A Metabolic Gene Prognostic Risk Model for Cervical Cancer

Abstract

Background: Previous studies have identified hundreds of constantly changing metabolic genes in cervical cancer, however, their prognostic effect remains to be explored. Methods: In this paper, Cox univariate regression and Lasso regression models were used to identify metabolic genes associated with squamous cervical cancer prognosis, and developed a prognostic risk score. Next, on the basis of the median risk score, cervical squamous cancer patients were divided into two groups: high- and low-risk patients. Kaplan-Meier analysis and receiver operating characteristic (ROC) curves were used to evaluate the predictive efficacy of the metabolic gene prognostic risk model. In addition, we analysed the correlation between drug sensitivity, immune cell infiltration, and Gene set variation analysis (GSVA) and the metabolic gene prognostic risk model. Results: The results showed that the prognosis of patients in the high-risk group was worse. The metabolic gene prognostic model was correlated with immune cell infiltration. It is also correlated with sensitivity to common chemotherapeutic drugs. In addition, gene set enrichment analysis results revealed several significantly enriched pathways, which may help to explain the underlying mechanisms of cervical carcinogenesis. Conclusions: The proposed prediction model can be potentially used for prognosis prediction of cervical cancer.