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Differential Gene Analysis of Trastuzumab in Breast Cancer Based on Network Pharmacology and Medical Images
The purpose of this study was to use network pharmacology, biomedical images and molecular docking technology in the treatment of breast cancer to investigate the feasible therapeutic targets and mechanisms of trastuzumab. In the first place, we applied pubchem swisstarget (http://www.swisstargetpre...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9304584/ https://www.ncbi.nlm.nih.gov/pubmed/35874525 http://dx.doi.org/10.3389/fphys.2022.942049 |
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author | Lu, Yuan Bi, Juan Li, Fei Wang, Gang Zhu, Junjie Jin, Jiqing Liu, Yueyun |
author_facet | Lu, Yuan Bi, Juan Li, Fei Wang, Gang Zhu, Junjie Jin, Jiqing Liu, Yueyun |
author_sort | Lu, Yuan |
collection | PubMed |
description | The purpose of this study was to use network pharmacology, biomedical images and molecular docking technology in the treatment of breast cancer to investigate the feasible therapeutic targets and mechanisms of trastuzumab. In the first place, we applied pubchem swisstarget (http://www.swisstargetprediction.ch/), (https://pubchem.ncbi.nlm.nih.gov/) pharmmapper (http://lilab-ecust.cn/pharmmapper/), and the batman-tcm (http://bionet.ncpsb.org.cn/batman-tcm/) database to collect the trastuzumab targets. Then, in NCBI-GEO, breast cancer target genes were chosen (https://www.ncbi.nlm.nih.gov/geo/). The intersection regions of drug and disease target genes were used to draw a Venn diagram. Through Cytoscape 3.7.2 software, and the STRING database, we then formed a protein-protein interaction (PPI) network. Besides, we concluded KEGG pathway analysis and Geen Ontology analysis by using ClueGO in Cytospace. Finally, the top 5 target proteins in the PPI network to dock with trastuzumab were selected. After screening trastuzumab and breast cancer in databases separately, we got 521 target genes of the drug and 1,464 target genes of breast cancer. The number of overlapping genes was 54. PPI network core genes include GAPDH, MMP9, CCNA2, RRM2, CHEK1, etc. GO analysis indicated that trastuzumab treats breast cancer through abundant biological processes, especially positive regulation of phospholipase activity, linoleic acid metabolic process, and negative regulation of endothelial cell proliferation. The molecular function is NADP binding and the cellular component is tertiary granule lumen. The results of KEGG enrichment analysis exhibited four pathways related to the formation and cure of breast cancer, containing Drug metabolism, Glutathione metabolism, Pyrimidine metabolism and PPAR signaling pathway. Molecular docking showed that trastuzumab has good binding abilities with five core target proteins (GAPDH, MMP9, CCNA2, RRM2, CHEK1). This study, through network pharmacology and molecular docking, provides new pieces of evidence and ideas to understand how trastuzumab treats breast cancer at the gene level. |
format | Online Article Text |
id | pubmed-9304584 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-93045842022-07-23 Differential Gene Analysis of Trastuzumab in Breast Cancer Based on Network Pharmacology and Medical Images Lu, Yuan Bi, Juan Li, Fei Wang, Gang Zhu, Junjie Jin, Jiqing Liu, Yueyun Front Physiol Physiology The purpose of this study was to use network pharmacology, biomedical images and molecular docking technology in the treatment of breast cancer to investigate the feasible therapeutic targets and mechanisms of trastuzumab. In the first place, we applied pubchem swisstarget (http://www.swisstargetprediction.ch/), (https://pubchem.ncbi.nlm.nih.gov/) pharmmapper (http://lilab-ecust.cn/pharmmapper/), and the batman-tcm (http://bionet.ncpsb.org.cn/batman-tcm/) database to collect the trastuzumab targets. Then, in NCBI-GEO, breast cancer target genes were chosen (https://www.ncbi.nlm.nih.gov/geo/). The intersection regions of drug and disease target genes were used to draw a Venn diagram. Through Cytoscape 3.7.2 software, and the STRING database, we then formed a protein-protein interaction (PPI) network. Besides, we concluded KEGG pathway analysis and Geen Ontology analysis by using ClueGO in Cytospace. Finally, the top 5 target proteins in the PPI network to dock with trastuzumab were selected. After screening trastuzumab and breast cancer in databases separately, we got 521 target genes of the drug and 1,464 target genes of breast cancer. The number of overlapping genes was 54. PPI network core genes include GAPDH, MMP9, CCNA2, RRM2, CHEK1, etc. GO analysis indicated that trastuzumab treats breast cancer through abundant biological processes, especially positive regulation of phospholipase activity, linoleic acid metabolic process, and negative regulation of endothelial cell proliferation. The molecular function is NADP binding and the cellular component is tertiary granule lumen. The results of KEGG enrichment analysis exhibited four pathways related to the formation and cure of breast cancer, containing Drug metabolism, Glutathione metabolism, Pyrimidine metabolism and PPAR signaling pathway. Molecular docking showed that trastuzumab has good binding abilities with five core target proteins (GAPDH, MMP9, CCNA2, RRM2, CHEK1). This study, through network pharmacology and molecular docking, provides new pieces of evidence and ideas to understand how trastuzumab treats breast cancer at the gene level. Frontiers Media S.A. 2022-07-08 /pmc/articles/PMC9304584/ /pubmed/35874525 http://dx.doi.org/10.3389/fphys.2022.942049 Text en Copyright © 2022 Lu, Bi, Li, Wang, Zhu, Jin and Liu. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Physiology Lu, Yuan Bi, Juan Li, Fei Wang, Gang Zhu, Junjie Jin, Jiqing Liu, Yueyun Differential Gene Analysis of Trastuzumab in Breast Cancer Based on Network Pharmacology and Medical Images |
title | Differential Gene Analysis of Trastuzumab in Breast Cancer Based on Network Pharmacology and Medical Images |
title_full | Differential Gene Analysis of Trastuzumab in Breast Cancer Based on Network Pharmacology and Medical Images |
title_fullStr | Differential Gene Analysis of Trastuzumab in Breast Cancer Based on Network Pharmacology and Medical Images |
title_full_unstemmed | Differential Gene Analysis of Trastuzumab in Breast Cancer Based on Network Pharmacology and Medical Images |
title_short | Differential Gene Analysis of Trastuzumab in Breast Cancer Based on Network Pharmacology and Medical Images |
title_sort | differential gene analysis of trastuzumab in breast cancer based on network pharmacology and medical images |
topic | Physiology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9304584/ https://www.ncbi.nlm.nih.gov/pubmed/35874525 http://dx.doi.org/10.3389/fphys.2022.942049 |
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