Developing a 5-gene prognostic signature for cervical cancer by integrating mRNA and copy number variations.

ABSTRACT:

Background

Cervical cancer is frequently detected gynecological cancer all over the world. This study was designed to develop a prognostic signature for an effective prediction of cervical cancer prognosis.

Methods

Differentially expressed genes (DEGs) were identified based on copy number variation (CNV) data and expression profiles from different databases. A prognostic model was constructed and further optimized by stepwise Akaike information criterion (stepAIC). The model was then evaluated in three groups (training group, test group and validation group). Functional analysis and immune analysis were used to assess the difference between high-risk and low-risk groups.

Results

The study developed a 5-gene prognostic model that could accurately classify cervical canc

SUBMITTER: Liu W 

PROVIDER: S-EPMC8859909 | biostudies-literature | 2022 Feb

REPOSITORIES: biostudies-literature