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. 2021 Nov 26;50(D1):D93–D101. doi: 10.1093/nar/gkab1036

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

Yigang Chen 1,2,3,4, Lantian Yao 4,5,4, Yun Tang 6,7, Jhih-Hua Jhong 8, Jingting Wan 9, Jingyue Chang 10, Shidong Cui 11,12, Yijun Luo 13, Xiaoxuan Cai 14,15, Wenshuo Li 16,17, Qi Chen 18, Hsi-Yuan Huang 19,20,21, Zhuo Wang 22, Weiming Chen 23, Tzu-Hao Chang 24, Fengxiang Wei 25,26,27,✉, Tzong-Yi Lee 28,29,✉, Hsien-Da Huang 30,31,32,✉
PMCID: PMC8728223  PMID: 34850139

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.

INTRODUCTION

Circular RNAs (circRNAs) are single-stranded RNA molecules that have individually formed into a covalently closed continuous loop. Compared with linear RNAs, circRNAs do not have 5′ and 3′ ends and are therefore resistant to exonuclease-mediated degradation, making them highly stable molecules (1). Studies have indicated that circRNAs act as regulators of gene expression by ‘sponging’ microRNAs (miRNAs) and RNA-binding proteins, and thereby influencing transcription and translation processes (2). CircRNAs have been shown to play a significant role in the diagnosis of various diseases such as Alzheimer's disease, osteoporosis, osteoarthritis, and cardiovascular diseases (3–6). Several studies have focused on the association of circRNAs with the pathological and clinical aspects of malignant diseases and highlighted their roles in oncogenic pathways, demonstrating their potential application as diagnostic biomarkers of cancer (7–9). The high abundance and stability of circRNAs and their tissue-specific expression frameworks and wide distribution in body vessels render them as promising targets for drug development (5,7,10,11).

Owing to the significance of circRNAs in eukaryotic cells in various biological and clinical aspects, the first version of CircNet database was published in January 2016, focusing on tissue-specific expression profiles and regulatory networks among circRNAs, miRNAs and genes (12). Since then, the database has accumulated data from over 200 research studies that have referred to CircNet annotations, especially those on regulatory networks of circRNAs in a wide array of cancers. For the updated version 2.0, we processed raw RNA-Seq data of six cancer types from The Cancer Genome Atlas (TCGA) database to detect novel circRNAs. To the best of our knowledge, CircNet 2.0 is the first database that includes circRNAs detected from TCGA samples. CircNet 2.0 also contains circRNA data from several online resources, such as circAtlas, a database that contains a collection of expression patterns and comprehensive annotations of highly reliable circRNAs derived from >1000 RNA-Seq samples (13). Another resource, MiOncoCirc, provides circRNA profiles that have been directly detected and captured in over 1000 human cancer tissue samples, using the exome capture RNA-Seq protocol (14). Through this update, CircNet 2.0 now encompasses over 2732 samples from 37 types of human cancers, providing a rich resource for cancer exploration and biological research.

To ensure the accuracy of the circRNAs obtained and the precise construction of the circRNA–miRNA–gene networks, the circRNA data were analyzed using a series of reliable bioinformatics algorithms for their identification and annotation. CIRI2 uses an adapted maximum-likelihood estimation based on multiple seed matching to accurately identify back-spliced circRNA reads and reduce false-positive estimations, achieving high sensitivity and reliability (15,16). The CIRCexplorer2 pipeline, upgraded from the original CIRCexplorer, enables the annotation of alternative back-splicing and splicing events in circRNAs and can reveal novel back-spliced or spliced exons with the de novo transcript assembly (17). We also applied circRNA detection tools such as Find_circ and DCC, which integrate a series of filters and data across replicate sets to harvest a list of precise circRNA candidates (18,19). By combining these algorithms in CircNet 2.0, we have enhanced this circRNA processing and data-incorporating pipeline. To build a reliable circRNA-miRNA-gene regulatory network, we annotated the circRNA data with full-length sequences by CIRI-full and then used PITA, miRanda and TargetScan to predict the miRNA interactions (20–23). Additionally, we integrated 384,897 experimentally verified miRNA–target interactions (MTIs) from miRTarBase, one of the most comprehensively annotated and experimentally validated MTI databases. miRTarBase provides comprehensive information on upstream and downstream regulatory targets of miRNAs, and its latest version contains more than 13,404 validated MTIs from 11 021 manually curated articles (24).

In the years since the publication of CircNet 1.0, the field of circRNA research has flourished. Many researchers have focused on circRNA-miRNA-gene regulatory networks in cancer research, and circRNAs are now considered as potential biomarkers of malignant diseases. The updated CircNet 2.0 aims to provide a more comprehensive resource of circRNA data derived from thousands of cancer samples and to allow the detection of novel circRNA from raw cancer data using reliable state-of-the-art algorithms. Furthermore, we have developed a novel pipeline for the construction of circRNA–miRNA-gene networks of high quality. We also built a more user-friendly web interface with comprehensive functionalities, to enhance user experience of the database.

DATA COLLECTION AND PROCESSING

Compared with CircNet 1.0, CircNet 2.0 has both novel and a larger number of integrated and processed data as well as a brand new network construction pipeline, as shown in Figure 1. In CircNet 2.0, we have integrated high-quality cancer-related circRNAs of the human species with their basic annotations and circRNA–miRNA interaction networks from circAtlas. We also integrated all the circRNAs registered on MiOncoCirc along with their basic annotation and expression levels. To ensure data integrity, we reprocessed a number of circRNAs that were missing parts of their annotation information and filtered out all irreparable data. All integrated samples can be accessed through the Gene Expression Omnibus (GEO) database (25). Basic information on all the integrated circRNAs was obtained using one of the following four bioinformatics algorithms: CIRI2, CIRCexplorer2, find_circ, and DCC. All circRNA–miRNA interactions were predicted using three prediction tools: PITA, miRanda and TargetScan (20–22). To enrich the circRNA information in the cancer area, we also collected the controlled raw RNA-Seq data from TCGA. Although we had accumulated over 100 TB of sequencing data from 37 cancer types registered on TGCA, the limitation of computing resources at our institute allowed us to process only the top six most important cancer types for finding novel related circRNAs; namely, breast cancer (breast invasive carcinoma), lung cancers (lung adenocarcinoma, lung squamous cell carcinoma), colon and rectal cancers (colon adenocarcinoma, rectum adenocarcinoma), and leukemia (acute myeloid leukemia). After obtaining basic information along with the full-length sequences of circRNAs in each sample, we predicted the circRNA–miRNA interactions using the three tools mentioned above. Suitable energy cut-off was set for each tool to filter the high quality circRNA–miRNA interactions prediction results. The circRNA–miRNA interactions were clustered into three groups, respectively correspond to the number of tools successfully predicted such interaction. Overall, we integrated 289 303 circRNAs from 2732 cancer samples covering 37 types of cancers into our database. For each circRNA, details on its genomic position, strand, host gene, full-length sequence, expression counts and interactions with miRNAs are provided. The circRNA nomenclature in CircNet 2.0 follows a similar rule to that of MiOncoCirc and Cancer-Specific CircRNA Database 2.0 (26), where the human genome assembly GRCh38 (hg38) position of the circRNA is used directly as its name and identity (ID).

Figure 1.

Figure 1.

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System workflow of CircNet 2.0.

To facilitate the construction of circRNA–miRNA–gene regulatory networks of high quality, we have integrated three databases of miRNAs and genes into CircNet 2.0. The latest release of miRBase (v22) contains miRNA sequences from 271 organisms, 38 589 hairpin precursors and 48 860 mature miRNAs (27). We incorporated all human mature miRNAs from miRBase (v22) into CircNet 2.0. Finally, from the HUGO Gene Nomenclature Committee (HGNC) guidelines for naming protein-coding genes and different classes of RNA genes and pseudogenes in humans (28), we integrated all HGNC gene symbols with annotations into our database. To facilitate the construction of the interaction networks between miRNAs and genes, 384 897 experimentally verified MTIs from miRTarBase were also integrated (24). All the tools included in the CircNet 2.0 data processing pipeline were listed in Supplementary Table S1.

To further enrich the functionalities of CircNet 2.0, we integrated other databases containing information about circRNAs, biological pathways, and diseases and developed several bioinformatics tools for user convenience. circBase is a database that merges and unifies datasets of circRNAs, from which the evidence supporting their expression can be accessed, downloaded, and browsed within the genomic context (29). As circBase ID is widely used in circRNA research, we integrated the database into CircNet 2.0 and developed a function that automatically converts the circBase ID into the CircNet ID, thereby allowing users to search for any circRNA on CircNet 2.0 by inputting its circBase ID. We also developed similar ID conversion function for circAtlas. Gene Ontology (GO) (30) and Kyoto Encyclopedia of Genes and Genomes (KEGG) (31) are well-known enrichment analysis databases that focus on providing functional interpretations of genes and genomes at both the molecular and higher levels. Additionally, DisGeNET is a discovery platform containing one of the largest publicly available collections of genes and variants associated with human diseases (32). On the basis of the collective data from GO, KEGG and DisGeNET that were also integrated into CircNet 2.0, we built a functional tool for the construction of circRNA-miRNA-gene interaction networks for disease analysis in our system. Lastly, we also integrated miRNA expression information of different TCGA cancers from UCSC Xena (33). The amalgamation of these circRNA expression and miRNA expression data allowed for the corroboration of circRNA–miRNA interactions in the network. Supplementary Table S2 summarized all the databases included in CircNet 2.0.

RESULTS

Data statistics and database content of CircNet 2.0

Of the 289 303 circRNAs recorded on CircNet 2.0 (Table 1), 126 491 are related to breast cancer, accounting for approximately half of the updated data. CircRNAs were detected in 2732 samples downloaded from TCGA, circAtlas, MiOncoCirc, and GEO. The detail information of sample id was available in Supplementary Table S3. We also integrated 2656 miRNAs from 8239 samples that covered 37 different types of cancer, 26 of which were included in TCGA projects. One in five samples for circRNA discovery and 1 in 10 samples for miRNA integration were of breast cancers. CircRNAs from prostate cancer, lymphoma, myeloma, sarcoma, and lung adenocarcinoma samples were also processed to enrich our database for wider applications and utility in cancer research. Furthermore, Supplementary Table S4 shown the distribution of circRNAs on chromosomes.

Table 1.

Data statistics of CircNet 2.0

Abbrev cancer name Cancer type Sample sources Number of circRNAs TCGA included Number of samples Number of miRNAs miRNA sample counts
BRCA Breast cancer circAtlas, MiOncoCirc, GEO, TCGA 126 491 yes 429 2238 832
PRAD Prostate cancer circAtlas, MiOncoCirc, GEO 80 744 yes 341 2111 544
LAML Acute leukimia lyphoma circAtlas, MiOncoCirc, GEO, TCGA 73 320 yes 194 1834 188
DLBC Diffuse large B cell lymphoma circAtlas, MiOncoCirc, GEO 70 255 yes 124 1883 47
MM Multiple myeloma circAtlas, MiOncoCirc, GEO 68 019 no 212 NA NA
SARC Sarcoma circAtlas, MiOncoCirc, GEO 67 392 yes 167 2093 260
MISC Miscellaneous circAtlas, MiOncoCirc, GEO 65 707 no 126 NA NA
MPN Myeloproliferative neoplasms circAtlas, MiOncoCirc, GEO 60 037 no 151 NA NA
LUAD Lung adenocarcinoma circAtlas, MiOncoCirc, GEO, TCGA 46 502 yes 168 2228 495
CHOL Bile duct cancer circAtlas, MiOncoCirc, GEO 45 601 yes 63 1779 45
SECR Adenoid cystic carcinoma circAtlas, MiOncoCirc, GEO 44 390 no 44 NA NA
PAAD Pancreatic cancer circAtlas, MiOncoCirc, GEO 40 841 yes 60 2050 182
BLCA Bladder urothelial carcinoma circAtlas, MiOncoCirc, GEO 40 698 yes 49 2210 429
HNSC Head and neck squamous cell carcinoma circAtlas, MiOncoCirc, GEO 36 504 yes 49 2246 529
LIHC Liver cancer circAtlas, MiOncoCirc, GEO 36 020 yes 21 2172 420
LUNG Lung cancer circAtlas, MiOncoCirc, GEO 33 303 no 42 NA NA
LUSC Lung squamous cell carcinoma circAtlas, MiOncoCirc, GEO, TCGA 32 884 yes 125 2213 380
READ Rectal cancer circAtlas, MiOncoCirc, GEO, TCGA 32 431 yes 103 2003 92
ESCA Esophageal cancer circAtlas, MiOncoCirc, GEO 32 345 yes 19 2092 195
GBM Glioblastoma circAtlas, MiOncoCirc, GEO 32 158 yes 26 1388 5
KDNY Kidney cancer circAtlas, MiOncoCirc, GEO 29 474 no 23 NA NA
STAD Stomach cancer circAtlas, MiOncoCirc, GEO 29 125 yes 27 2178 428
SKCM Melanoma circAtlas, MiOncoCirc, GEO 26 869 yes 31 2220 452
ACC Adrenocortical cancer circAtlas, MiOncoCirc, GEO 26 839 yes 24 1952 79
COLO Squamous cell carcinoma circAtlas, MiOncoCirc, GEO 25 983 no 29 NA NA
NRBL Neuroblastoma circAtlas, MiOncoCirc, GEO 25 939 no 13 NA NA
OV Ovarian cancer circAtlas, MiOncoCirc, GEO 22 922 yes 20 2165 485
MBL Medulloblastoma circAtlas, MiOncoCirc, GEO 22 298 no 7 NA NA
THCA Thryoid cancer circAtlas, MiOncoCirc, GEO 20 298 yes 13 2,217 569
RHABDO Rhabdomyosarcoma circAtlas, MiOncoCirc, GEO 19 899 no 10 NA NA
TGCT Testicular cancer circAtlas, MiOncoCirc, GEO 13 377 yes 4 2212 155
LYMP Lymphoma circAtlas, MiOncoCirc, GEO 11 765 no 3 NA NA
MESO Mesothelioma circAtlas, MiOncoCirc, GEO 8140 yes 3 1964 87
THYM Thymoma circAtlas, MiOncoCirc, GEO 5918 yes 2 2128 126
UCEC Uterine carcinosarcoma circAtlas, MiOncoCirc, GEO 5532 yes 2 2238 430
LGG Lower grade glioma circAtlas, MiOncoCirc, GEO 5414 yes 2 2157 524
COAD Colon cancer circAtlas, MiOncoCirc, GEO, TCGA 1487 yes 6 2113 261
Total 289 303 2732 2656 8239
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For CircNet 2.0, we focused on updating circRNAs in cancers and their interactions with miRNAs. Table 2 presents the improvements and updated content of CircNet 2.0 in comparison with CircNet 1.0 and other circRNA databases. As indicated in Table 2, CircNet 2.0 provides expression profiles and interaction information of circRNAs, miRNAs, and genes across 37 human cancers, including breast cancer, prostate adenocarcinoma, and lymphoma. The number of processed samples has been significantly increased from 464 to 2732. Full-length circRNA sequences were annotated, allowing for a more precise prediction of circRNA–miRNA interactions. As mentioned above, miRTarBase was included to provide high-quality miRNA–gene interactions. Therefore, this update can facilitate the construction of comprehensive circRNA–miRNA–gene regulatory networks of high quality. Furthermore, CircNet 2.0 not only provides the genomic annotation and expression profiles of circRNAs but also displays the genomic information and expression patterns of miRNAs in different TCGA cancer types.

Table 2.

Comparison of CircNet 2.0 with other circular RNA databases

MiOncoCirc circAtlas 2.0 CSCD2.0 CircNet 1.0 CircNet 2.0
Publication Cell (2019) Genome Biol. (2020) NAR (2021) NAR Database Issue (2016) Submitted
Last update 2018 2019 2021 2015 2021
Support species Homo sapiens 6 species Homo sapiens Homo sapiens Homo sapiens
Number of cancer types 19 0 23 0 37
Number of samples 2000 + samples 1070 samples 1113 cancer samples 464 samples 2732 cancer samples
Data sources GEO SRA, NGDC, GeneBank ENCODE, SRA GEO TCGA, GEO
Circular RNA detection methods CIRCexplorer, CrossMap CIRI2/CIRI-full, CIRCexplor2, Find_circ, DCC CIRI2, circRNA_finder, find_circ, circexplorer2 Memczak's algorithm CIRI2/CIRI-full, CIRCexplor2, Find_circ, DCC
circRNA-miRNA interaction no yes yes yes yes
miRNA-gene interaction no no no yes yes
circRNA expression yes yes yes yes yes
microRNA expression no no yes no yes
other characteristics - Conserved score, circRNA-miRNA network, circRNA annotation about full-length sequence and ORF. CircRNA annotation about full-length sequence and ORF. Expression profiles of circRNA isoforms CircRNA annotation about full-length sequence and ORF, circRNA-miRNA-Gene network, GO analysis, KEGG analysis, Disease enrichment analysis.
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Construction of circRNA–miRNA–gene regulatory networks

CircNet 2.0 facilitates the construction of novel circRNA–miRNA–gene regulatory networks. The interactions between circRNAs and miRNAs were initially predicted using miRanda, TargetScan, and PITA based on the atomic principles of miRNA interactions and popular bioinformatics algorithms. Then, 384 897 experimentally validated MTIs between miRNAs and mRNAs from miRTarBase were incorporated into CircNet 2.0, making circRNA–miRNA–gene regulatory networks of high quality available. With miRNAs as intermediates, circRNAs could be connected with genes, which proved to be a powerful tool for the daily discovery of novel potential drug targets for replacing known drug targets with obvious side effects or for solving the problems of undruggable targets. The regulatory networks among circRNAs, miRNAs and genes may draw the attention of researchers to pathways that have been overlooked. For example, Zhang et al. (34) discovered that circTRIM33-12 upregulated ten-eleven translocation 1 (TET1) expression by sponging miR-191, leading to a significant decrease in 5-hydroxymethylcytosine and DNA methylation levels in hepatocellular carcinoma (HCC). Their findings provided new insight into the role of circRNAs in HCC progression.

Comprehensive web functionalities of CircNet 2.0

The highlights of the CircNet 2.0 functionalities are shown in Figure 2. Users can browse circRNA–miRNA–gene regulatory networks by clicking on any node of the visualized network. To further illustrate the circRNA–miRNA interactions, CircNet 2.0 now contains the expression profiles of both circRNAs and target miRNAs that act as indicators of their interactions in different cancer categories. For example, for this update, the potencies of circRNAs in encoding proteins were calculated through the identification of their open reading frames (ORFs). Additionally, this update also integrated the RNA-binding proteins of circRNAs predicted by circAtlas, which can enhance the regulatory network. Furthermore, CircNet 2.0 can provide a one-click functional enrichment analysis of the genes interacting with a specific circRNA, using GO, KEGG and related disease enrichment analysis tools. Such analyses of gene functions are important for the interpretation of biological data and have become an indispensable routine in clinical research. The data capacity in CircNet 2.0 for allowing the cancer-specific analysis of cancer-related circRNAs would greatly support disease enrichment analyses. Working as an indicator of disease pathology, it would help to shed light on the correlations between circRNA expression patterns and the oncology of various cancer types and facilitate the discovery of new cancer drug targets.

Figure 2.

Figure 2.

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Highlighted improvements of CircNet 2.0.

Enhanced user interface of CircNet 2.0

As shown in Figure 3, we updated the web interface to provide a user-friendly experience in data presentation, search, and visualization. CircNet 2.0 allows users to browse the network by simply searching for the circRNA, miRNA or gene symbol of interest. Given the lack of a common nomination rule of circRNAs, CircNet 2.0 also provides a BLAST search to allow users to match potential circRNAs to the data in our database by sequence similarity (Supplementary Figure S1). Once successfully matched, users can obtain not only the sequence and expression profiles of circRNAs and related miRNAs in certain types of cancers but also their ORFs, RNA-binding protein information, and potential target miRNAs and affected mRNAs. Additionally, CircNet 2.0 allows users to search for significantly expressed cancer-related circRNAs for cancer-specific analysis, which would be of great convenience to researchers and supply them with a vast amount of reliable information for their target screening and exploration. The interface also allows users to search the database by circBase ID, which will be converted to the CircNet ID, thereby improving the efficiency of data browsing. Additionally, CircNet 2.0 facilitates the search of circRNAs via a search of their host gene (Supplementary Figure S2). By simply clicking on the details of a circRNA, CircNet 2.0 provides its functional annotation and expression pattern, its interactions with miRNAs, and comprehensive information of the circRNA–miRNA–gene regulatory network. Users can also click on any node of the visualized network to browse and zoom-in on it. Overall, CircNet 2.0 provides a practical means for the analysis of circRNA regulation processes.

Figure 3.

Figure 3.

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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.

DISCUSSION AND CONCLUSION

The discovery of circRNAs and their potential targets in diseases has become an important research direction, and published studies have provided promising strategies for the development of novel therapeutic methods. For example, Du et al. (35) reported that circ-Dnmt1 overexpression in breast cancer cells increased their proliferation and survival as mediated through the activation of autophagy by the interaction of the circRNA with p53 and AUF1. The silencing of circ-Dnmt1 reversed these processes. According to Wang et al. (36), the sponging of miR-442a by overexpressed circNT5E promoted the formation of glioblastoma (GBM) in vivo, with the circRNA affecting multiple biological functions of the cancerous cells, including their proliferation, apoptosis, and metastasis. CircRNAs also play important roles in the tumor microenvironment (TME), where they are involved in the activities of various types of cells, such as cancer-associated epithelial cells, immune cells (e.g. tumor-associated macrophages, dendritic cells, and mast cells), and cancer stem cells (37). Zhan et al. (38) provided the first evidence for differentially expressed circRNA patterns in macrophages of various polarization states, which shed light on the role of these RNAs in the oncogenesis of cancers due to changes in the TME. Furthermore, circRNAs have been identified as novel targets for reversing drug resistance in various cancer types. For example, it was confirmed that circPAN3 can be used as a target for reversing adriamycin resistance in patients with acute myeloid leukemia through the miR-153-5p/miR-183-5p-X-linked inhibitor of apoptosis protein (XIAP) axis (35,39). The CircNet 2.0 database will facilitate the acquisition of knowledge about such circRNA-associated biological processes and the discovery of potential biomarkers by helping researchers to explore circRNA annotations and circRNA–miRNA–gene regulatory networks.

To ensure the reliability and accuracy of the database contents provided to users, the analyzed data were confirmed with the findings detailed in existing research articles on circRNA–miRNA–gene regulatory networks in cancers. In 2020, Ding et al. (40) proposed that the circRNA hsa_circ_0001955 had the potential to regulate the miRNA hsa-miR-145-5p in colorectal cancer (CRC), where the genes encoding cell division protein kinase 6 (CDK6), matrix metalloproteinase-12 (MMP12), and Ras-related protein Rab-3A interacting protein (RAB3IP) acted as potential downstream targets. The circRNA dataset of 10 patients with CRC was extracted from the GEO database, and differential expression analysis was performed using the GEO2R tool provided by that database to identify differentially expressed circRNAs. Using data from the cancer-specific circRNA database, the differentially expressed circRNAs chosen were confirmed to have miRNA response elements, indicating that the circRNAs could act as miRNA sponges in CRC. Specific miRNA prediction and downstream gene identification were performed using the starBase database (41), whereupon it was found that there was a significant regulatory connection among hsa_circ_0001955, hsa-miR-145-5p, and CDK6/MMP12/RAB3IP. We conducted the same procedure in CircNet 2.0 and searched for hsa_circ_0001955 after converting its ID to chr15:64203082|64216713 in the database, and the circRNA was predicted to interact with dozens of miRNAs, including the TargetScan-predicted hsa-miR-145-5p. As presented in Figure 4, CircNet 2.0 can provide these experimentally verified circRNA regulatory networks, which are consistent with the findings of Ding et al. (42) and other studies, validating the reliability of the database content. An additional case study of the circ_0004463/miR-380-3p/FOXO1 axis is presented in Supplementary Figure S3.

Figure 4.

Figure 4.

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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.

In summary, we have not only integrated data from existing circRNA databases into CircNet 2.0 but also detected novel circRNAs from the sequencing data of the GEO and TCGA databases. The application of multiple circRNA prediction tools ensured the accuracy of the data contained in CircNet 2.0. We also designed a more user-friendly web interface to aid researchers in searching and browsing circRNAs of interest conveniently. In conclusion, CircNet 2.0 provides a practical and user-friendly platform on which researchers can explore novel cancer biomarkers and circRNAs related to the pathogenesis, diagnosis, and therapy of malignant and other diseases.

DATA AVAILABILITY

The CircNet 2.0 database will be continuously maintained and updated. The database is now publicly accessible at https://awi.cuhk.edu.cn/∼CircNet.

Supplementary Material

gkab1036_Supplemental_File
Click here for additional data file. (904.2KB, pdf)

ACKNOWLEDGEMENTS

The authors sincerely appreciate the Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen for financially supporting this research. The results shown here are in whole or part based upon data generated by the TCGA Research Network: https://www.cancer.gov/tcga.

Contributor Information

Yigang Chen, The Genetics Laboratory, Longgang District Maternity & Child Healthcare Hospital of Shenzhen City, Shenzhen, Guangdong Province 518172, China; School of Life and Health Sciences, The Chinese University of Hong Kong, Shenzhen 518172, China; Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen 518172, China.

Lantian Yao, Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen 518172, China; School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen 518172, China.

Yun Tang, School of Life and Health Sciences, The Chinese University of Hong Kong, Shenzhen 518172, China; Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen 518172, China.

Jhih-Hua Jhong, Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen 518172, China.

Jingting Wan, School of Life and Health Sciences, The Chinese University of Hong Kong, Shenzhen 518172, China.

Jingyue Chang, School of Life and Health Sciences, The Chinese University of Hong Kong, Shenzhen 518172, China.

Shidong Cui, School of Life and Health Sciences, The Chinese University of Hong Kong, Shenzhen 518172, China; Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen 518172, China.

Yijun Luo, School of Life and Health Sciences, The Chinese University of Hong Kong, Shenzhen 518172, China.

Xiaoxuan Cai, School of Life and Health Sciences, The Chinese University of Hong Kong, Shenzhen 518172, China; Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen 518172, China.

Wenshuo Li, Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen 518172, China; School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen 518172, China.

Qi Chen, Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen 518172, China.

Hsi-Yuan Huang, The Genetics Laboratory, Longgang District Maternity & Child Healthcare Hospital of Shenzhen City, Shenzhen, Guangdong Province 518172, China; School of Life and Health Sciences, The Chinese University of Hong Kong, Shenzhen 518172, China; Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen 518172, China.

Zhuo Wang, Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen 518172, China.

Weiming Chen, School of Data Science, The Chinese University of Hong Kong, Shenzhen 518172, China.

Tzu-Hao Chang, Graduate Institute of Biomedical Informatics, Taipei Medical University, Taipei 110, Taiwan.

Fengxiang Wei, The Genetics Laboratory, Longgang District Maternity & Child Healthcare Hospital of Shenzhen City, Shenzhen, Guangdong Province 518172, China; Department of Cell Biology, Jiamusi University, Jiamusi, Heilongjiang Province 154007, China; Shenzhen Children's Hospital of China Medical University, Shenzhen, Guangdong Province 518172, China.

Tzong-Yi Lee, School of Life and Health Sciences, The Chinese University of Hong Kong, Shenzhen 518172, China; Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen 518172, China.

Hsien-Da Huang, The Genetics Laboratory, Longgang District Maternity & Child Healthcare Hospital of Shenzhen City, Shenzhen, Guangdong Province 518172, China; School of Life and Health Sciences, The Chinese University of Hong Kong, Shenzhen 518172, China; Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen 518172, China.

SUPPLEMENTARY DATA

Supplementary Data are available at NAR Online.

FUNDING

National Natural Science Foundation of China [32070674]; Key Program of Guangdong Basic and Applied Basic Research Fund (Guangdong Shenzhen Joint Fund) [2020B1515120069]; Shenzhen City and Longgang District for the Warshel Institute for Computational Biology; Guangdong Young Scholar Development Fund [2021E0005]; National Natural Science Foundation of China [32070659]; Science, the Technology and Innovation Commission of Shenzhen Municipality [JCYJ20200109150003938]; Guangdong Province Basic and Applied Basic Research Fund [2021A1515012447]; Ganghong Young Scholar Development Fund [2021E007]; Basic research project of Shenzhen Science and Technology Research Projects [JCYJ20190808102405474]. Funding for open access charge: Shenzhen City and Longgang District for the Warshel Institute for Computational Biology.

Conflict of interest statement. None declared.

REFERENCES

  • 1. Jeck W.R., Sorrentino J.A., Wang K., Slevin M.K., Burd C.E., Liu J., Marzluff W.F., Sharpless N.E.. Circular RNAs are abundant, conserved, and associated with ALU repeats. RNA. 2013; 19:141–157. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 2. Panda A.C. Circular RNAs Act as miRNA Sponges. Adv. Exp. Med. Biol. 2018; 1087:67–79. [DOI] [PubMed] [Google Scholar]
  • 3. Altesha M.A., Ni T., Khan A., Liu K., Zheng X.. Circular RNA in cardiovascular disease. J. Cell. Physiol. 2019; 234:5588–5600. [DOI] [PubMed] [Google Scholar]
  • 4. Akhter R. Circular RNA and Alzheimer's disease. Adv. Exp. Med. Biol. 2018; 1087:239–243. [DOI] [PubMed] [Google Scholar]
  • 5. Yang Y., Yujiao W., Fang W., Linhui Y., Ziqi G., Zhichen W., Zirui W., Shengwang W.. The roles of miRNA, lncRNA and circRNA in the development of osteoporosis. Biol. Res. 2020; 53:40. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 6. Zhou Z.B., Huang G.X., Fu Q., Han B., Lu J.J., Chen A.M., Zhu L.. circRNA.33186 contributes to the pathogenesis of osteoarthritis by sponging miR-127-5p. Mol. Ther. 2019; 27:531–541. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 7. Zhang H.D., Jiang L.H., Sun D.W., Hou J.C., Ji Z.L.. CircRNA: a novel type of biomarker for cancer. Breast Cancer. 2018; 25:1–7. [DOI] [PubMed] [Google Scholar]
  • 8. Li P., Chen S., Chen H., Mo X., Li T., Shao Y., Xiao B., Guo J.. Using circular RNA as a novel type of biomarker in the screening of gastric cancer. Clin. Chim. Acta. 2015; 444:132–136. [DOI] [PubMed] [Google Scholar]
  • 9. Verduci L., Strano S., Yarden Y., Blandino G.. The circRNA-microRNA code: emerging implications for cancer diagnosis and treatment. Mol. Oncol. 2019; 13:669–680. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 10. Li R., Jiang J., Shi H., Qian H., Zhang X., Xu W. CircRNA: a rising star in gastric cancer. Cell. Mol. Life Sci.: CMLS. 2020; 77:1661–1680. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 11. Wu Y., Lu X., Shen B., Zeng Y.. The Therapeutic Potential and Role of miRNA, lncRNA, and circRNA in Osteoarthritis. Curr. Gene Ther. 2019; 19:255–263. [DOI] [PubMed] [Google Scholar]
  • 12. Liu Y.C., Li J.R., Sun C.H., Andrews E., Chao R.F., Lin F.M., Weng S.L., Hsu S.D., Huang C.C., Cheng C.et al.. CircNet: a database of circular RNAs derived from transcriptome sequencing data. Nucleic Acids Res. 2016; 44:D209–D215. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 13. Wu W., Ji P., Zhao F.. CircAtlas: an integrated resource of one million highly accurate circular RNAs from 1070 vertebrate transcriptomes. Genome Biol. 2020; 21:101. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 14. Vo J.N., Cieslik M., Zhang Y., Shukla S., Xiao L., Zhang Y., Wu Y.M., Dhanasekaran S.M., Engelke C.G., Cao X.et al.. The landscape of circular RNA in cancer. Cell. 2019; 176:869–881. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 15. Gao Y., Zhang J., Zhao F.. Circular RNA identification based on multiple seed matching. Brief. Bioinform. 2018; 19:803–810. [DOI] [PubMed] [Google Scholar]
  • 16. Gao Y., Wang J., Zhao F.. CIRI: an efficient and unbiased algorithm for de novo circular RNA identification. Genome Biol. 2015; 16:4. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 17. Dong R., Ma X.K., Chen L.L., Yang L.. Genome-wide annotation of circRNAs and their alternative back-splicing/splicing with CIRCexplorer pipeline. Methods Mol. Biol. 2019; 1870:137–149. [DOI] [PubMed] [Google Scholar]
  • 18. Cheng J., Metge F., Dieterich C.. Specific identification and quantification of circular RNAs from sequencing data. Bioinformatics. 2016; 32:1094–1096. [DOI] [PubMed] [Google Scholar]
  • 19. Memczak S., Jens M., Elefsinioti A., Torti F., Krueger J., Rybak A., Maier L., Mackowiak S.D., Gregersen L.H., Munschauer M.et al.. Circular RNAs are a large class of animal RNAs with regulatory potency. Nature. 2013; 495:333–338. [DOI] [PubMed] [Google Scholar]
  • 20. Rogers R., Otal T., Drogin E.Y., Dean B.M.. Effectiveness of the miranda acquiescence questionnaire for investigating impaired miranda reasoning. J. Am. Acad. Psychiatry Law. 2020; 48:226–236. [DOI] [PubMed] [Google Scholar]
  • 21. Kertesz M., Iovino N., Unnerstall U., Gaul U., Segal E.. The role of site accessibility in microRNA target recognition. Nat. Genet. 2007; 39:1278–1284. [DOI] [PubMed] [Google Scholar]
  • 22. Agarwal V., Bell G.W., Nam J.W., Bartel D.P.. Predicting effective microRNA target sites in mammalian mRNAs. Elife. 2015; 4:e05005. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 23. Zheng Y., Ji P., Chen S., Hou L., Zhao F.. Reconstruction of full-length circular RNAs enables isoform-level quantification. Genome Med. 2019; 11:2. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 24. Huang H.Y., Lin Y.C., Li J., Huang K.Y., Shrestha S., Hong H.C., Tang Y., Chen Y.G., Jin C.N., Yu Y.et al.. miRTarBase 2020: updates to the experimentally validated microRNA-target interaction database. Nucleic Acids Res. 2020; 48:D148–D154. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 25. Barrett T., Wilhite S.E., Ledoux P., Evangelista C., Kim I.F., Tomashevsky M., Marshall K.A., Phillippy K.H., Sherman P.M., Holko M.et al.. NCBI GEO: archive for functional genomics data sets–update. Nucleic Acids Res. 2013; 41:D991–D995. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 26. Feng J., Chen W., Dong X., Wang J., Mei X., Deng J., Yang S., Zhuo C., Huang X., Shao L.et al.. CSCD2: an integrated interactional database of cancer-specific circular RNAs. Nucleic Acids Res. 2021; 10.1093/nar/gkab830. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 27. Kozomara A., Birgaoanu M., Griffiths-Jones S.. miRBase: from microRNA sequences to function. Nucleic Acids Res. 2019; 47:D155–D162. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 28. Bruford E.A., Braschi B., Denny P., Jones T.E.M., Seal R.L., Tweedie S.. Guidelines for human gene nomenclature. Nat. Genet. 2020; 52:754–758. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 29. Glažar P., Papavasileiou P., Rajewsky N.. circBase: a database for circular RNAs. RNA. 2014; 20:1666–1670. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 30. Gene Ontology Consortium The Gene Ontology resource: enriching a GOld mine. Nucleic Acids Res. 2021; 49:D325–D334. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 31. Kanehisa M., Furumichi M., Tanabe M., Sato Y., Morishima K.. KEGG: new perspectives on genomes, pathways, diseases and drugs. Nucleic Acids Res. 2017; 45:D353–D361. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 32. Piñero J., Ramírez-Anguita J.M., Saüch-Pitarch J., Ronzano F., Centeno E., Sanz F., Furlong L.I.. The DisGeNET knowledge platform for disease genomics: 2019 update. Nucleic Acids Res. 2020; 48:D845–D855. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 33. Goldman M.J., Craft B., Hastie M., Repečka K., McDade F., Kamath A., Banerjee A., Luo Y., Rogers D., Brooks A.N.et al.. Visualizing and interpreting cancer genomics data via the Xena platform. Nat. Biotechnol. 2020; 38:675–678. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 34. Zhang P.F., Wei C.Y., Huang X.Y., Peng R., Yang X., Lu J.C., Zhang C., Gao C., Cai J.B., Gao P.T.et al.. Circular RNA circTRIM33-12 acts as the sponge of MicroRNA-191 to suppress hepatocellular carcinoma progression. Mol. Cancer. 2019; 18:105. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 35. Du W.W., Yang W., Li X., Awan F.M., Yang Z., Fang L., Lyu J., Li F., Peng C., Krylov S.N.et al.. A circular RNA circ-DNMT1 enhances breast cancer progression by activating autophagy. Oncogene. 2018; 37:5829–5842. [DOI] [PubMed] [Google Scholar]
  • 36. Wang R., Zhang S., Chen X., Li N., Li J., Jia R., Pan Y., Liang H.. CircNT5E acts as a sponge of miR-422a to promote glioblastoma tumorigenesis. Cancer Res. 2018; 78:4812–4825. [DOI] [PubMed] [Google Scholar]
  • 37. Ma Z., Shuai Y., Gao X., Wen X., Ji J.. Circular RNAs in the tumour microenvironment. Mol. Cancer. 2020; 19:8. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 38. Zhang Y., Zhang Y., Li X., Zhang M., Lv K.. Microarray analysis of circular RNA expression patterns in polarized macrophages. Int. J. Mol. Med. 2017; 39:373–379. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 39. Shang J., Chen W.M., Wang Z.H., Wei T.N., Chen Z.Z., Wu W.B.. CircPAN3 mediates drug resistance in acute myeloid leukemia through the miR-153-5p/miR-183-5p-XIAP axis. Exp. Hematol. 2019; 70:42–54. [DOI] [PubMed] [Google Scholar]
  • 40. Ding B., Yao M., Fan W., Lou W.. Whole-transcriptome analysis reveals a potential hsa_circ_0001955/hsa_circ_0000977-mediated miRNA-mRNA regulatory sub-network in colorectal cancer. Aging. 2020; 12:5259–5279. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 41. Li J.H., Liu S., Zhou H., Qu L.H., Yang J.H.. starBase v2.0: decoding miRNA-ceRNA, miRNA-ncRNA and protein-RNA interaction networks from large-scale CLIP-Seq data. Nucleic Acids Res. 2014; 42:D92–D97. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 42. Wu S., Deng H., He H., Xu R., Wang Y., Zhu X., Zhang J., Zeng Q., Zhao X.. The circ_0004463/miR-380-3p/FOXO1 axis modulates mitochondrial respiration and bladder cancer cell apoptosis. Cell Cycle. 2020; 19:3563–3580. [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

gkab1036_Supplemental_File
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Data Availability Statement

The CircNet 2.0 database will be continuously maintained and updated. The database is now publicly accessible at https://awi.cuhk.edu.cn/∼CircNet.

Articles from Nucleic Acids Research are provided here courtesy of Oxford University Press

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