Evidence map›Paper›PMID 35590327›Full record

ArticleWorld journal of surgical oncology2022

Compound Kushen injection inhibits EMT of gastric cancer cells via the PI3K/AKT pathway.

Luo Li, Keshan Wang, Zhenguo Liu, Yajuan Lü, Congcong Wang, Xuefei Yi, Jianping Guo

Open access · goldAbstract read
In one paragraph

Article in World journal of surgical oncology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
1.0field-weighted citation impact, top 25% 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

3 citing papers in PubMed, 13 citations in OpenAlex.

  1. Review
  2. Article
  3. Network pharmacology study andWorld journal of gastrointestinal oncology · 2024
    Article
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 at 3 institutions in 1 country.

Luo LiQuality Management Office, Zibo Central Hospital, Zibo, 255000, Shandong, China.
Keshan WangDepartment of Intervention, The Fourth People's Hospital of ZiBo City, Zibo, 255000, Shandong, China.
Zhenguo LiuDepartment of Oncology, The People's Hospital of Gaoqing District, Zibo, 256300, Shandong, China.
Yajuan LüDepartment of Radiotherapy, Shandong Qianfoshan Hospital, Jinan, 250014, Shandong, China.
Congcong WangDepartment of Oncology, Maternal and Child Health Care Hospital of Zibo, Zibo, 255029, Shandong, China.
Xuefei YiDepartment of Oncology, Maternal and Child Health Care Hospital of Zibo, Zibo, 255029, Shandong, China.
Jianping GuoDepartment of Oncology, Maternal and Child Health Care Hospital of Zibo, Zibo, 255029, Shandong, China. gjpboom@163.com.
Central Hospital of Zibo · CNShandong Provincial QianFoShan Hospital · CNThe Fourth People's Hospital of Zibo City · CN

Funding

Key R&D Program of Zibo City No.2018kj010100Shandong Province Medicine and Health Science and Technology Development Plan No.202003030471 and No.202009031334
6 · The paper itself

Abstract

backgroundThe effective components contained in compound Kushen injection (CKI) and the genes and signalling pathways related to gastric cancer (GC) were analyzed through the network pharmacology method of traditional Chinese medicine, and various possible mechanisms by which CKI affects the proliferation, differentiation, survival, and metastasis of GC cells were discussed. The PI3K/AKT signalling pathway is considered to be one of the most important pathways targeted by CKI in the regulation of GC cells. The implementation of related cell experiments also confirmed the information we revealed.

methodsEffective drug components of Kushen and Baituling in CKI were identified from the Traditional Chinese Medicine Systems Pharmacology Database (TCMSP). Genes related to GC were identified using the GENECARD and OMIM databases. The common target genes related to the effective components of the drug and GC were identified using the intersection method and visualized using software. A protein-protein interaction network (PPI) was established using STRING online software to confirm the key genes. Gene Ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed to predict the key pathways of CKI in GC treatment. BGC-803 and MKN-28 GC cells were used to verify the signalling pathway. Cell proliferation, apoptosis, migration ability, and invasion ability were assessed using CCK8, flow cytometry, scratch, and transwell assays. Immunofluorescence assays and western blotting were used to detect the expression of related proteins.

resultsCKI regulated GC cells through 35 effective drug components of GC-related target genes. In total, 194 genes were common targets of CKI and GC. The most significant function of the enriched genes was DNA-binding transcription activator activity as demonstrated by GO enrichment analysis. The metabolic pathway with the highest enrichment was the PI3K/AKT signalling pathway as demonstrated by KEGG enrichment analysis. Our cell experimental evidence also shows that CKI inhibits GC cell growth and migration and induce GC cell apoptosis. In addition, CKI inhibits the EMT process in GC cells through the PI3K/AKT signalling pathway.

conclusionAKT1 is a key gene for CKI treatment of GC. CKI inhibited GC cell growth and migration and induced GC cell apoptosis. In addition, CKI regulated the EMT process in GC cells through the PI3K/AKT signalling pathway.

Indexed as

Antineoplastic AgentsDrugs, Chinese HerbalStomach NeoplasmsHumansPhosphatidylinositol 3-KinasesProto-Oncogene Proteins c-aktAntineoplastic AgentsDrugs, Chinese HerbalkushenPhosphatidylinositol 3-KinasesProto-Oncogene Proteins c-aktCKIEMTGastric cancerPI3K/AKT

Identifiers

PMID35590327
PMCPMC9121642
OpenAlexW4280609976

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.