Integrating in-silico and experimental validation approaches to unveil the therapeutic mechanism of naringenin against breast cancer

Naringenin (NAR), a flavanone abundant in citrus fruits, has shown antiproliferative effects in several cancers, including breast cancer. However, its precise molecular mechanisms remain unclear. This study integrates network pharmacology, molecular modeling, and in vitro assays to investigate the anti-breast cancer potential of NAR. Target Genes associated with both NAR and breast cancer were identified through multiple databases, yielding 62 overlapping genes, which were further analyzed via a protein-protein interaction (PPI) network. Gene Ontology (GO) and KEGG pathway enrichment analyses revealed key involvement of PI3K-Akt and MAPK signaling pathways in NAR's mechanism of action. Molecular docking studies showed strong binding affinities of NAR with key targets SRC, PIK3CA, BCL2, and ESR1, findings supported by molecular dynamics (MD) simulations, which confirmed stable protein-ligand interactions. Cell-based assays using MCF-7 human breast cancer cells demonstrated that NAR inhibits proliferation, induces apoptosis, reduces migration, and increases reactive oxygen species (ROS) generation. These results validate computational predictions and suggest that SRC may be a primary target mediating NAR's anticancer activity. Collectively, this study provides mechanistic insights into the anti-breast cancer action of NAR and supports its potential as a lead compound for the development of SRC-targeted breast cancer therapies.

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Publication Details

Journal
Scientific Reports
Published
2025-10-15
DOI
https://doi.org/10.1038/s41598-025-19931-6
Citations
1
Primary Topic
Computational Drug Discovery Methods
Type
article
Field-Weighted Citation Impact
1.50

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article

Integrating in-silico and experimental validation approaches to unveil the therapeutic mechanism of naringenin against breast cancer

Ashraf Dar, Basharat Bhat, Suhail Ahmad Mir, Umme Hani et al.
1 citations
Scientific Reports
Computational Drug Discovery Methods
1.50
article

Integrating in-silico and experimental validation approaches to unveil the therapeutic mechanism of naringenin against breast cancer

Ashraf Dar, Basharat Bhat, Suhail Ahmad Mir, Umme Hani, Riyaz Ali M. Osmani, Ghulam Nabi Bader, Laraibah Hamid, Nazima Haider, Nasir Nisar, Zahrah Ali Ahmed Asiri
article en
1 citations

Abstract

Naringenin (NAR), a flavanone abundant in citrus fruits, has shown antiproliferative effects in several cancers, including breast cancer. However, its precise molecular mechanisms remain unclear. This study integrates network pharmacology, molecular modeling, and in vitro assays to investigate the anti-breast cancer potential of NAR. Target Genes associated with both NAR and breast cancer were identified through multiple databases, yielding 62 overlapping genes, which were further analyzed via a protein-protein interaction (PPI) network. Gene Ontology (GO) and KEGG pathway enrichment analyses revealed key involvement of PI3K-Akt and MAPK signaling pathways in NAR's mechanism of action. Molecular docking studies showed strong binding affinities of NAR with key targets SRC, PIK3CA, BCL2, and ESR1, findings supported by molecular dynamics (MD) simulations, which confirmed stable protein-ligand interactions. Cell-based assays using MCF-7 human breast cancer cells demonstrated that NAR inhibits proliferation, induces apoptosis, reduces migration, and increases reactive oxygen species (ROS) generation. These results validate computational predictions and suggest that SRC may be a primary target mediating NAR's anticancer activity. Collectively, this study provides mechanistic insights into the anti-breast cancer action of NAR and supports its potential as a lead compound for the development of SRC-targeted breast cancer therapies.

Scientific ReportsVol. 15(1)
Government of Maharashtra (IN), University of Kashmir (IN), King Khalid University (SA)
Department of Science and Technology, Ministry of Science and Technology, India, King Khalid University
Openalex Percentile: Top 12%
Computational Drug Discovery Methods
1.50
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