Systematic analyses of a novel circRNA-related miRNAs prognostic signature for Cervical Cancer

Abstract

Accumulating evidences shed light on the important roles of Circular RNAs (circRNAs) acting as competing endogenous RNAs (ceRNAs) in cervical cancer (CC) biology. The present study aimed to identify a novel circRNA-related prognostic signature for CC. The expression data and clinical information of CC were downloaded from the Gene Expression Omnibus (GEO) datasets to identify the differential circRNAs expression. Based on the targeted miRNA prediction, circRNA-related miRNAs were detected in training group and validation group of The Cancer Genome Atlas (TCGA) dataset to construct the novel prognostic signature of CC with least absolute shrinkage and selection operator (LASSO). Moreover, the Kaplan-Meier (K-M) analysis was applied to test the model. In the present study, three differentially expressed circRNAs (hsa_circ_0001498, hsa_circ_0066147, and hsa_circ_0006948) were identified in GSE102686 and GSE107472. Then, with the criteria 25 predicted miRNAs were analyzed in TCGA datasets to calculate the prognostic signature. Furthermore, we developed a six-miRNA signature (hsa-miR-217, hsa-miR-30b-3p, hsa-miR-136-5p, hsa-miR-185-3p, hsa-miR-501-5p and hsa-miR-658) based on their expression level and coefficients. We performed a Pearson correlation analysis to screen 47 mRNAs which are negatively regulated by these six miRNAs. Functional enrichment analysis indicated these mRNAs were mainly enriched in cancer-related biology, such as regulation of transcription, signal transduction, and cell cycle. The present study provides novel insight for better understanding of circRNA-related ceRNA network in CC and facilitates the identification of potential biomarkers for prognosis.

Keywords:
Circular RNAs; competing endogenous RNAs; miRNAs; least absolute shrinkage and selection operator; prognostic signature