Bioinformatics analysis to screen the key prognostic genes in ovarian cancer.

ABSTRACT:

Background

Ovarian cancer (OC) is a gynecological oncology that has a poor prognosis and high mortality. This study is conducted to identify the key genes implicated in the prognosis of OC by bioinformatic analysis.

Methods

Gene expression data (including 568 primary OC tissues, 17 recurrent OC tissues, and 8 adjacent normal tissues) and the relevant clinical information of OC patients were downloaded from The Cancer Genome Atlas database. After data preprocessing, cluster analysis was conducted using the ConsensusClusterPlus package in R. Using the limma package in R, differential analysis was performed to identify feature genes. Based on Kaplan-Meier (KM) survival analysis, prognostic seed genes were selected from the feature genes. After key prognostic genes were further

SUBMITTER: Li L 

PROVIDER: S-EPMC5390356 | biostudies-literature | 2017 Apr

REPOSITORIES: biostudies-literature