BERBERINE ENGAGES A SEROTONERGIC–MONOAMINERGIC AND JAK/PI3K MULTI-TARGET AXIS IN BREAST CANCER: AN INTEGRATED NETWORK PHARMACOLOGY STUDY

Abstract— Berberine, a quaternary protoberberine alkaloid of Berberis and Coptis species, showsreproducible anti-proliferative activity in breast cancer models, but the experimental literature isfragmented across unrelated pathways and no prioritised target map exists. This study converts thatscattered evidence into a ranked, robustness-tested target network. Berberine (PubChem CID 2353)was retrieved, its three-dimensional structure generated with the ETKDG algorithm and minimisedwith MMFF94 in RDKit, and probable human targets predicted with SwissTargetPrediction. Breastcancer-associated genes were retrieved from GeneCards and intersected with the predicted targets inVenny 2.1; because an unfiltered disease query returns essentially the whole annotated genome, theintersection was recomputed across five GeneCards relevance-score thresholds as a sensitivityanalysis. Common targets were assembled into a STRING v12.0 network, visualised in Cytoscape3.10 and ranked by cytoHubba maximal clique centrality (MCC) together with degree, betweennessand closeness centrality computed independently in NetworkX. Functional annotation usedg:Profiler, Enrichr and ShinyGO against Gene Ontology, KEGG, Reactome and WikiPathways.Ninety-six unique targets intersected the disease gene set; 96 of 96 survived a relevance threshold of10 and 89 of 96 a threshold of 20. The network comprised 94 connected proteins and 398 edges, witha mean clustering coefficient of 0.461 against a density of 0.091, indicating strong modularity. Twocoherent modules emerged: a serotonergic–monoaminergic module (SLC6A4, MAOA, MAOB andfive 5-HT receptor subtypes) recovered by MCC, and a JAK/PI3K–nuclear-receptor module (ESR1,PPARG, GSK3B, PARP1, EP300, PIK3CA, JAK1/2) recovered by the centrality measures.Enrichment was dominated by serotonin binding, neuroactive ligand–receptor interaction,serotonergic synapse, cAMP and calcium signalling, pathways in cancer, tryptophan metabolism andPI3K–Akt signalling. The analysis nominates SLC6A4, MAOB, JAK2, PIK3CA and PARP1 as thebest-supported priorities for experimental validation.