{"title":"SRC是Arctigenin治疗三阴性乳腺癌的潜在靶点:基于机器学习算法、分子模型和体外测试。","authors":"Yuezhou Huang, Qing Luo, Linfeng Li, Tianping Li","doi":"10.3389/fmolb.2025.1644169","DOIUrl":null,"url":null,"abstract":"<p><strong>Introduction: </strong>This research explores the therapeutic potential of Arctigenin (AG) against triple-negative breast cancer (TNBC) and elucidates its underlying molecular mechanisms.</p><p><strong>Methods: </strong>Potential targets of AG and TNBC-related genes were identified through public databases. By intersecting drug-specific and disease-related targets, key genes were selected for further analysis. Differential gene expression profiling and Weighted Gene Co-expression Network Analysis (WGCNA) were performed. Functional enrichment analysis was conducted using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). Machine learning algorithms were employed to identify hub genes, followed by validation through molecular docking, molecular dynamics (MD) simulations, and surface plasmon resonance (SPR) assays. In vitro experiments including cell viability assays, cell cycle analysis, apoptosis detection, and Western blotting were performed on MDA-MB-453 and MDA-MB-231 cell lines.</p><p><strong>Results: </strong>Our study identified 183 AG-related targets, 5,193 differentially expressed genes, and 6,173 co-expression module genes associated with TNBC. Machine learning algorithms pinpointed 4 hub genes from 28 intersecting targets. Molecular docking, Molecular dynamics (MD) and surface plasmon resonance (SPR) indicated a moderately strong interaction between AG and SRC kinase, where the oxygen atom of AG forms hydrogen bonds with the oxygen atom in M341 and the nitrogen atom in G344 of SRC. In vitro experiments confirmed that AG reduced the viability of MDA-MB-453 and MDA-MB-231 cells in a concentration-and time-dependent manner, leading S phase arrest and apoptosis. Western blotting indicated that AG significantly reduced the levels of Bcl-2, caspase-3, and caspase-9, as well as decreased SRC, p-PI3K-p85, p-AKT1, p-MEK1/2, and p-ERK1/2 expression in TNBC cells in a concentration dependent manner.</p><p><strong>Conclusion: </strong>AG exerts anti-TNBC effects by directly binding to SRC kinase, concurrently inhibiting both PI3K/AKT and MEK/ERK signaling pathways, ultimately leading to cell cycle arrest and apoptosis.</p>","PeriodicalId":12465,"journal":{"name":"Frontiers in Molecular Biosciences","volume":"12 ","pages":"1644169"},"PeriodicalIF":3.9000,"publicationDate":"2025-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12460110/pdf/","citationCount":"0","resultStr":"{\"title\":\"SRC is a potential target of Arctigenin in treating triple-negative breast cancer: based on machine learning algorithms, molecular modeling and <i>in Vitro</i> test.\",\"authors\":\"Yuezhou Huang, Qing Luo, Linfeng Li, Tianping Li\",\"doi\":\"10.3389/fmolb.2025.1644169\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Introduction: </strong>This research explores the therapeutic potential of Arctigenin (AG) against triple-negative breast cancer (TNBC) and elucidates its underlying molecular mechanisms.</p><p><strong>Methods: </strong>Potential targets of AG and TNBC-related genes were identified through public databases. By intersecting drug-specific and disease-related targets, key genes were selected for further analysis. Differential gene expression profiling and Weighted Gene Co-expression Network Analysis (WGCNA) were performed. Functional enrichment analysis was conducted using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). Machine learning algorithms were employed to identify hub genes, followed by validation through molecular docking, molecular dynamics (MD) simulations, and surface plasmon resonance (SPR) assays. In vitro experiments including cell viability assays, cell cycle analysis, apoptosis detection, and Western blotting were performed on MDA-MB-453 and MDA-MB-231 cell lines.</p><p><strong>Results: </strong>Our study identified 183 AG-related targets, 5,193 differentially expressed genes, and 6,173 co-expression module genes associated with TNBC. Machine learning algorithms pinpointed 4 hub genes from 28 intersecting targets. Molecular docking, Molecular dynamics (MD) and surface plasmon resonance (SPR) indicated a moderately strong interaction between AG and SRC kinase, where the oxygen atom of AG forms hydrogen bonds with the oxygen atom in M341 and the nitrogen atom in G344 of SRC. In vitro experiments confirmed that AG reduced the viability of MDA-MB-453 and MDA-MB-231 cells in a concentration-and time-dependent manner, leading S phase arrest and apoptosis. Western blotting indicated that AG significantly reduced the levels of Bcl-2, caspase-3, and caspase-9, as well as decreased SRC, p-PI3K-p85, p-AKT1, p-MEK1/2, and p-ERK1/2 expression in TNBC cells in a concentration dependent manner.</p><p><strong>Conclusion: </strong>AG exerts anti-TNBC effects by directly binding to SRC kinase, concurrently inhibiting both PI3K/AKT and MEK/ERK signaling pathways, ultimately leading to cell cycle arrest and apoptosis.</p>\",\"PeriodicalId\":12465,\"journal\":{\"name\":\"Frontiers in Molecular Biosciences\",\"volume\":\"12 \",\"pages\":\"1644169\"},\"PeriodicalIF\":3.9000,\"publicationDate\":\"2025-09-11\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12460110/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Frontiers in Molecular Biosciences\",\"FirstCategoryId\":\"99\",\"ListUrlMain\":\"https://doi.org/10.3389/fmolb.2025.1644169\",\"RegionNum\":3,\"RegionCategory\":\"生物学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2025/1/1 0:00:00\",\"PubModel\":\"eCollection\",\"JCR\":\"Q2\",\"JCRName\":\"BIOCHEMISTRY & MOLECULAR BIOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Frontiers in Molecular Biosciences","FirstCategoryId":"99","ListUrlMain":"https://doi.org/10.3389/fmolb.2025.1644169","RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2025/1/1 0:00:00","PubModel":"eCollection","JCR":"Q2","JCRName":"BIOCHEMISTRY & MOLECULAR BIOLOGY","Score":null,"Total":0}
SRC is a potential target of Arctigenin in treating triple-negative breast cancer: based on machine learning algorithms, molecular modeling and in Vitro test.
Introduction: This research explores the therapeutic potential of Arctigenin (AG) against triple-negative breast cancer (TNBC) and elucidates its underlying molecular mechanisms.
Methods: Potential targets of AG and TNBC-related genes were identified through public databases. By intersecting drug-specific and disease-related targets, key genes were selected for further analysis. Differential gene expression profiling and Weighted Gene Co-expression Network Analysis (WGCNA) were performed. Functional enrichment analysis was conducted using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). Machine learning algorithms were employed to identify hub genes, followed by validation through molecular docking, molecular dynamics (MD) simulations, and surface plasmon resonance (SPR) assays. In vitro experiments including cell viability assays, cell cycle analysis, apoptosis detection, and Western blotting were performed on MDA-MB-453 and MDA-MB-231 cell lines.
Results: Our study identified 183 AG-related targets, 5,193 differentially expressed genes, and 6,173 co-expression module genes associated with TNBC. Machine learning algorithms pinpointed 4 hub genes from 28 intersecting targets. Molecular docking, Molecular dynamics (MD) and surface plasmon resonance (SPR) indicated a moderately strong interaction between AG and SRC kinase, where the oxygen atom of AG forms hydrogen bonds with the oxygen atom in M341 and the nitrogen atom in G344 of SRC. In vitro experiments confirmed that AG reduced the viability of MDA-MB-453 and MDA-MB-231 cells in a concentration-and time-dependent manner, leading S phase arrest and apoptosis. Western blotting indicated that AG significantly reduced the levels of Bcl-2, caspase-3, and caspase-9, as well as decreased SRC, p-PI3K-p85, p-AKT1, p-MEK1/2, and p-ERK1/2 expression in TNBC cells in a concentration dependent manner.
Conclusion: AG exerts anti-TNBC effects by directly binding to SRC kinase, concurrently inhibiting both PI3K/AKT and MEK/ERK signaling pathways, ultimately leading to cell cycle arrest and apoptosis.
期刊介绍:
Much of contemporary investigation in the life sciences is devoted to the molecular-scale understanding of the relationships between genes and the environment — in particular, dynamic alterations in the levels, modifications, and interactions of cellular effectors, including proteins. Frontiers in Molecular Biosciences offers an international publication platform for basic as well as applied research; we encourage contributions spanning both established and emerging areas of biology. To this end, the journal draws from empirical disciplines such as structural biology, enzymology, biochemistry, and biophysics, capitalizing as well on the technological advancements that have enabled metabolomics and proteomics measurements in massively parallel throughput, and the development of robust and innovative computational biology strategies. We also recognize influences from medicine and technology, welcoming studies in molecular genetics, molecular diagnostics and therapeutics, and nanotechnology.
Our ultimate objective is the comprehensive illustration of the molecular mechanisms regulating proteins, nucleic acids, carbohydrates, lipids, and small metabolites in organisms across all branches of life.
In addition to interesting new findings, techniques, and applications, Frontiers in Molecular Biosciences will consider new testable hypotheses to inspire different perspectives and stimulate scientific dialogue. The integration of in silico, in vitro, and in vivo approaches will benefit endeavors across all domains of the life sciences.