Abstract
Transcriptomic profiling of Traditional Chinese Medicine (TCM) perturbations is essential for elucidating the molecular mechanisms of therapeutic interventions. Although data from TCM treatment experiments are scattered across public repositories, a comprehensive, harmonized dataset remains unavailable due to heterogeneous experimental designs and inconsistent metadata. Here, we present a curated, harmonized resource comprising 362 human gene expression profiles derived from 27 TCMs and 137 TCM-derived ingredients spanning 26 human disease contexts, re-processed via a unified bioinformatics pipeline. This atlas captures TCM-induced genome-wide alterations in both protein-coding genes and long non-coding RNAs. We confirmed the dataset’s biological fidelity by validating the high reproducibility of the dataset, the enrichment of known pharmacological targets, and recapitulated the well-established therapeutic associations between TCM and disease treatment. This standardized dataset serves as a foundational resource for researchers to systematically investigate therapeutic mechanisms and predict clinical indications of TCM.
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Data availability
The harmonized gene-level expression matrices and associated metadata generated in this study are publicly available at Figshare: https://doi.org/10.6084/m9.figshare.31094347.
Code availability
The bioinformatic analyses were conducted using R statistical software (version 4.4.3). No custom algorithms or software were developed for this study; all analyses utilized standard functions from publicly available R packages. Data cleaning and manipulation were performed using dplyr (v1.1.4) and stringr (v1.6.0). Differential expression analysis was conducted using limma (v3.62.2). GO and KEGG pathway enrichment analyses, as well as Gene Set Enrichment Analysis (GSEA), were performed using clusterProfiler (v4.14.0). Visualizations were generated using ggplot2 (v4.0.0) and enrichplot (v1.26.1).
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Acknowledgements
We are grateful to all contributors to this study and acknowledge the funding sources that provided financial support. This work was supported by the National Natural Science Foundation of China (62372144, 62573169, 62572155) and Outstanding Youth Foundation of Heilongjiang Province of China (YQ2023F004).
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H.Y.Z.: Study conception and design, methodological design, data processing and analysis, manuscript drafting and revision. P.Q.B: Data processing and analysis, figure and table visualization, manuscript drafting and revision. Z.M.L.: Data collection and collation. M.R.G.: Data processing. L.L.: Data collection and collation. D.C.H.: Data processing. J.C.G.: Study conception and design. L.W.: Study conception and design, methodological design, resource provision and support, supervision and guidance.All authors read, reviewed, and approved the final manuscript.
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Zhao, H., Ben, P., Liu, Z. et al. An in-depth transcriptomic atlas deciphering traditional Chinese medicine mechanisms and disease associations. Sci Data (2026). https://doi.org/10.1038/s41597-026-06988-9
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DOI: https://doi.org/10.1038/s41597-026-06988-9
