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<p>Inter-patient heterogeneity complicates predicting treatment response in ovarian cancer (OC). We developed OMICS-FUSE, an early-fusion multi-omics predictive model integrating proteomic, transcriptomic, and methylomic data from OC patients, evaluated across five machine learning algorithms with SHapley Additive exPlanations (SHAP) and experimental validation. The early-fusion Random Forest model achieved excellent predictive accuracy (AUC = 0.939, accuracy = 0.896, F1 = 0.939), with performance comparable to or surpassing that of the best-performing single-omics models. Nevertheless, the multi-omics framework yielded superior balance across accuracy and F1 score. SHAP analysis identified key determinants of treatment response, including CLEC2A, MYH4, and methylation of SYT12_1, with functional enrichment implicating immune regulation, metabolic pathways, and drug resistance signaling. Experimental validation confirmed six hub genes (<i>CASP8</i>, <i>AQP8</i>, <i>CAV1</i>, <i>FN1</i>, <i>CREB1</i>, <i>KDR</i>), exhibiting expression patterns associated with drug resistance, immune regulation, and prognosis. This multi-omics machine learning model enables robust, interpretable prediction, uncovering molecular signatures for therapeutic stratification and precision oncology in OC.</p>

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Multi-omics fusion with machine learning enables robust prediction of treatment response in ovarian cancer for precision population health

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月14日

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  • Jie Chen,
  • Tianshi Mao,
  • Yu Yang,
  • Yue Wu,
  • Mingqi Wang,
  • Xiexia Huang,
  • Li Su,
  • Xiaohong Wei,
  • Guiyang Xia,
  • Huan Xia,
  • Sheng Lin,
  • Mei Zhang

摘要

Inter-patient heterogeneity complicates predicting treatment response in ovarian cancer (OC). We developed OMICS-FUSE, an early-fusion multi-omics predictive model integrating proteomic, transcriptomic, and methylomic data from OC patients, evaluated across five machine learning algorithms with SHapley Additive exPlanations (SHAP) and experimental validation. The early-fusion Random Forest model achieved excellent predictive accuracy (AUC = 0.939, accuracy = 0.896, F1 = 0.939), with performance comparable to or surpassing that of the best-performing single-omics models. Nevertheless, the multi-omics framework yielded superior balance across accuracy and F1 score. SHAP analysis identified key determinants of treatment response, including CLEC2A, MYH4, and methylation of SYT12_1, with functional enrichment implicating immune regulation, metabolic pathways, and drug resistance signaling. Experimental validation confirmed six hub genes (CASP8, AQP8, CAV1, FN1, CREB1, KDR), exhibiting expression patterns associated with drug resistance, immune regulation, and prognosis. This multi-omics machine learning model enables robust, interpretable prediction, uncovering molecular signatures for therapeutic stratification and precision oncology in OC.