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<p>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.</p>

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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月3日

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  • Yifei Shi,
  • Dong Niu,
  • Chunhui Jin

摘要

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.