Single-Cell Transcriptomics-Guided Network Pharmacology of Traditional Chinese Medicine in Tumor Microenvironment Regulation
Keywords:
single-cell transcriptomics; network pharmacology; traditional Chinese medicine; tumor microenvironment; systems medicine; computational governance; data infrastructureAbstract
The tumor microenvironment is now understood as a heterogeneous, dynamically regulated ecosystem whose cellular states, molecular gradients, and spatial architectures jointly determine tumor progression and therapeutic response. Traditional Chinese Medicine formulations offer multi-component interventions that may modulate this ecosystem through coordinated action on multiple targets, yet their mechanistic opacity has limited clinical translation. This paper develops a systems-oriented framework in which single-cell transcriptomics functions as a high-resolution observational layer, while network pharmacology supplies an inferential architecture for mapping herbal constituents to cellular regulatory programs. The integration of these layers enables the identification of cell-type-specific responses, ligand-receptor perturbations, and multicellular network shifts associated with TCM exposure. We examine structural trade-offs among data granularity, computational complexity, model interpretability, and clinical generalizability. The discussion addresses modular architectures for data harmonization, network community detection, signature matching, and machine learning-based prioritization, together with considerations of data provenance, reproducibility, fairness, and regulatory oversight. Rather than proposing a single computational protocol, the paper emphasizes the governance of evidence generation, the sustainability of data infrastructures, and the policy conditions under which single-cell-guided network pharmacology can evolve into a trustworthy translational instrument. The resulting perspective situates TCM research within broader precision oncology and systems medicine agendas, highlighting both the promise and the infrastructural burdens of integrating traditional therapeutics with modern high-resolution molecular science.
References
1. Hanahan, D., & Weinberg, R. A. (2011). Hallmarks of cancer: The next generation. Cell, 144(5), 646–674.
2. Binnewies, M., Roberts, E. W., Kersten, K., Chan, V., Fearon, D. F., Merad, M., Coussens, L. M., Gabrilovich, D. I., Ostrand-Rosenberg, S., Hedrick, C. C., Vonderheide, R. H., Pittet, M. J., Jain, R. K., Zou, W., Howcroft, T. K., Woodhouse, E. C., Weinberg, R. A., & Krummel, M. F. (2018). Understanding the tumor immune microenvironment (TIME) for effective therapy. Nature Medicine, 24(5), 541–550.
3. Stuart, T., Butler, A., Hoffman, P., Hafemeister, C., Papalexi, E., Mauck, W. M., III, Hao, Y., Stoeckius, M., Smibert, P., & Satija, R. (2019). Comprehensive integration of single-cell data. Cell, 177(7), 1888–1902.
4. Papalexi, E., & Satija, R. (2018). Single-cell RNA sequencing to explore immune cell heterogeneity. Nature Reviews Immunology, 18(1), 35–45.
5. Hopkins, A. L. (2008). Network pharmacology: The next paradigm in drug discovery. Nature Chemical Biology, 4(11), 682–690.
6. Barabási, A.-L., & Oltvai, Z. N. (2004). Network biology: Understanding the cell's functional organization. Nature Reviews Genetics, 5(2), 101–113.
7. Li, S., & Zhang, B. (2013). Traditional Chinese medicine network pharmacology: Theory, methodology and application. Chinese Journal of Natural Medicines, 11(2), 110–120.
8. Lamb, J., Crawford, E. D., Peck, D., Modell, J. W., Blat, I. C., Wrobel, M. J., Lerner, J., Brunet, J.-P., Subramanian, A., Ross, K. N., Reich, M., Hieronymus, H., Wei, G., Armstrong, S. A., Haggarty, S. J., Clemons, P. A., Wei, R., Carr, S. A., Lander, E. S., & Golub, T. R. (2006). The Connectivity Map: Using gene-expression signatures to connect small molecules, genes, and disease. Science, 313(5795), 1929–1935.
9. Subramanian, A., Tamayo, P., Mootha, V. K., Mukherjee, S., Ebert, B. L., Gillette, M. A., Paulovich, A., Pomeroy, S. L., Golub, T. R., Lander, E. S., & Mesirov, J. P. (2005). Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles. Proceedings of the National Academy of Sciences, 102(43), 15545–15550.
10. Wang, Y., Yao, J., Sui, Y., Jiang, H., Ma, B., Lai, S., ... & Tan, N. (2026). HerbSyner_Finder: a network community-based model for identifying synergistic combinations from herbal medicines and complex systems. Targetome, 2(2).
11. Joyce, J. A., & Pollard, J. W. (2009). Microenvironmental regulation of metastasis. Nature Reviews Cancer, 9(4), 239–252.
12. Shannon, P., Markiel, A., Ozier, O., Baliga, N. S., Wang, J. T., Ramage, D., Amin, N., Schwikowski, B., & Ideker, T. (2003). Cytoscape: A software environment for integrated models of biomolecular interaction networks. Genome Research, 13(11), 2498–2504.
13. Kanehisa, M., & Goto, S. (2000). KEGG: Kyoto Encyclopedia of Genes and Genomes. Nucleic Acids Research, 28(1), 27–30.
14. Newman, D. J., & Cragg, G. M. (2020). Natural products as sources of new drugs over the nearly four decades from 01/1981 to 09/2019. Journal of Natural Products, 83(3), 770–803.
15. Vamathevan, J., Clark, D., Czodrowski, P., Dunham, I., Ferran, E., Lee, G., Li, B., Madabhushi, A., Shah, P., Spitzer, M., & Zhao, S. (2019). Applications of machine learning in drug discovery and development. Nature Reviews Drug Discovery, 18(6), 463–477.
16. Wilkinson, M. D., Dumontier, M., Aalbersberg, I. J., Appleton, G., Axton, M., Baak, A., Blomberg, N., Boiten, J.-W., da Silva Santos, L. B., Bourne, P. E., Bouwman, J., Brookes, A. J., Clark, T., Crosas, M., Dillo, I., Dumon, O., Edmunds, S., Evelo, C. T., Finkers, R., ... & Mons, B. (2016). The FAIR Guiding Principles for scientific data management and stewardship. Scientific Data, 3, 160018.
17. Wang, Y. (2025, June). RAGNet: Transformer–GNN–Enhanced Cox–Logistic Hybrid Model for Rheumatoid Arthritis Risk Prediction. In Proceedings of the 2025 International Conference on Health Informatization and Data Analytics (pp. 90-94).
18. Topol, E. J. (2019). High-performance medicine: The convergence of human and artificial intelligence. Nature Medicine, 25(1), 44–56.
19. Mittelstadt, B. D., Allo, P., Taddeo, M., Wachter, S., & Floridi, L. (2016). The ethics of algorithms: Mapping the debate. Big Data & Society, 3(2), 2053951716679679.
20. Zitvogel, L., Tesniere, A., & Kroemer, G. (2006). Cancer despite immunosurveillance: Immunoselection and immunosubversion. Nature Reviews Immunology, 6(10), 715–727.
21. Tsimberidou, A. M., Fountzilas, E., Nikanjam, M., & Kurzrock, R. (2020). Review of precision cancer medicine: Evolution of the treatment paradigm. Cancer Treatment Reviews, 86, 102019.
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