Deciphering the potential pathogenic mechanisms of 3-BHA in ovarian cancer through integrated bioinformatics and machine learning strategies.

Ovarian cancer (OC) remains a malignancy characterized by obscure risk factors and unfavorable prognosis. While 3-tert-butyl-4-hydroxyanisole (3-BHA) is suspected of exerting toxic effects on ovarian health, the precise molecular mechanisms underlying its impact remain elucidated. This study aims to systematically investigate the potential pathogenic mechanisms of 3-BHA in the progression of OC.Integrated transcriptomic data from the GEO database (GSE18520 and GSE40595) were analyzed. A synergistic computational framework was employed, incorporating Differentially Expressed Genes (DEGs) identification, Weighted Gene Co-expression Network Analysis (WGCNA), multiple machine learning algorithms, and SHapley Additive exPlanations (SHAP) analysis to achieve high-interpretability feature selection.Five hub genes-CXCR4, CCL7, CXCL8, CXCR2, and CX3CL1-were identified, all demonstrating robust diagnostic efficacy with AUC values of 0.911, 0.882, 0.823, 0.772, and 0.837, respectively. Prognostic profiling via GEPIA3 highlighted CXCR2 overexpression as a potential critical biomarker driving poor clinical outcomes in OC. Furthermore, molecular docking validated the strong binding affinity of 3-BHA with CX3CL1 and CXCR2. Subsequent 100 ns molecular dynamics simulations and thermodynamic stability assessments confirmed the structural stability of the 3-BHA-CXCR2 complex.By integrating bioinformatics and computational toxicology, this study deciphers the potential mechanistic landscape through which 3-BHA influences OC. These findings not only refine the toxicological understanding of 3-BHA but also provide novel candidates for early diagnosis and prognostic risk stratification in OC.