Integrative Bioinformatics and Systems Biology Approach for the Identification of Key Genes Responses to Masoprocol Treatment in Breast Cancer
DOI:
https://doi.org/10.64229/t2j17t84Keywords:
Masoprocol, Breast cancer, TP53, Mutation, Molecular docking, Protein-protein interactions, Pathway enrichment, Systems biologyAbstract
This study presents a system-level investigation repositioning Masoprocol as a potential therapeutic candidate for breast cancer. Previously withdrawn due to limited mechanistic insights, Masoprocol was re-evaluated using integrative bioinformatics and systems biology to reveal its multi-target potential and pharmacological relevance. Absorption, distribution, metabolism, excretion, and toxicity (ADMET) profiling confirmed favourable drug-like characteristics, including Lipinski compliance, low predicted toxicity, and limited central nervous system penetration. Target prediction and enrichment analyses were performed, followed by construction of a protein-protein interaction network and a gene regulatory network. Masoprocol was found to interact with multiple breast cancer-associated genes, with TP53 emerging as a prominent regulatory hub across PPI and GRN layers. Transcriptomic profiling showed ESR1, MMP9, and AR were upregulated in tumors, whereas PTGS2 was downregulated. Elevated MMP9 and AR levels correlated with poor overall survival. Co-expression analysis linked Masoprocol’s targets to oncogenic regulators involved in epithelial-mesenchymal transition and hormone signaling. Immune infiltration analysis suggested potential immunomodulatory effects. Gene ontology and Kyoto Encyclopedia of Genes and Genomes enrichment highlighted estrogen signaling and multiple cancer-associated pathways. Docking and molecular dynamics simulations with the Y220C TP53 mutant showed strong binding but moderate conformational stability, indicating a need for structural optimization. These findings support Masoprocol’s repositioning as a polypharmacological breast cancer candidate and provide a roadmap for its future optimization and experimental validation.
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