Integrated bioinformatics analysis to decipher molecular mechanism of compound Kushen injection for esophageal cancer by combining WGCNA with network pharmacology
Copyright policy )Integrated bioinformatics analysis to decipher molecular mechanism of compound Kushen injection for esophageal cancer by combining WGCNA with network pharmacology
AbstractCompound 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.
- Beijing University of Chinese Medicine China (People's Republic of)
Models, Molecular, Esophageal Neoplasms, Receptor, ErbB-2, Sequence Analysis, RNA, Gene Expression Profiling, Computational Biology, Antineoplastic Agents, Kaplan-Meier Estimate, Article, Receptor, IGF Type 1, ErbB Receptors, Gene Expression Regulation, Neoplastic, Molecular Docking Simulation, Databases, Genetic, Humans, Cyclin D1, Gene Regulatory Networks, Algorithms, Drugs, Chinese Herbal
Models, Molecular, Esophageal Neoplasms, Receptor, ErbB-2, Sequence Analysis, RNA, Gene Expression Profiling, Computational Biology, Antineoplastic Agents, Kaplan-Meier Estimate, Article, Receptor, IGF Type 1, ErbB Receptors, Gene Expression Regulation, Neoplastic, Molecular Docking Simulation, Databases, Genetic, Humans, Cyclin D1, Gene Regulatory Networks, Algorithms, Drugs, Chinese Herbal
citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).27 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Top 10% influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Average impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Top 10%
Citations provided by BIP!Popularity provided by BIP!