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Research
Miaomiao xi
TANK Medicinal Biology Institute of Xi'an
Corresponding Author
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This work is licensed under a CC BY 4.0 License. Read Full License
Background: Aidi injection (ADI)is a Chinese patent medicine with anti-cancer effect, which has been used to treat breast cancer (BC) in China for many years, but its potential pharmacological mechanism is still indeterminacy. In this resaearch, network pharmacology, a systematic and comprehensive approach, was used to reveal ADI's potential pharmacological mechanism in treating BC for the first time.
Methods: Databases were used to collect targets related to the bioactive components of ADI and BC. the relevant networks were established by the string database, and were screened potential bioactive components and core targets. Eventually, core targets and pathway enrichment were analyzed by DAVID database.
Results: As the results showed, we collected 99 bioactive ingredients,345 ADI-related targets after deduplication and 368 BC-related targets. Of these, 108 common targets were overlapped. We then performed an enrichment analysis on the common target network and the protein-protein interaction (PPI) network.
Conclusion: The results showed that ADI may inhibit breast cancer through seven important signal pathways involved in the "regulation of vascular endothelial function", "inflammatory response" and "apoptosis” biological processes. Through further clustering and enrichment analysis of the PPI network of ADI’s bioactive component targets and BC-related targets, we found that cancer, ErbB, MAPK, TLR, chemokine, p53 and cell cycle signaling pathway, mainly contributed to the effects of ADI in treating BC. In conclusion, this study reveals the possible mechanism of ADI in treating BC, and provides a new direction for drug development for ADI in treating BC.
Keywords
Breast Cancer, Aidi injection, Network pharmacology
Figure 1
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Figure 5
This is a list of supplementary files associated with this preprint. Click to download.
- S1.ComponentsofeachherbinADI.xlsx
S1. Components of each herb in ADI
- S2.BioactivecomponentsofeachherbinADI.xlsx
S2. Bioactive components of each herb in ADI
- S3.PotentialtargetsofbioactivecomponentsinADI.xlsx
S3. Potential targets of bioactive components in ADI
- S4.KnownBCrelatedtargets.xlsx
S4. Known BC-related targets
- S5.108commontargetsbetweenADIandBC.xlsx
S5.109 common targets between ADI and BC
- S6.Networkcentralityanalysisandevaluation.xlsx
S6. Network centrality analysis and evaluation
- S7.GOandKEGGpathwayanalysisforthe108commontargets.xlsx
S7. GO and KEGG pathway analysis for the 109 common targets
- S8.TheClusterofthecoretargetPPInetwork.xlsx
S8. The Cluster of the core-target PPI network
- S9.GOanalysisforeachcluster.xlsx
S9. GO analysis for each cluster
- S10.KEGGpathwayanalysisforeachcluster.xlsx
S10. KEGG pathway analysis for each cluster
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A new preprint design is coming!
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This is a preprint, a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Research
Miaomiao xi
TANK Medicinal Biology Institute of Xi'an
Corresponding Author
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
History
CURRENT STATUS: Posted
Version 1
Posted 08 Oct, 2020
No community comments so far
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Background: Aidi injection (ADI)is a Chinese patent medicine with anti-cancer effect, which has been used to treat breast cancer (BC) in China for many years, but its potential pharmacological mechanism is still indeterminacy. In this resaearch, network pharmacology, a systematic and comprehensive approach, was used to reveal ADI's potential pharmacological mechanism in treating BC for the first time.
Methods: Databases were used to collect targets related to the bioactive components of ADI and BC. the relevant networks were established by the string database, and were screened potential bioactive components and core targets. Eventually, core targets and pathway enrichment were analyzed by DAVID database.
Results: As the results showed, we collected 99 bioactive ingredients,345 ADI-related targets after deduplication and 368 BC-related targets. Of these, 108 common targets were overlapped. We then performed an enrichment analysis on the common target network and the protein-protein interaction (PPI) network.
Conclusion: The results showed that ADI may inhibit breast cancer through seven important signal pathways involved in the "regulation of vascular endothelial function", "inflammatory response" and "apoptosis” biological processes. Through further clustering and enrichment analysis of the PPI network of ADI’s bioactive component targets and BC-related targets, we found that cancer, ErbB, MAPK, TLR, chemokine, p53 and cell cycle signaling pathway, mainly contributed to the effects of ADI in treating BC. In conclusion, this study reveals the possible mechanism of ADI in treating BC, and provides a new direction for drug development for ADI in treating BC.
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
This is a list of supplementary files associated with this preprint. Click to download.
- S1.ComponentsofeachherbinADI.xlsx
S1. Components of each herb in ADI
- S2.BioactivecomponentsofeachherbinADI.xlsx
S2. Bioactive components of each herb in ADI
- S3.PotentialtargetsofbioactivecomponentsinADI.xlsx
S3. Potential targets of bioactive components in ADI
- S4.KnownBCrelatedtargets.xlsx
S4. Known BC-related targets
- S5.108commontargetsbetweenADIandBC.xlsx
S5.109 common targets between ADI and BC
- S6.Networkcentralityanalysisandevaluation.xlsx
S6. Network centrality analysis and evaluation
- S7.GOandKEGGpathwayanalysisforthe108commontargets.xlsx
S7. GO and KEGG pathway analysis for the 109 common targets
- S8.TheClusterofthecoretargetPPInetwork.xlsx
S8. The Cluster of the core-target PPI network
- S9.GOanalysisforeachcluster.xlsx
S9. GO analysis for each cluster
- S10.KEGGpathwayanalysisforeachcluster.xlsx
S10. KEGG pathway analysis for each cluster
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