Assessing the clinical utility of multi-omics data for predicting serous ovarian cancer prognosis

Abstract

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.

Impact statement

• 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.

Keywords:

• Ovarian cancer
• prognosis
• treatment
• biomarker
• multi-omics data and prediction model

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

There is no data needed to be deposited. The source code used in our study is available via https://github.com/zhezhe301/ov_prognosis_prediction.git. The datasets generated and/or analysed during the current study are available from the corresponding author on reasonable request. This study was performed according to the Enhancing the QUAlity and Transparentcy of health Research (EQUATOR) network guideline (Ogrinc et al. 2019).

Additional information

Funding

This work was supported by the General Program [31771397, 81573251, 81503089, 81571411] of the Natural Science Foundation of China (www.nsfc.gov.cn) and the Beijing Nova Program (20180059).