Evidence map›Paper›PMID 38840248›Full record

ArticleBMC complementary medicine and therapies2024

Mechanistic prediction and validation of Brevilin A Therapeutic effects in Lung Cancer.

Ruixue Wang, Cuiyun Gao, Meng Yu, Jialing Song, Zhenzhen Feng, Ruyu Wang, Huafeng Pan, Haimeng Liu, Wei Li, Xiangzhen Fan

Abstract read
In one paragraph

Article in BMC complementary medicine and therapies, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Molecules (Basel, Switzerland) · 2025
    Review
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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

10 authors.

Ruixue Wang *Shandong University of Traditional Chinese Medicine, Jinan, Shandong, China.
Cuiyun Gao *Department of Rehabilitation Medicine, Binzhou Medical University Hospital, Binzhou, Shandong, China.
Meng YuShandong University of Traditional Chinese Medicine, Jinan, Shandong, China.
Jialing SongDepartment of Rehabilitation Medicine, Binzhou Medical University Hospital, Binzhou, Shandong, China.
Zhenzhen FengDepartment of Rehabilitation Medicine, Binzhou Medical University Hospital, Binzhou, Shandong, China.
Ruyu WangSchool of clinical medicine, Jiangxi University of Chinese Medicine, Nanchang, Jiangxi, China.
Huafeng PanScience and Technology Innovation Center, Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, China.
Haimeng LiuDepartment of Rehabilitation Medicine, Binzhou Medical University Hospital, Binzhou, Shandong, China. haimeng1215@126.com.
Wei LiShandong University of Traditional Chinese Medicine, Jinan, Shandong, China. yishengliwei@163.com.
Xiangzhen FanShandong University of Traditional Chinese Medicine, Jinan, Shandong, China. 643355196@qq.com.

Funding

China Postdoctoral Science Foundation 2023M732138Department of Education of Shandong Province 2022KJ091Natural Science Foundation of Shandong Province ZR2022QH001Science and Technology Project of Binzhou Medical University BY2021KYQD33
6 · The paper itself

Abstract

backgroundTraditional Chinese medicine (TCM) has been found widespread application in neoplasm treatment, yielding promising therapeutic candidates. Previous studies have revealed the anti-cancer properties of Brevilin A, a naturally occurring sesquiterpene lactone derived from Centipeda minima (L.) A.Br. (C. minima), a TCM herb, specifically against lung cancer. However, the underlying mechanisms of its effects remain elusive. This study employs network pharmacology and experimental analyses to unravel the molecular mechanisms of Brevilin A in lung cancer.

methodsThe Batman-TCM, Swiss Target Prediction, Pharmmapper, SuperPred, and BindingDB databases were screened to identify Brevilin A targets. Lung cancer-related targets were sourced from GEO, Genecards, OMIM, TTD, and Drugbank databases. Utilizing Cytoscape software, a protein-protein interaction (PPI) network was established. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), Gene set enrichment analysis (GSEA), and gene-pathway correlation analysis were conducted using R software. To validate network pharmacology results, molecular docking, molecular dynamics simulations, and in vitro experiments were performed.

resultsWe identified 599 Brevilin A-associated targets and 3864 lung cancer-related targets, with 155 overlapping genes considered as candidate targets for Brevilin A against lung cancer. The PPI network highlighted STAT3, TNF, HIF1A, PTEN, ESR1, and MTOR as potential therapeutic targets. GO and KEGG analyses revealed 2893 enriched GO terms and 157 enriched KEGG pathways, including the PI3K-Akt signaling pathway, FoxO signaling pathway, and HIF-1 signaling pathway. GSEA demonstrated a close association between hub genes and lung cancer. Gene-pathway correlation analysis indicated significant associations between hub genes and the cellular response to hypoxia pathway. Molecular docking and dynamics simulations confirmed Brevilin A's interaction with PTEN and HIF1A, respectively. In vitro experiments demonstrated Brevilin A-induced dose- and time-dependent cell death in A549 cells. Notably, Brevilin A treatment significantly reduced HIF-1α mRNA expression while increasing PTEN mRNA levels.

conclusionsThis study demonstrates that Brevilin A exerts anti-cancer effects in treating lung cancer through a multi-target and multi-pathway manner, with the HIF pathway potentially being involved. These results lay a theoretical foundation for the prospective clinical application of Brevilin A.

Indexed as

Lung NeoplasmsMolecular Docking SimulationSesquiterpenesA549 CellsCrotonatesHumansLactonesNetwork PharmacologyProtein Interaction Mapsbrevilin ACrotonatesLactonesSesquiterpenesBrevilin AExperimental validationLung cancerMolecular dockingMolecular mechanismNetwork pharmacology

Identifiers

PMID38840248
PMCPMC11151568

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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.