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

Author:

Chen Yigang123,Yao Lantian34,Tang Yun23,Jhong Jhih-Hua3ORCID,Wan Jingting2,Chang Jingyue2,Cui Shidong23,Luo Yijun2,Cai Xiaoxuan23,Li Wenshuo34,Chen Qi3,Huang Hsi-Yuan123,Wang Zhuo3ORCID,Chen Weiming5,Chang Tzu-Hao6,Wei Fengxiang178,Lee Tzong-Yi23ORCID,Huang Hsien-Da123ORCID

Affiliation:

1. The Genetics Laboratory, Longgang District Maternity & Child Healthcare Hospital of Shenzhen City, Shenzhen, Guangdong Province 518172, China

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

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

4. School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen 518172, China

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

6. Graduate Institute of Biomedical Informatics, Taipei Medical University, Taipei 110, Taiwan

7. Department of Cell Biology, Jiamusi University, Jiamusi, Heilongjiang Province 154007, China

8. Shenzhen Children's Hospital of China Medical University, Shenzhen, Guangdong Province 518172, China

Abstract

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.

Funder

National Natural Science Foundation of China

Key Program of Guangdong Basic and Applied Basic Research Fund

Shenzhen City and Longgang District for the Warshel Institute for Computational Biology

Guangdong Young Scholar Development Fund

Science, Technology and Innovation Commission of Shenzhen Municipality

Guangdong Province Basic and Applied Basic Research Fund

Ganghong Young Scholar Development Fund

Shenzhen Science and Technology

Publisher

Oxford University Press (OUP)

Subject

Genetics

Reference42 articles.

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