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Ovarian cancer (OC) is characterised by heterogeneity that complicates the prediction of patient survival and treatment outcomes. Here, we conducted analyses to predict the prognosis of patients from the Genomic Data Commons database and validated the predictions by fivefold cross-validation and by using an independent dataset in the International Cancer Genome Consortium database. We analysed the somatic DNA mutation, mRNA expression, DNA methylation, and microRNA expression data of 1203 samples from 599 serous ovarian cancer (SOC) patients. We found that principal component transformation (PCT) improved the predictive performance of the survival and therapeutic models. Deep learning algorithms also showed better predictive power than the decision tree (DT) and random forest (RF). Furthermore, we identified a series of molecular features and pathways that are associated with patient survival and treatment outcomes. Our study provides perspective on building reliable prognostic and therapeutic strategies and further illuminates the molecular mechanisms of SOC. What is already known on this subject? Recent studies have focussed on predicting cancer outcomes based on omics data. But the limitation is the performance of single-platform genomic analyses or the small numbers of genomic analyses. What do the results of this study add? We analysed multi-omics data, found that principal component transformation (PCT) significantly improved the predictive performance of the survival and therapeutic models. Deep learning algorithms also showed better predictive power than did decision tree (DT) and random forest (RF). Furthermore, we identified a series of molecular features and pathways that are associated with patient survival and treatment outcomes. What are the implications of these findings for clinical practice and/or further research? Our study provides perspective on how to build reliable prognostic and therapeutic strategies and further illuminates the molecular mechanisms of SOC for future studies. [ABSTRACT FROM AUTHOR]

Ovarian cancer (OC) is characterised by heterogeneity that complicates the prediction of patient survival and treatment outcomes. Here, we conducted analyses to predict the prognosis of patients from the Genomic Data Commons database and validated the predictions by fivefold cross-validation and by using an independent dataset in the International Cancer Genome Consortium database. We analysed the somatic DNA mutation, mRNA expression, DNA methylation, and microRNA expression data of 1203 samples from 599 serous ovarian cancer (SOC) patients. We found that principal component transformation (PCT) improved the predictive performance of the survival and therapeutic models. Deep learning algorithms also showed better predictive power than the decision tree (DT) and random forest (RF). Furthermore, we identified a series of molecular features and pathways that are associated with patient survival and treatment outcomes. Our study provides perspective on building reliable prognostic and therapeutic strategies and further illuminates the molecular mechanisms of SOC. What is already known on this subject? Recent studies have focussed on predicting cancer outcomes based on omics data. But the limitation is the performance of single-platform genomic analyses or the small numbers of genomic analyses. What do the results of this study add? We analysed multi-omics data, found that principal component transformation (PCT) significantly improved the predictive performance of the survival and therapeutic models. Deep learning algorithms also showed better predictive power than did decision tree (DT) and random forest (RF). Furthermore, we identified a series of molecular features and pathways that are associated with patient survival and treatment outcomes. What are the implications of these findings for clinical practice and/or further research? Our study provides perspective on how to build reliable prognostic and therapeutic strategies and further illuminates the molecular mechanisms of SOC for future studies. [ABSTRACT FROM AUTHOR]