Cancer Genetics and Epigenetics 2024, Vol.12, No.1, 55-65 http://medscipublisher.com/index.php/cge 62 combining bioinformatics methods and network analysis technology, the interaction network between cells can also be constructed to reveal the communication mechanisms and regulatory relationships between different cell types (Zhang et al., 2021). Cao et al. (2022) used single cell sequencing (scRNA-seq) technology to study the role of tumor heterogeneity in tumor progression, especially how to track gene expression or mutations in heterogeneous cells by measuring the entire transcriptome of single cells. situation, assess the clonal origins of cancer cells, and determine the selective evolution of different subpopulations of cancer cells. Zheng et al. (2021) performed single-cell RNA sequencing of T cells from more than 300 patients in 21 cancer types, revealing differences in immune cell types related to tumor characteristics, providing a basis for tumor-infiltrating T cells in the tumor microenvironment. Status and abundance provide a systematic comparison. These studies not only contribute to a deeper understanding of the interactions between tumor cells and the complexity of the tumor ecosystem, but may also provide new ideas for the development of new treatment strategies. For example, by interfering with the interaction between tumor cells and immune cells, the anti-tumor response of the immune system can be enhanced; by regulating the interaction between tumor cells and stromal cells, processes such as tumor invasion and metastasis can be inhibited. 4 Summary and Outlook 4.1 Research summary and main findings This study deeply explores the application prospects of artificial intelligence in the field of drug design and the ethical considerations it brings, while focusing on the unique value of single-cell RNA sequencing technology in the study of tumor heterogeneity. Through a systematic literature review, in-depth data analysis, and interdisciplinary research methods, the great potential of artificial intelligence technology in drug design is revealed, as well as the ethical challenges that must be faced along with this development. Alizadeh et al (2015) discussed the extent of tumor heterogeneity, an emerging topic that researchers are only beginning to understand, exploring how genetic and epigenetic heterogeneity influence tumor evolution and clinical progression. Research has found that artificial intelligence technology can play an important role in all stages of drug design through powerful computing power and advanced algorithm models. From target identification to molecular screening to clinical trial optimization, artificial intelligence can significantly improve research and development efficiency, reduce costs, and is expected to bring revolutionary breakthroughs to new drug development. However, with the rapid development of technology, issues such as how to ensure that the application of artificial intelligence complies with ethical norms, protects patient privacy, and avoids data abuse have become increasingly prominent (Gawad et al., 2016). In terms of studying tumor heterogeneity, this study further confirms the great value of single-cell RNA sequencing technology. This technology can comprehensively reveal the heterogeneity of tumors at the single-cell level, including differences in gene expression of different cell subpopulations, the complexity of regulatory networks, and the interaction between tumor cells and the microenvironment. These findings not only contribute to a deeper understanding of tumor pathogenesis, but also provide new ideas for the development of therapeutic strategies targeting specific cell subpopulations or microenvironments. This study not only provides strong theoretical support and practical guidance for the application of artificial intelligence in drug design, but also brings new perspectives and methods to the study of tumor heterogeneity. With the continuous advancement of technology and the deepening of interdisciplinary cooperation, artificial intelligence and single-cell RNA sequencing technology will play an increasingly important role in future medical research.
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