Exploring the Underlying Molecular Mechanisms of Chaetomugilin Q in Breast Cancer Using Network Pharmacology, Molecular Docking, and Bioinformatics

Chaetomugilin Q has demonstrated anti-tumor effects in preclinical models. However, the potential mechanisms of chaetomugilin Q in treating breast cancer remain unclear. This study used network pharmacology and molecular docking prediction to explore these mechanisms and prioritize candidate targets of chaetomugilin Q in breast cancer. PharmMapper, SuperPred, and GalaxySagittarius-AF were utilized to obtain targets associated with chaetomugilin Q, while GeneCards, CTD, DrugBank, OMIM, and TTD were used to collect breast cancer-related targets. A total of 299 overlapping candidate targets were obtained using Venny 2.1.0. Subsequently, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed using the DAVID tool. These candidate targets were input into the STRING database and Cytoscape 3.10.1 software to construct a protein–protein interaction (PPI) network and prioritize candidate core targets. Molecular docking prediction was performed using the CB-Dock2 server. The candidate core targets were further analyzed using GEPIA, HPA, cBioPortal, Kaplan–Meier Plotter, and TIMER. After GO and KEGG enrichment analyses, 1174 relevant terms and 169 signaling pathways were identified, respectively. PIK3R1, HSP90AA1, SRC, AKT1, STAT3, PIK3CD, PIK3CB, EGFR, GRB2, and ESR1 were prioritized as candidate core targets. Molecular docking results showed that the predicted docking scores of chaetomugilin Q to the candidate core targets were lower than −5 kcal/mol. Bioinformatics analyses based on independent public databases indicated that these candidate core targets exhibited differential expression at the mRNA and protein levels and were associated with patient prognosis, mutation status, and immune cell infiltration, providing clinical and biological context. This study provides a computational prediction of the potential mechanisms of chaetomugilin Q against breast cancer and a theoretical basis for subsequent experimental validation.

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Journal
Molecules
Published
2026-10-13
DOI
https://doi.org/10.3390/molecules31203661
Primary Topic
Computational Drug Discovery Methods
Type
article
Field-Weighted Citation Impact
0.00

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article

Exploring the Underlying Molecular Mechanisms of Chaetomugilin Q in Breast Cancer Using Network Pharmacology, Molecular Docking, and Bioinformatics

Jingjing Mu, Q. He, Jianchun Qin, Feng Wang et al.
Molecules
Computational Drug Discovery Methods
article

Exploring the Underlying Molecular Mechanisms of Chaetomugilin Q in Breast Cancer Using Network Pharmacology, Molecular Docking, and Bioinformatics

Jingjing Mu, Q. He, Jianchun Qin, Feng Wang, Zhirong Su, Yuying Miao, Jiaxuan Xiang, Weibing Li
article en

Abstract

Chaetomugilin Q has demonstrated anti-tumor effects in preclinical models. However, the potential mechanisms of chaetomugilin Q in treating breast cancer remain unclear. This study used network pharmacology and molecular docking prediction to explore these mechanisms and prioritize candidate targets of chaetomugilin Q in breast cancer. PharmMapper, SuperPred, and GalaxySagittarius-AF were utilized to obtain targets associated with chaetomugilin Q, while GeneCards, CTD, DrugBank, OMIM, and TTD were used to collect breast cancer-related targets. A total of 299 overlapping candidate targets were obtained using Venny 2.1.0. Subsequently, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed using the DAVID tool. These candidate targets were input into the STRING database and Cytoscape 3.10.1 software to construct a protein–protein interaction (PPI) network and prioritize candidate core targets. Molecular docking prediction was performed using the CB-Dock2 server. The candidate core targets were further analyzed using GEPIA, HPA, cBioPortal, Kaplan–Meier Plotter, and TIMER. After GO and KEGG enrichment analyses, 1174 relevant terms and 169 signaling pathways were identified, respectively. PIK3R1, HSP90AA1, SRC, AKT1, STAT3, PIK3CD, PIK3CB, EGFR, GRB2, and ESR1 were prioritized as candidate core targets. Molecular docking results showed that the predicted docking scores of chaetomugilin Q to the candidate core targets were lower than −5 kcal/mol. Bioinformatics analyses based on independent public databases indicated that these candidate core targets exhibited differential expression at the mRNA and protein levels and were associated with patient prognosis, mutation status, and immune cell infiltration, providing clinical and biological context. This study provides a computational prediction of the potential mechanisms of chaetomugilin Q against breast cancer and a theoretical basis for subsequent experimental validation.

MoleculesVol. 31(20)
Yuncheng University (CN), Jilin University (CN)
ShanXi Science and Technology Department, Shanxi Provincial Education Department
Openalex Percentile: Top 14%
Computational Drug Discovery Methods
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