Evidence map›Paper›PMID 40417000›Full record

ReviewOpen life sciences2025

Modulating the tumor microenvironment: The role of traditional Chinese medicine in improving lung cancer treatment.

Geling Teng, Min Zhang, Yuling Pan, Sajad Karampoor, Rasoul Mirzaei

Abstract readReview
In one paragraph

Review in Open life sciences, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. Review
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

5 authors.

Geling TengDepartment of Respiratory and Critical Care Medicine, Shandong Public Health Clinical Center, Shandong University, Jinan, 250013, China.
Min ZhangDepartment of Respiratory and Critical Care Medicine, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, 250021, China.
Yuling PanSchool of Medicine, Shandong University of Traditional Chinese Medicine, Jinan, 250355, China.
Sajad KarampoorGastrointestinal and Liver Diseases Research Center, Iran University of Medical Sciences, Tehran, Iran.
Rasoul MirzaeiNovin-sin Biotechnology Inc., Tehran, Iran.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The holistic approach of traditional Chinese medicine (TCM) has been increasingly being focused on as a potential adjuvant to conventional lung cancer therapies in an attempt at modulating the tumor microenvironment (TME). Covering a diverse range of herbal medicine, acupuncture, and dietary therapy, TCM brings a unique perspective to influencing the TME. Importantly, the study has found the effects of specific TCM compounds, such as cantharidin, boehmenan, shikonin, and salidroside, on lung cancer in the TME. These compounds interact intricately with key apoptotic regulators, oxidative stress pathways, and inflammation-related mechanisms, suggesting their potential role in enhancing conventional therapies. TCM compounds could modulate a variety of cellular and molecular pathways, potentially inhibiting tumor proliferation, invasion, and metastasis. Besides, the practices of TCM alleviate the side effects of conventional treatments and enhance immune function, hence promoting the quality of life among lung cancer patients. In this regard, this review gives a contemporary account of the state of affairs on the part of TCM within the framework of the treatment of lung cancer with reference to its recent developments, and diverse roles.

Indexed as

apoptosisimmunomodulationlung cancermetastasis: Side effectTCMTME

Identifiers

PMID40417000
PMCPMC12103189

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.