Evidence map›Paper›PMID 37610675›Full record

ArticleJournal of cancer research and clinical oncology2023

Using network pharmacological analysis and molecular docking to investigate the mechanism of action of quercetin's suppression of oral cancer.

Hui Dong, Mengli Li, Hong Chen, Linqing Tian, Wei Wei, Shibing Wang, Gang Cheng, Shuyan Liu

Open access · greenAbstract read
In one paragraph

Article in Journal of cancer research and clinical oncology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
2.2field-weighted citation impact, top 10% of its field
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

5 citing papers in PubMed, 11 citations in OpenAlex.

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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

8 authors at 4 institutions in 1 country.

Hui Dong *Department of Stomatology, Punan Hospital of Pudong New District, Shanghai, China.
Mengli Li *Department of Stomatology, Bengbu Medical College, 2600 Donghai Avenue, Bengbu, 233030, China.
Hong ChenCenter for Plastic and Reconstructive Surgery, Cancer Center, Department of Stomatology, Zhejiang Provincial People's Hospital (Affiliated People's Hospital, Hangzhou Medical College), Hangzhou, 310014, Zhejiang, China.
Linqing TianGraduate School, Bengbu Medical College, 2600 Donghai Avenue, Bengbu, 233030, China.
Wei WeiPostgraduate Training Base of Jinzhou Medical University (Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College), Hangzhou, 310014, Zhejiang, People's Republic of China.
Shibing WangCancer Center, Molecular Diagnosis Laboratory, Key Laboratory of Tumor Molecular Diagnosis and Individualized Medicine of Zhejiang Province, Zhejiang Provincial People's Hospital (Affiliated People's Hospital, Hangzhou Medical College), Hangzhou, 310014, Zhejiang, China.
Gang ChengDepartment of Stomatology, Bengbu Medical College, 2600 Donghai Avenue, Bengbu, 233030, China. gang_cheng001@hotmail.com.
Shuyan LiuCenter for Plastic and Reconstructive Surgery, Cancer Center, Department of Stomatology, Zhejiang Provincial People's Hospital (Affiliated People's Hospital, Hangzhou Medical College), Hangzhou, 310014, Zhejiang, China. liushuyan@hmc.edu.cn.
Hangzhou Medical College · CNBengbu Medical College · CNPunan Hospital · CNZhejiang Provincial People's Hospital · CN

Funding

Science Technology Department of Zhejiang Province LGD22H140003Zhejiang Provincial Medical Technology Plan Project 2022KY505Zhejiang Traditional Chinese Medicine Science and Technology Program No. 2021ZA007
6 · The paper itself

Abstract

objectiveThis investigation seeks to explore the mechanism of quercetin in oral cancer by incorporating network pharmacology analysis and molecular docking.

methodsFirst, we use the network pharmacology analysis to discover possible core targets for quercetin and oral cancer. We subsequently utilized the docking of molecules techniques to calculate the affinities of critical targets and quercetin for verification.

resultsTCMSP and the Swiss Target Prediction database found 190 quercetin action targets, while GeneCards, OMIM, PharmGkb, and the Therapeutic Target Database found 8971 oral cancer-related targets. Venny 2.1.0 online software conducted an intersection analysis of quercetin-related target information with information about oral cancer, and 172 putative quercetin-anti-oral cancer targets were examined. Six prospective core targets for quercetin treatment of oral cancer were identified from the PPI network topology analysis of 172 putative therapeutic targets. These targets include AKT1, PIK3R1, MYC, HIF1A, SRC, and HSP90AA1. GO enrichment function analysis showed that 2372 biological processes, 98 cell components, and 201 molecular functions were involved. Through enrichment analysis of the KEGG pathway, 172 signal pathways were obtained. A few examples are PI3K-AKT, HIF-1, IL-17, and other signaling pathways. The molecular docking scores of quercetin and the primary therapeutic targets AKT1, HIF1A, HSP90AA1, MYC, PIK3R1, and SRC are all less than -0.7 points, demonstrating good compatibility between the medicine and small molecules and suggesting that quercetin may affect oral cancer through the primary target.

conclusionThis study explores quercetin's mechanism and possible targets for oral cancer treatment, offering novel approaches. Quercetin may be a multitarget medication against oral cancer in the future.

Indexed as

Drugs, Chinese HerbalMouth NeoplasmsHumansMolecular Docking SimulationPhosphatidylinositol 3-KinasesProspective StudiesQuercetinDrugs, Chinese HerbalPhosphatidylinositol 3-KinasesQuercetinMechanism of actionMolecular dockingNetwork pharmacologyOral cancerQuercetin

Identifiers

PMID37610675
PMCPMC11797625
OpenAlexW4386084283

What OpenQuestion holds

Textmetadata
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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.