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<p>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.</p>

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An in-depth transcriptomic atlas deciphering traditional Chinese medicine mechanisms and disease associations

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年3月5日

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  • Hongying Zhao,
  • Peiqi Ben,
  • Zhimiao Liu,
  • Marui Guan,
  • Lin Lin,
  • Dongchen Han,
  • Jincheng Guo,
  • Li Wang

摘要

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.