Evidence map›Paper›PMID 32964029›Full record

ArticleBioMed research international2020

Systematic Elucidation of the Mechanism of Quercetin against Gastric Cancer via Network Pharmacology Approach.

Liangjun Yang, Zhipeng Hu, Jiajie Zhu, Qiting Liang, Hengli Zhou, Jiali Li, Xiangzhen Fan, Ziming Zhao, Huafeng Pan, Baoying Fei

Open access · hybridAbstract read
In one paragraph

Article in BioMed research international, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
20citing papers in PubMed, 1 pooled it
3.2field-weighted citation impact, top 7% 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

20 citing papers in PubMed, 1 synthesis or guideline pooled it, 32 citations in OpenAlex.

  1. Pooled it
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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 at 2 institutions in 1 country.

Liangjun YangDepartment of Gastroenterology, Tongde Hospital of Zhejiang Province, Hangzhou 310012, China.ORCID https://orcid.org/0000-0002-0343-7828
Zhipeng HuHospital of Chengdu University of Traditional Chinese Medicine, Chengdu 610075, China.ORCID https://orcid.org/0000-0003-1524-6452
Jiajie ZhuDepartment of Gastroenterology, Tongde Hospital of Zhejiang Province, Hangzhou 310012, China.ORCID https://orcid.org/0000-0002-0900-3502
Qiting LiangScience and Technology Innovation Center, Guangzhou University of Chinese Medicine, Guangzhou 510405, China.ORCID https://orcid.org/0000-0003-2196-0126
Hengli ZhouScience and Technology Innovation Center, Guangzhou University of Chinese Medicine, Guangzhou 510405, China.ORCID https://orcid.org/0000-0002-6955-8294
Jiali LiScience and Technology Innovation Center, Guangzhou University of Chinese Medicine, Guangzhou 510405, China.ORCID https://orcid.org/0000-0001-8734-0494
Xiangzhen FanScience and Technology Innovation Center, Guangzhou University of Chinese Medicine, Guangzhou 510405, China.ORCID https://orcid.org/0000-0001-9631-3033
Ziming ZhaoGuangdong Province Engineering Technology Research Institute of T.C.M., Guangzhou 510095, China.ORCID https://orcid.org/0000-0002-4710-1971
Huafeng PanScience and Technology Innovation Center, Guangzhou University of Chinese Medicine, Guangzhou 510405, China.ORCID https://orcid.org/0000-0003-0369-5963
Baoying FeiDepartment of Gastroenterology, Tongde Hospital of Zhejiang Province, Hangzhou 310012, China.ORCID https://orcid.org/0000-0002-8438-881X
Guangzhou University of Chinese Medicine · CNTongde Hospital of Zhejiang Province · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This study was aimed at elucidating the potential mechanisms of quercetin in the treatment of gastric cancer (GC). A network pharmacology approach was used to analyze the targets and pathways of quercetin in treating GC. The predicted targets of quercetin against GC were obtained through database mining, and the correlation of these targets with GC was analyzed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses. Next, the protein-protein interaction (PPI) network was constructed, and overall survival (OS) analysis of hub targets was performed using the Kaplan-Meier Plotter online tool. Finally, the mechanism was further analyzed via molecular docking of quercetin with the hub targets. Thirty-six quercetin-related genes were identified, 15 of which overlapped with GC-related targets. These targets were further mapped to 319 GO biological process terms and 10 remarkable pathways. In the PPI network analysis, six hub targets were identified, including AKT1, EGFR, SRC, IGF1R, PTK2, and KDR. The high expression of these targets was related to poor OS in GC patients. Molecular docking analysis confirmed that quercetin can bind to these hub targets. In conclusion,

Indexed as

Gene Expression ProfilingGene Expression Regulation, NeoplasticGene OntologyHumansMolecular Docking SimulationProtein Interaction MapsProteinsQuercetinStomach NeoplasmsSurvival AnalysisProteinsQuercetin

Identifiers

PMID32964029
PMCPMC7486643
OpenAlexW3081619736

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.