甲状腺乳头状癌相关ceRNA网络的生物信息学分析

Bioinformatics analysis of the competing endogenous RNA network associated with papillary thyroid carcinoma

目的 近些年由于高通量测序的发展,针对环状RNA(circRNA)的研究越来越多,但仍有大量的环状RNA有待探索,本文通过对甲状腺乳头状癌(PTC)中竞争性内源性RNA(ceRNA)的研究,构建circRNA-miRNA-mRNA的ceRNA网络,探索PTC中环状RNA潜在的调控机制,为临床寻找新的标志物或治疗靶点提供新的思路和见解。方法 整合GEO和TCGA两大数据库,下载与PTC相关的芯片及临床数据,运用生物信息学方法构建与生存相关的ceRNA网络机制。结果 两大数据库中共鉴定出16个环状RNA、199个miRNA和4 308个mRNA的差异表达基因,通过Cancer-Specific CircRNA Database预测环状RNA可能结合的miRNA并绘制韦恩图,将得到11个差异表达的miRNA通过TargetScan、miRDB两大数据库预测mRNA靶基因并整合,共得到751个差异表达的mRNA,通过三者之间作用关系整合成ceRNA网络并进行可视化,利用STRING网站工具构建蛋白质相互作用网络,计算并筛选出其中密度最高、接近中心性的前10个枢纽基因,GO和KEGG通路富集分析表明枢纽基因与某些癌症相关的生物学功能和途径密切相关。使用R语言中的生存包分析枢纽基因的预后意义,发现KCNA1和NLGN1与PTC患者的生存显著相关。结论 本研究从PTC相关的环状RNA角度出发,探讨其通过ceRNA网络可能参与PTC的发生与发展过程,进一步加深对PTC发病机制和治疗的认识。

Objective In recent years,due to the development of high-throughput sequencing,there are more and more studies on circular RNA(circRNA),but there are still a large number of circular RNA to be explored.In this paper,through the study of competitive endogenous RNA(ceRNA)in papillary thyroid carcinoma(PTC),we construct the ceRNA network of circRNA-miRNA-mRNA,explore the potential regulatory mechanism of circular RNA in PTC,and provide new ideas and insights for the clinical search of new markers or therapeutic targets.Methods Integrating the two major databases of GEO and TCGA,downloading the microarrays and clinical data related to PTC,and using bioinformatics methods to construct ceRNA network mechanisms related to survival analysis.Results A total of 16 differentially expressed genes of circular RNA,199 miRNA and 4308 mRNA were identified in the two databases.The possible binding miRNA of circular RNA was predicted by Cancer-Specific CircRNA Database and Wayne diagram was drawn.Eleven differentially expressed miRNA were predicted and integrated into mRNA target genes by TargetScan and miRDB databases.A total of 751 differentially expressed mRNA were obtained,which were integrated into ceRNA network and visualized by the interaction between the three.The protein interaction network was constructed by using the STRING website tool,and the top 10 hub genes with the highest density and closest to centrality were calculated and screened.GO and KEGG pathway enrichment analysis showed that hub genes were closely related to some cancer-related biological functions and pathways.Using the survival package in the R language to analyze the prognostic significance of hub genes,it was found that KCNA1 and NLGN1 were significantly associated with survival in patients with PTC.Conclusion In this study,we explored the potential involvement of circRNA in the development and progression of PTC through the ceRNA network.Our findings deepen our understanding of the pathogenesis and treatment of PTC from the perspective of circRNA.