Microarray is an efficient tool for circRNA profiling

• Published in: Briefings in bioinformatics Vol. 20; no. 4; pp. 1420 - 1433
• Main Authors: Li, Shasha, Teng, Shuaishuai, Xu, Junquan, Su, Guannan, Zhang, Yu, Zhao, Jianqing, Zhang, Suwei, Wang, Haiyan, Qin, Wenyan, Lu, Zhi John, Guo, Yong, Zhu, Qianyong, Wang, Dong
• Format: Journal Article
• Language: English
• Published: England Oxford University Press 19.07.2019; Oxford Publishing Limited (England)
• Subjects: Bioinformatics; Brain - metabolism; Cervical cancer; Circular RNA; Computational Biology; Databases, Nucleic Acid - statistics & numerical data; Datasets; DNA probes; Female; Gene Expression Profiling - methods; Gene Expression Profiling - statistics & numerical data; Gene sequencing; Genomes; Humans; Neoplasms - blood; Neoplasms - genetics; Neoplasms - metabolism; Oligonucleotide Array Sequence Analysis - methods; Oligonucleotide Array Sequence Analysis - statistics & numerical data; Polymerase chain reaction; Reverse transcription; RNA, Circular - blood; RNA, Circular - genetics; RNA, Circular - metabolism; RNA-Seq - methods; RNA-Seq - statistics & numerical data; Tissue Distribution; Tumors; Uterine Cervical Neoplasms - blood; Uterine Cervical Neoplasms - genetics; Uterine Cervical Neoplasms - metabolism; circRNAs; circRNA microarray; cervical cancer; plasma circRNA; noninvasively biomarker
• ISSN: 1467-5463; 1477-4054; 1477-4054
• DOI: 10.1093/bib/bby006

Abstract Circular RNAs (circRNAs) are emerging as a new class of endogenous and regulatory noncoding RNAs in latest years. With the widespread application of RNA sequencing (RNA-seq) technology and bioinformatics prediction, large numbers of circRNAs have been identified. However, at present, we lack a comprehensive characterization of all these circRNAs in interested samples. In this study, we integrated 87 935 circRNAs sequences that cover most of circRNAs identified till now represented in circBase to design microarray probes targeting back-splice site of each circRNA to profile expression of those circRNAs. By comparing the circRNA detection efficiency of RNA-seq with this circRNA microarray, we revealed that microarray is more efficient than RNA-seq for circRNA profiling. Then, we found ∼80 000 circRNAs were expressed in cervical tumors and matched normal tissues, and ∼25 000 of them were differently expressed. Notably, many of these circRNAs detected by this microarray can be validated by quantitative reverse transcription polymerase chain reaction (RT-qPCR) or RNA-seq. Strikingly, as many as ∼18 000 circRNAs could be robustly detected in cell-free plasma samples, and the expression of ∼2700 of them differed after surgery for tumor removal. Our findings provided a comprehensive and genome-wide characterization of circRNAs in paired normal tissues and tumors and plasma samples from multiple individuals. In addition, we also provide a rich resource with 41 microarray data sets and 10 RNA-seq data sets and strong evidences for circRNA expression in cervical cancer. In conclusion, circRNAs could be efficiently profiled by circRNA microarray to target their reported back-splice sites in interested samples.