Machine learning-derived identification of prognostic signature for improving prognosis and drug response in patients with ovarian cancer.

ABSTRACT: Clinical assessments relying on pathology classification demonstrate limited effectiveness in predicting clinical outcomes and providing optimal treatment for patients with ovarian cancer (OV). Consequently, there is an urgent requirement for an ideal biomarker to facilitate precision medicine. To address this issue, we selected 15 multicentre cohorts, comprising 12 OV cohorts and 3 immunotherapy cohorts. Initially, we identified a set of robust prognostic risk genes using data from the 12 OV cohorts. Subsequently, we employed a consensus cluster analysis to identify distinct clusters based on the expression profiles of the risk genes. Finally, a machine learning-derived prognostic signature (MLDPS) was developed based on differentially expressed genes and univariate Cox regression genes b

SUBMITTER: Huan Q 

PROVIDER: S-EPMC10805490 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature