Identification and construction of a prognostic risk model based on multi-RNA methylation regulators in cervical cancer.

Abstract

Cervical cancer is one of the most aggressive female cancers. RNA methylation is a necessary epigenetic modification in biological process. This study aimed to construct an RNA methylation regulator-based risk model for predicting the prognosis of cervical cancer patients. The transcriptome profiles of cervical cancer data were obtained from The Cancer Genome Atlas (TCGA) and GSE44001. An RNA methylation-related risk model was constructed and assessed by the Least absolute shrinkage and selection operator (Lasso)-penalized Cox regression model and receiver operating characteristic (ROC). Kaplan-Meier and Cox regression analyses were used to evaluate the prognostic effect of the risk model and calculated scores. The immune infiltration difference was further analyzed between the subgroups with a single-sample gene set enrichment analysis (ssGSEA). A total of 63 methylation modulators were included in this study, and 618 cervical cancer patients were identified from TCGA and GSE44001. Differential expression genes profiling RNA methylation regulators between normal and tumor samples were distinct. A four-gene signature panel was constructed to predict the prognostic risk. The predictive ability was satisfactory. Cervical cancer patients were classified into high- or low-risk subgroups according to the median risk score. Moreover, the immune infiltration patterns between them differed. A risk model including four RNA methylation regulators was constructed, which will provide new perspectives for further investigation of the relationship between RNA methylation and cervical cancer.