Protein expression profiling identifies a prognostic model for ovarian cancer.

ABSTRACT:

Background

Owing to the high morbidity and mortality, ovarian cancer has seriously endangered female health. Development of reliable models can facilitate prognosis monitoring and help relieve the distress.

Methods

Using the data archived in the TCPA and TCGA databases, proteins having significant survival effects on ovarian cancer patients were screened by univariate Cox regression analysis. Patients with complete information concerning protein expression, survival, and clinical variables were included. A risk model was then constructed by performing multiple Cox regression analysis. After validation, the predictive power of the risk model was assessed. The prognostic effect and the biological function of the model were evaluated using co-expression analysis and enrichment

SUBMITTER: Xiong L 

PROVIDER: S-EPMC9284690 | biostudies-literature | 2022 Jul

REPOSITORIES: biostudies-literature