Evidence map›Paper›PMID 32728182›Full record

ArticleScientific reports2020

Integrated bioinformatics analysis to decipher molecular mechanism of compound Kushen injection for esophageal cancer by combining WGCNA with network pharmacology.

Wei Zhou, Jiarui Wu, Jingyuan Zhang, Xinkui Liu, Siyu Guo, ShanShan Jia, Xiaomeng Zhang, Yingli Zhu, Miaomiao Wang

Open access · goldAbstract read
In one paragraph

Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
22citing papers in PubMed, 2 pooled it
3.5field-weighted citation impact, top 6% 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

22 citing papers in PubMed, 2 syntheses or guidelines pooled it, 33 citations in OpenAlex.

  1. Pooled it
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  10. A pan-cancer analysis of pituitary tumor-transforming 3, pseudogene.American journal of translational research · 2023
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  14. Comprehensive Analysis of Tripterine Anti-Ovarian Cancer Effects Using Weighted Gene Co-Expression Network Analysis and Molecular Docking.Medical science monitor : international medical journal of experimental and clinical research · 2022
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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

9 authors at 1 institution in 1 country.

Wei ZhouBeijing University of Chinese Medicine, Beijing, 100102, China.
Jiarui WuBeijing University of Chinese Medicine, Beijing, 100102, China. exogamy@163.com.
Jingyuan ZhangBeijing University of Chinese Medicine, Beijing, 100102, China.
Xinkui LiuBeijing University of Chinese Medicine, Beijing, 100102, China.
Siyu GuoBeijing University of Chinese Medicine, Beijing, 100102, China.
ShanShan JiaBeijing University of Chinese Medicine, Beijing, 100102, China.
Xiaomeng ZhangBeijing University of Chinese Medicine, Beijing, 100102, China.
Yingli ZhuBeijing University of Chinese Medicine, Beijing, 100102, China.
Miaomiao WangBeijing University of Chinese Medicine, Beijing, 100102, China.
Beijing University of Chinese Medicine · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Compound Kushen injection (CKI), a medicine in widespread clinical use in China, has proven therapeutic effects on cancer. However, few molecular mechanism analyses have been carried out. To address this problem, bioinformatics approaches combining weighted gene co-expression network analysis with network pharmacology methods were undertaken to elucidate the underlying molecular mechanisms of CKI in the treatment of esophageal cancer (ESCA). First, the key gene modules related to the clinical traits of ESCA were analysed by WCGNA. Based on the results, the hub genes related to CKI treatment for ESCA were explored through network pharmacology. Molecular docking simulation was performed to recognize the binding activity of hub genes with CKI compounds. The results showed that the potential hub targets, including EGFR, ErbB2, CCND1 and IGF1R, are therapeutic targets of CKI for the treatment of ESCA. Moreover, these targets were significantly enriched in many pathways related to cancer and signalling pathways, such as the PI3K-Akt signalling pathway and ErbB signalling pathway. In conclusion, this research partially highlighted the molecular mechanism of CKI in the treatment of ESCA, offering great potential in the identification of the effective compounds in CKI and biomarkers for ESCA treatment.

Indexed as

AlgorithmsAntineoplastic AgentsComputational BiologyCyclin D1Databases, GeneticDrugs, Chinese HerbalErb-b2 Receptor Tyrosine KinasesErbB ReceptorsEsophageal NeoplasmsGene Expression ProfilingGene Expression Regulation, NeoplasticGene Regulatory NetworksHumansKaplan-Meier EstimateModels, MolecularMolecular Docking SimulationAntineoplastic AgentsCCND1 protein, humanCyclin D1Drugs, Chinese HerbalEGFR protein, humanERBB2 protein, humanErb-b2 Receptor Tyrosine KinasesErbB ReceptorsIGF1R protein, humankushenReceptor, IGF Type 1

Identifiers

PMID32728182
PMCPMC7391752
OpenAlexW3045857778

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.