Identification of differentially expressed genes and signaling pathways in ovarian cancer by integrated bioinformatics analysis.

ABSTRACT:

Background

The mortality rate associated with ovarian cancer ranks the highest among gynecological malignancies. However, the cause and underlying molecular events of ovarian cancer are not clear. Here, we applied integrated bioinformatics to identify key pathogenic genes involved in ovarian cancer and reveal potential molecular mechanisms.

Results

The expression profiles of GDS3592, GSE54388, and GSE66957 were downloaded from the Gene Expression Omnibus (GEO) database, which contained 115 samples, including 85 cases of ovarian cancer samples and 30 cases of normal ovarian samples. The three microarray datasets were integrated to obtain differentially expressed genes (DEGs) and were deeply analyzed by bioinformatics methods. The gene ontology (GO) and Kyoto Encyclopedia of G

SUBMITTER: Yang X 

PROVIDER: S-EPMC5858852 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature