Developing a 5-gene Prognostic Signature for Cervical Cancer by Integrating MRNA and Copy Number Variations

Overview

Journal BMC Cancer

Publisher Biomed Central

Specialty Oncology

Date 2022 Feb 21

PMID 35184747

Authors

Wenxin Liu

Qiuying Jiang

Chao Sun

Shihao Liu

Zhikun Zhao

Dongfang Wu

Affiliations

Soon will be listed here.

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 cancer samples into high-risk and low-risk groups with distinctly different prognosis. Low-risk group exhibited more favorable prognosis and higher immune infiltration than high-risk group. Both univariate and multivariate Cox regression analysis showed that the risk score was an independent risk factor for cervical cancer.

Conclusions: The 5-gene prognostic signature could serve as a predictor for identifying high-risk cervical cancer patients, and provided potential direction for studying the mechanism or drug targets of cervical cancer. The integrated analysis of CNV and mRNA expanded a new perspective for exploring prognostic signatures in cervical cancer.

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