Li-Na Yang1, Zhu-Lin Wu1, Zhen-Jiang Yang1, Shi-Guang Li1, Chen-Sheng Ouyang2. 1. Department of Oncology and Haematology, The Fourth Clinical Medical College of Guangzhou University of Chinese Medicine, Shenzhen, Guangdong Province, 518033, China. 2. Department of Oncology and Haematology, The Fourth Clinical Medical College of Guangzhou University of Chinese Medicine, Shenzhen, Guangdong Province, 518033, China. chensheng_ouyang@126.com.
Abstract
OBJECTIVE: To screen the key Chinese Herbal Medicines (KCHMs) against breast cancer by data mining, and analyze the potential mechanism of KCHMs using network pharmacology method. METHODS: Clinical prescriptions consisted of CHMs for treating breast cancer were screened, and then Traditional Chinese Medicine Inheritance Support System (TCMISS) was applied to obtain the KCHMs. Subsequently, active ingredients and corresponding target genes of KCHMs were searched by Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP) database, and target genes of breast cancer were collected using OMIM and MalaCards. After that, the overlapping target genes of KCHMs and breast cancer were screened, and the protein-protein interaction (PPI) network was built. In addition, a network of "KCHMs-active ingredients-breast cancer-targets" was constructed by Cytoscape 3.7.1. Finally, Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) analysis were performed with Database for Annotation, Visualization and Integrated Discovery (DAVID) database to reveal the action mechanism of KCHMs. RESULTS: A total of 7 KCHMs were identified, whose active ingredients include quercetin, luteolin, nobiletin, kaempferol, isorhamnetin, naringenin, and be-ta-sitosterol, etc. Based on protein-protein interaction analysis, core targets were ESR1, MYC, CCND1, EGFR, CASP3, ERBB2, etc. Several KEGG pathways (e.g, PI3K-Akt, p53, ErbB, and HIF-1 signaling pathways) were found. CONCLUSION: Based on the combination of the data mining method and network pharmacology approach, the therapeutic effect of KCHMs on breast cancer may be realized by acting on target genes and signaling pathways related to the formation and progression of breast cancer.
OBJECTIVE: To screen the key Chinese Herbal Medicines (KCHMs) against breast cancer by data mining, and analyze the potential mechanism of KCHMs using network pharmacology method. METHODS: Clinical prescriptions consisted of CHMs for treating breast cancer were screened, and then Traditional Chinese Medicine Inheritance Support System (TCMISS) was applied to obtain the KCHMs. Subsequently, active ingredients and corresponding target genes of KCHMs were searched by Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP) database, and target genes of breast cancer were collected using OMIM and MalaCards. After that, the overlapping target genes of KCHMs and breast cancer were screened, and the protein-protein interaction (PPI) network was built. In addition, a network of "KCHMs-active ingredients-breast cancer-targets" was constructed by Cytoscape 3.7.1. Finally, Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) analysis were performed with Database for Annotation, Visualization and Integrated Discovery (DAVID) database to reveal the action mechanism of KCHMs. RESULTS: A total of 7 KCHMs were identified, whose active ingredients include quercetin, luteolin, nobiletin, kaempferol, isorhamnetin, naringenin, and be-ta-sitosterol, etc. Based on protein-protein interaction analysis, core targets were ESR1, MYC, CCND1, EGFR, CASP3, ERBB2, etc. Several KEGG pathways (e.g, PI3K-Akt, p53, ErbB, and HIF-1 signaling pathways) were found. CONCLUSION: Based on the combination of the data mining method and network pharmacology approach, the therapeutic effect of KCHMs on breast cancer may be realized by acting on target genes and signaling pathways related to the formation and progression of breast cancer.
Authors: Damian Szklarczyk; Andrea Franceschini; Stefan Wyder; Kristoffer Forslund; Davide Heller; Jaime Huerta-Cepas; Milan Simonovic; Alexander Roth; Alberto Santos; Kalliopi P Tsafou; Michael Kuhn; Peer Bork; Lars J Jensen; Christian von Mering Journal: Nucleic Acids Res Date: 2014-10-28 Impact factor: 16.971