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Abstract

With advancements in technology, single-cell RNA sequencing has emerged as a powerful tool in cancer drug discovery. This technique enables the construction of gene expression profiles at the individual cell level, offering detailed insights into cellular heterogeneity and molecular pathways involved in tumor development. It enables researchers to gain a deeper understanding of tumor heterogeneity. Researchers can study cell subpopulations and gene expression patterns. This understanding helps in identifying potential drug targets. Additionally, it aids in predicting therapeutic responses. This high-resolution gene expression analysis provides a new perspective and opportunity for cancer drug development, which is expected to accelerate the discovery and development process of new anti-cancer drugs. This article provides a comprehensive overview of the basic processes and developmental trajectory of single-cell RNA sequencing technology, with a particular emphasis on its applications in various aspects of cancer drug discovery. It also addresses the challenges faced by single-cell RNA sequencing and potential future directions. This review aims to enhance readers’ understanding of single-cell sequencing, inspire new ideas for oncology drug development, and support the translation of clinical research into practice, ultimately enabling physicians to design more precise and personalized treatment strategies.

© 2025 The Author(s). Published by Bentham Science Publishers.
This is an open access article published under CC BY 4.0 https://creativecommons.org/licenses/by/4.0/legalcode
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2025-07-30
2025-12-17

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Single-Cell Transcriptomics: Technical Advances, Applications and Challenges in Cancer Drug Discovery
Bentham Science Publishers ; https://doi.org/10.2174/0115680096372729250715050452
/content/journals/ccdt/10.2174/0115680096372729250715050452
/content/journals/ccdt/10.2174/0115680096372729250715050452
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  • Article Type:     Review Article
Keywords: cancer drug discovery ; drug resistance ; tumor microenvironment ; heterogeneity ; Single-cell RNA sequencing ; target identification

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