz N., Friedrich S., Marklund M., Bergenstråhle J., Tarish F., Tanoglidi A., Vickovic S., Larsson L., Salmén F., Ogris C., Wallenborg K., Lagergren J., Ståhl P., Sonnhammer E., Helleday T., and Lundeberg J., 2018, Spatial maps of prostate cancer transcriptomes reveal an unexplored landscape of heterogeneity, Nature Communications, 9(1): 2419. https://doi.org/10.1038/s41467-018-04724-5 Bian S., Hou Y., Zhou X., Li X., Yong J., Wang Y., Wang W., Yan J., Hu B., Guo H., Wang J., Gao S., Mao Y., Dong J., Zhu P., Xiu D., Yan L., Wen L., Qiao J., Tang F., and Fu W., 2018, Single-cell multiomics sequencing and analyses of human colorectal cancer, Science, 362(6418): 1060-1063. https://doi.org/10.1126/science.aao3791 Biswas A., Ghaddar B., Riedlinger G., and De S., 2022, Inference on spatial heterogeneity in tumor microenvironment using spatial transcriptomics data, Computational and Systems Oncology, 2(3): e21043. https://doi.org/10.1002/cso2.1043 Chen S., Shinkle A., Zhao Y., Mo C., Houston A., Lal P., Herndon J., Fields R., Gillanders W., Chen F., and Ding L., 2022, Spatial transcriptomics and multiplexed imaging to explore tumor heterogeneity and immune complexity, Cancer Research, 82(12S): 1710. https://doi.org/10.1158/1538-7445.am2022-1710
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