Identification of a Recurrence Gene Signature for Ovarian Cancer Prognosis by Integrating Single-Cell RNA Sequencing and Bulk Expression Datasets.

ABSTRACT: Ovarian cancer is one of the most common gynecological malignancies in women, with a poor prognosis and high mortality. With the expansion of single-cell RNA sequencing technologies, the inner biological mechanism involved in tumor recurrence should be explored at the single-cell level, and novel prognostic signatures derived from recurrence events were urgently identified. In this study, we identified recurrence-related genes for ovarian cancer by integrating two Gene Expression Omnibus datasets, including an ovarian cancer single-cell RNA sequencing dataset (GSE146026) and a bulk expression dataset (GSE44104). Based on these recurrence genes, we further utilized the merged expression dataset containing a total of 524 ovarian cancer samples to identify prognostic signatures and constructe

SUBMITTER: Zhang Y 

PROVIDER: S-EPMC9214038 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature