CircNet 2.0: an updated database for exploring circular RNA regulatory networks in cancers

Abstract

Circular RNAs (circRNAs), which are single-stranded RNA molecules that have individually formed into a covalently closed continuous loop, act as sponges of microRNAs to regulate transcription and translation. CircRNAs are important molecules in the field of cancer diagnosis, as growing evidence suggests that they are closely related to pathological cancer features. Therefore, they have high potential for clinical use as novel cancer biomarkers. In this article, we present our updates to CircNet (version 2.0), into which circRNAs from circAtlas and MiOncoCirc, and novel circRNAs from The Cancer Genome Atlas database have been integrated. In total, 2732 samples from 37 types of cancers were integrated into CircNet 2.0 and analyzed using several of the most reliable circRNA detection algorithms. Furthermore, target miRNAs were predicted from the full-length circRNA sequence using three reliable tools (PITA, miRanda and TargetScan). Additionally, 384 897 experimentally verified miRNA-target interactions from miRTarBase were integrated into our database to facilitate the construction of high-quality circRNA-miRNA-gene regulatory networks. These improvements, along with the user-friendly interactive web interface for data presentation, search, and visualization, showcase the updated CircNet database as a powerful, experimentally validated resource, for providing strong data support in the biomedical fields. CircNet 2.0 is currently accessible at https://awi.cuhk.edu.cn/∼CircNet.

Figure 1.

System workflow of CircNet 2.0.

Figure 2.

Highlighted improvements of CircNet 2.0.

Figure 3.

Demonstration of the web interface of CircNet 2.0. We use basic search as an example. After input of the circRNA ID, with one click, CircNet 2.0 will provide the annotation of the circRNA, its expression, its interactions with miRNA, and detailed information of the circRNA–miRNA–gene regulatory network. Users can also click on any node of the network to browse and zoom-in on it.

Figure 4.

Case study of the hsa_circ_0001955 regulatory network. (A) Using ‘Search by CircBase ID,’ hsa_circ_0001955 is converted to the CircNet ID chr15:64203082|64216713. The circRNA annotation is shown after one click. (B) In the network page of chr15:64203082|64216713, we can see that TargetScan has predicted the target of the circRNA to be has-miR-145-5p, which corresponds to the result of previous studies. We can find that all three target genes are proven as the interaction targets of has-miR-145-5p in our network.