Wang Shasha, Zhang Songying
Zhejiang University, School of Medicine, Sir Run Run Shaw Hospital, Department of Obstetrics and Gynecology, Assisted Reproduction Unit, Hangzhou, China.
Genet Mol Biol. 2022 Jun 24;45(2):e20210405. doi: 10.1590/1678-4685-GMB-2021-0405. eCollection 2022.
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.
越来越多的证据揭示了环状RNA（circRNA）作为竞争性内源性RNA（ceRNA）在宫颈癌（CC）生物学中的重要作用。本研究旨在为CC鉴定一种新的circRNA相关预后标志物。从基因表达综合数据库（GEO）数据集中下载CC的表达数据和临床信息，以识别差异circRNA表达。基于靶向miRNA预测，在癌症基因组图谱（TCGA）数据集的训练组和验证组中检测circRNA相关的miRNA，以构建具有最小绝对收缩和选择算子（LASSO）的CC新预后标志物。此外，应用Kaplan-Meier（K-M）分析来检验该模型。在本研究中，在GSE102686和GSE107472中鉴定出三种差异表达的circRNA（hsa_circ_0001498、hsa_circ_0066147和hsa_circ_0006948）。然后，根据标准在TCGA数据集中分析25个预测的miRNA，以计算预后标志物。此外，我们根据它们的表达水平和系数开发了一个六miRNA标志物（hsa-miR-217、hsa-miR-30b-3p、hsa-miR-136-5p、hsa-miR-185-3p、hsa-miR-501-5p和hsa-miR-658）。我们进行了Pearson相关分析，以筛选受这六种miRNA负调控的47个mRNA。功能富集分析表明，这些mRNA主要富集在癌症相关生物学过程中，如转录调控、信号转导和细胞周期。本研究为更好地理解CC中circRNA相关的ceRNA网络提供了新的见解，并有助于识别潜在的预后生物标志物。
