ArticleEvidence-based complementary and alternative medicine : eCAM2020
A Bioinformatics Research on Novel Mechanism of Compound Kushen Injection for Treating Breast Cancer by Network Pharmacology and Molecular Docking Verification.
Article in Evidence-based complementary and alternative medicine : eCAM, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.
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The trial behind it
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Who cites it
9 citing papers in PubMed, 1 synthesis or guideline pooled it, 15 citations in OpenAlex.
- Meta-analysis of integrated ChIP-seq and transcriptome data revealed genomic regions affected by estrogen receptor alpha in breast cancer.BMC medical genomics · 2023Pooled it
- Bioinformatics and Omics-based Perspectives on Breast Cancer: Advancing Target Gene Identification for the Development of Anticancer Agents.Asian Pacific journal of cancer prevention : APJCP · 2025Review
- Uncovering the anti-breast cancer activity potential of east Kalimantan propolis by In vitro and bioinformatics analysis.Heliyon · 2024Article
- Current Molecular Combination Therapies Used for the Treatment of Breast Cancer.International journal of molecular sciences · 2022Review
- Cell cycle arrest is an important mechanism of action of compound Kushen injection in the prevention of colorectal cancer.Scientific reports · 2022Article
- Exploration of the Mechanism of Lianhua Qingwen in Treating Influenza Virus Pneumonia and New Coronavirus Pneumonia with the Concept of "Different Diseases with the Same Treatment" Based on Network Pharmacology.Evidence-based complementary and alternative medicine : eCAM · 2022Article
- Clinical Effectiveness and Safety of Chinese Herbal Medicine Compound Kushen Injection as an Add-On Treatment for Breast Cancer: A Systematic Review and Meta-Analysis.Evidence-based complementary and alternative medicine : eCAM · 2022Article
- A systematic review and meta-analysis on the efficacy of Compound Kushen Injection in 3 kinds of digestive tract tumor.Journal of gastrointestinal oncology · 2021Article
- A System Pharmacology Model for Decoding the Synergistic Mechanisms of Compound Kushen Injection in Treating Breast Cancer.Frontiers in pharmacology · 2021Article
Corrections and comments
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Authors and funding
7 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Compound Kushen injection (CKI) has been extensively used in treating breast cancer (BC). However, the molecular mechanism remains unclear. In this study, 16 active compounds of CKI were obtained from 3 articles for target prediction. Then, a compound-predicted target network and a compound-BC target network were conducted by Cytoscape 3.6.1. The gene ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were performed on the DAVID database. The binding energy between the key targets of CKI and the active compounds was studied by molecular docking. As a result, 16 active compounds of CKI were identified, corresponding to 285 putative targets. The key targets of CKI for BC are HSD11B1, DPP4, MMP9, CDK1, MMP2, PTGS2, and CA14. The function enrichment analysis obtained 13 GO entries and 6 KEGG pathways, including bladder cancer, cancer pathways, chemical carcinogenesis, estrogen signaling pathway, TNF signaling pathway, and leukocyte transendothelial migration. The result of molecular docking indicated that DPP4 had strong binding activity with matrine, alicyclic protein, and sophoridine, and MMP9 had strong binding activity with adenine and sophoridine. In conclusion, the therapeutic effect of CKI on BC is based on the overall pharmacological effect formed by the combined effects of multiple components, multiple targets, and multiple pathways. This study provides a theoretical basis for further experimental research in the future.
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Registered trials
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.