Mechanistic Insights into IL-17 Pathway Modulation in Breast Cancer by Pteridaceae Bioactive Compounds: A Network Pharmacology and Molecular Docking Approach

Abstract

Abstract Objective This study assessed the pharmacological prospects of Pteridaceae phytoconstituents using an integrated network pharmacology (NP) and computational molecular docking (MD) strategy. Materials and Methods Various peer-reviewed literature databases, including ScienceDirect, PubMed, Scopus, and Google Scholar, were used to gather bioactive constituents reported from this family. Absorption, Distribution, Metabolism, Excretion and Toxicology (ADMET) parameters, drug-likeness rules, and bioavailability scores were evaluated using SwissADME software, while toxicity profiles were predicted using ProTox-3.0. Potential targets of each compound were identified using Swiss Target Prediction software. Breast cancer–specific genes were obtained from OMIM (Online Mendelian Inheritance in Man), UniProt, CTD (Comparative Toxicogenomic Database), PharmGKB, and GeneCards. NP tools were employed to identify common targets between drugs and diseases, construct a protein–protein interaction (PPI) network, and perform Gene Ontology (GO) and KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway analyses. Finally, MD analysis was performed to determine the binding efficiency of selected compounds with hub genes, thereby validating their potential activity. Results The study identified 143 predicted targets for six Pteridaceae-derived compounds, along with 12,120 breast cancer–associated targets retrieved from multiple databases. Comparison yielded 139 overlapping protein targets (4 of which showed low interaction scores), which were subsequently mapped into a PPI network consisting of 135 nodes and 683 edges. KEGG-based pathway and GO enrichment analyses revealed 111 signaling pathways, 86 biological processes, 22 cellular components, and 20 molecular functions. KEGG pathway analysis identified the interleukin-17 pathway as significantly enriched. MD analysis showed that pterosin J exhibited the highest binding affinities among the six compounds tested, with docking scores of −7.4, −7.3, −7.3, and −6.7 kcal/mol against PTGS2, HSP90AA1, GSK-3β, and MAPK3, respectively. Conclusion The present bioinformatics study suggested that pterosin J may have anticancer potential against breast cancer. Further in vitro and in vivo experiments are suggested to confirm the potential of the compound.