Evidence map›Paper›PMID 41079717›Full record

ReviewFrontiers in pharmacology2025

Preclinical research and potential clinical application of traditional Chinese medicine in cancer treatment: take Astragalus and Dioscorea opposita as an example.

Huizhu Jia, Yifan Qi, Hui Wang, Fengjiao Zhang, Jiarui Wu, Zhiqiang Zhang

Abstract readReview
In one paragraph

Review in Frontiers in pharmacology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Huizhu JiaHenan University of Chinese Medicine, Zhengzhou, China.
Yifan QiHenan University of Chinese Medicine, Zhengzhou, China.
Hui WangHenan University of Chinese Medicine, Zhengzhou, China.
Fengjiao ZhangHenan University of Animal Husbandry and Economy, Zhengzhou, China.
Jiarui WuSchool of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing, China.
Zhiqiang ZhangHenan University of Chinese Medicine, Zhengzhou, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Cancer is a complex and debilitating physical health disease that is important for social and individual health. Astragalus and Dioscorea opposita are two herbs that are traditionally used in folk medicine to treat various diseases, and they also play a role in immunizing and alleviating cancer. Purpose: This review aims to evaluate the anti-tumor potential of Astragalus and Dioscorea opposita by summarizing the active components and their known effects on tumor-related pathways, further illustrating the anti-tumor potential of traditional Chinese medicine. This study aims to explore the therapeutic effects of traditional Chinese medicines on different types of tumors. Methods: A comprehensive summary of the literature has been compiled, gathering information on the phytochemical characteristics, anti-tumor mechanisms, and the current status of both traditional Chinese and Western medicine relating to Astragalus and Dioscorea opposita. It demonstrates the previously established links between Astragalus and Dioscorea opposita and their anti-tumor properties. Results: The combined use of Astragalus and Dioscorea opposita enhances the immune regulatory effect through synergistic action, thereby improving the overall immune status of the body. Conclusion: This review provides a reference for experimental research in the treatment of tumors.

Indexed as

Astragalus membranaceuscancerDioscorea oppositaimmune microenvironmenttherapeutic pair

Identifiers

PMID41079717
PMCPMC12507749

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.