Cancer Genetics and Epigenetics 2024, Vol.12, No.1, 55-65 http://medscipublisher.com/index.php/cge 61 subpopulations obtained through single-cell RNA sequencing technology has more biological significance and clinical application value. It can help us better understand the pathogenesis of tumors, predict patients' treatment response and prognosis, and even guide the development of personalized treatment strategies (Janiszewska, 2020). 3.2 Analysis of gene expression differences and regulatory networks In the process of exploring tumor heterogeneity, understanding the differences in gene expression between different cell subpopulations and how these differences are regulated is a crucial step. This understanding not only helps to reveal the mechanisms of tumor occurrence and development, but may also provide ideas for new treatment strategies. Single-cell RNA sequencing technology is a powerful tool for analyzing the expression differences and regulatory networks of these genes (Zeisel et al., 2018). Traditional gene expression analysis methods often can only give the average expression level of a cell population and cannot accurately reflect the differences between individual cells. Single-cell RNA sequencing technology can measure the transcriptome of individual cells, revealing subtle but important gene expression changes among cell subpopulations. These changes may involve specific transcription factors, signaling pathways, or epigenetic modifications, which together form a complex gene regulatory network. By comparing the gene expression profiles of different cell subpopulations, it is possible to identify which genes are up-regulated or down-regulated in specific subpopulations, and then speculate on the biological processes in which these genes may be involved. In addition, by combining bioinformatics methods and network analysis technology, the interaction network between genes can also be constructed to reveal the regulatory relationship between them. For example, certain transcription factors may regulate processes such as cell proliferation, apoptosis, or invasion by activating or inhibiting the expression of downstream target genes (Lafzi et al., 2018). He et al. (2020) performed single-cell RNA sequencing (scRNA-seq) on early-stage lung adenocarcinoma patients with EGFR mutations, revealing the heterogeneous tumor and immune cell populations in the tumor microenvironment (TME), providing a basis for understanding cells in the TME. provide unique insights into the complex interactions between. Huang et al. (2023) proposed a new gene selection method, automatic associated feature learning (AAFL), to automatically identify different gene features of different cell subpopulations (cancer subtypes), which provides a new way to understand cell heterogeneity and complex tumors. Ecosystems provide important insights. This in-depth analysis of gene expression differences and regulatory networks not only contributes to a more comprehensive understanding of tumor heterogeneity, but may also provide new targets for therapeutic strategies targeting specific cell subpopulations. In the future, with the continuous development and improvement of technology, single-cell RNA sequencing will play an increasingly important role in the field of tumor research and move towards the goal of personalized medicine. 3.3 Research on interactions between tumor cells In the complex tumor ecosystem, tumor cells do not exist in isolation. They interact closely with surrounding stromal cells, immune cells, and other types of cells, forming an intricate cellular communication network. These interactions have profound effects on tumor growth, invasion, metastasis, and treatment resistance. Therefore, in-depth study of the interactions between tumor cells is of great significance for revealing the pathogenesis of tumors and developing new treatment strategies (Zeisel et al., 2018). Single-cell RNA sequencing technology provides new perspectives and methods for studying interactions between tumor cells. Single-cell RNA sequencing technology can capture the transcriptome information of single cells and study the interactions between tumor cells and other cell types at the single-cell level.Through single-cell RNA sequencing, multiple cell types in tumor samples can be sequenced and analyzed simultaneously, including tumor cells, immune cells, stromal cells, etc. In this way, gene expression profiles can be compared between different cell types and key genes and signaling pathways related to cell-cell interactions can be identified. In addition,
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