Evidence map›Paper›PMID 42112118›Full record

ArticleJournal of ginseng research2026

Optimizing Component Formula Extracted from Ginseng and Salvia Inhibits Lung Cancer Metastasis and EMT via Suppressing AKT/GSK-3β/β-catenin Pathway.

Yanxia Ma, Ze Chen, Tangxiaoxiao He, Qinwei Lu, Zhiwei Pan, Weiping Chen, Lei Bi

Abstract read
In one paragraph

Article in Journal of ginseng research, 2026. 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
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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

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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

7 authors.

Yanxia MaSchool of Chinese Medicine, Nanjing University of Chinese Medicine, Nanjing, 210023, China.
Ze ChenSchool of Chinese Medicine, Nanjing University of Chinese Medicine, Nanjing, 210023, China.
Tangxiaoxiao HeSchool of Chinese Medicine, Nanjing University of Chinese Medicine, Nanjing, 210023, China.
Qinwei LuSchool of Chinese Medicine, Nanjing University of Chinese Medicine, Nanjing, 210023, China.
Zhiwei PanSchool of Chinese Medicine, Nanjing University of Chinese Medicine, Nanjing, 210023, China.
Weiping ChenFaculty of Chinese Medicine, Macau University of Science and Technology, Avenida Wai Long, Macau, Taipa, 999078, China.
Lei BiSchool of Chinese Medicine, Nanjing University of Chinese Medicine, Nanjing, 210023, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: EMT plays a pivotal role in the metastatic progression of lung cancer. Ginseng and salvia are widely used combinations in Chinese medicine for cancer therapy, but their role in regulating cancer metastasis remains unknown. Purpose: This investigation seeks to examine the efficacy and potential mechanism of optimizing component formula (OCF) extracted from ginseng and salvia on metastasis-associated EMT reprogramming in lung cancer. Methods: The inhibitory effects of OCF on tumor growth and metastatic cascades were systematically characterized Results: OCF markedly suppressed the proliferative capacity, migratory and invasive behaviors in H1299 and 95D cells. Signal Transduction PathwayFinder PCRArray and qRT-PCR analysises confirmed that suppressing EMT was a potential way of OCF to inhibit metastasis in lung cancer. Mechanistically, OCF exhibited a good affinity with AKT resulting in inhibition of the AKT/GSK-3β/β-catenin pathway. Co-treatment of OCF with SC79 or MK-2206 demonstrated that OCF weakened EMT progress via repressing AKT/GSK3β/β-catenin pathway in lung cancer. Furthermore, OCF treatment inhibited lung cancer growth and metastasis Conclusion: OCF weakened EMT progression in lung cancer by suppressing AKT/GSK-3β/β-catenin pathway, which positions OCF as a therapeutic candidate for lung cancer.

Indexed as

AKT/GSK-3β/β-catenin pathwayEpithelial to mesenchymal transition (EMT)GinsengLung cancerMetastasisOptimizing component formula (OCF)Salvia

Identifiers

PMID42112118
PMCPMC13149888

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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.