Evidence mapPaperPMID 39604504Full record

ArticleScientific reports2024

Single-cell multiomics reveals simvastatin inhibits pan-cancer epithelial-mesenchymal transition via the MEK/ERK pathway in XBP1+ mast cells.

Sen Lin, Huimin Zhang, Ruiqi Zhao, Zhulin Wu, Weiqing Zhang, Mengjiao Yu, Bei Zhang, Lanyue Ma, Danfei Li, Lisheng Peng and 1 more

Abstract read
In one paragraph

Article in Scientific reports, 2024. 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

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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

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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

11 authors.

Sen Lin *The Fourth Clinical Medical College, Guangzhou University of Chinese Medicine, Guangzhou, China.
Huimin Zhang *The First Clinical Medical College, Guangzhou University of Chinese Medicine, Guangzhou, China.
Ruiqi Zhao *The Fourth Clinical Medical College, Guangzhou University of Chinese Medicine, Guangzhou, China.
Zhulin WuDepartment of Traditional Chinese Medicine, People's Hospital of Longhua, Shenzhen, China.
Weiqing ZhangDepartment of Traditional Chinese Medicine, People's Hospital of Longhua, Shenzhen, China.
Mengjiao YuThe Fourth Clinical Medical College, Guangzhou University of Chinese Medicine, Guangzhou, China.
Bei ZhangThe Fourth Clinical Medical College, Guangzhou University of Chinese Medicine, Guangzhou, China.
Lanyue MaThe Fourth Clinical Medical College, Guangzhou University of Chinese Medicine, Guangzhou, China.
Danfei LiThe Fourth Clinical Medical College, Guangzhou University of Chinese Medicine, Guangzhou, China.
Lisheng PengDepartment of Hepatology, Shenzhen Traditional Chinese Medicine Hospital, Shenzhen, China. szpengls2023@163.com.
Weijun LuoDepartment of Traditional Chinese Medicine, People's Hospital of Longhua, Shenzhen, China. szluoweijun@163.com.

Funding

Clinical Special Funds of Fujian University of Traditional Chinese Medicine XB2023187Fujian Provincial Health Commission Science and Technology Plan Project 2022GGA031Shenzhen Science and Technology Innovation Commission General Program JCYJ20210324111207020Special Subject of Medical Research of Longhua District Medical Association 2023LHMA02
6 · The paper itself

Abstract

Distant metastasis is the leading cause of cancer-related mortality, and achieving survival benefits through advancements in systemic therapy remains challenging. Mast cells play a dual role in shaping the tumor microenvironment (TME) and influencing distant metastasis, underscoring the significant research value of targeting mast cells for systemic therapy in advanced cancer. We investigated variations in mast cell infiltration levels in primary and metastatic malignancies using immunocyte infiltration analysis. Mast cell subsets were identified from pan-cancer distant metastasis single-cell sequencing data through dimensionality reduction clustering and cell type annotation, combined with cell trajectory and communication network analyses. A prognostic model was established using WGCNA and 12 machine learning algorithms to identify potential mast cell targets. Drug sensitivity and Mendelian randomization analyses were conducted to select potential drugs targeting mast cells, and their effects on epithelial-mesenchymal transition (EMT) were validated through in vitro experiments, including wound healing, transwell, and western blot assays. Results revealed that activated mast cells show increased infiltration in metastatic tumors, correlating with poor survival duration. XBP1+ mast cells were identified as key components of the inhibitory TME, potentially involved in EMT activation. Simvastatin was identified as a potential drug, reversing EMT induced by XBP1+ mast cells in pan-cancer. Aberrant activation of MEK/ERK signaling in XBP1+ mast cells can stimulate cancer cell EMT by modulating degranulation, while Simvastatin can inhibit EMT by suppressing degranulation.

Indexed as

Epithelial-Mesenchymal TransitionMast CellsSimvastatinSingle-Cell AnalysisTumor MicroenvironmentX-Box Binding Protein 1Cell Line, TumorHumansMAP Kinase Signaling SystemMultiomicsNeoplasmsSimvastatinX-Box Binding Protein 1XBP1 protein, humanDistant metastasisHMG-CoA reductase inhibitorsMast cellsMulti-omicsPan-cancer

Identifiers

PMID39604504
PMCPMC11603196

What Socratic holds

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LicenceCC BY-NC-ND
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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 Socratic graph.