Identification and validation of a novel glycolysis-related gene signature for predicting the prognosis in ovarian cancer.

ABSTRACT:

Background

Ovarian cancer (OC) is the most lethal gynaecological tumor. Changes in glycolysis have been proven to play an important role in OC progression. We aimed to identify a novel glycolysis-related gene signature to better predict the prognosis of patients with OC.

Methods

mRNA and clinical data were obtained from The Cancer Genome Atlas (TCGA), International Cancer Genome Consortium (ICGC) and Genotype Tissue Expression (GTEx) database. The "limma" R package was used to identify glycolysis-related differentially expressed genes (DEGs). Then, a multivariate Cox proportional regression model and survival analysis were used to develop a glycolysis-related gene signature. Furthermore, the TCGA training set was divided into two internal test sets for validation, while the

SUBMITTER: Yu J 

PROVIDER: S-EPMC8258938 | biostudies-literature | 2021 Jul

REPOSITORIES: biostudies-literature