Comprehensive profiling of circRNAs and the tumor suppressor function of circHIPK3 in clear cell renal carcinoma

Li, Haomin; Heng, Baoli; Ouyang, Peng; Xie, Xuexia; Zhang, Tingshun; Chen, Guo; Chen, Zheng; Cheang, Kahong; Lai, Caiyong

doi:10.1007/s10735-020-09882-9

Cited by 17 publications

(14 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…CircBase has been used in several key studies to identify targeted circRNAs, and this identification can be validated by quantitative real-time PCR or downstream analysis. Thus, circBase plays an important role in the identification of potential biomarkers for various cancers [134][135][136][137][138][139][140][141][142][143].…”

Section: Comprehensive Databases Of Circrnasmentioning

confidence: 99%

Disease-Associated Circular RNAs: From Biology to Computational Identification

Tang

Kui

Lu³

et al. 2020

BioMed Research International

View full text Add to dashboard Cite

Circular RNAs (circRNAs) are endogenous RNAs with a covalently closed continuous loop, generated through various backsplicing events of pre-mRNA. An accumulating number of studies have shown that circRNAs are potential biomarkers for major human diseases such as cancer and Alzheimer’s disease. Thus, identification and prediction of human disease-associated circRNAs are of significant importance. To this end, a computational analysis-assisted strategy is indispensable to detect, verify, and quantify circRNAs for downstream applications. In this review, we briefly introduce the biology of circRNAs, including the biogenesis, characteristics, and biological functions. In addition, we outline about 30 recent bioinformatic analysis tools that are publicly available for circRNA study. Principles for applying these computational strategies and considerations will be briefly discussed. Lastly, we give a complete survey on more than 20 key computational databases that are frequently used. To our knowledge, this is the most complete and updated summary on publicly available circRNA resources. In conclusion, this review summarizes key aspects of circRNA biology and outlines key computational strategies that will facilitate the genome-wide identification and prediction of circRNAs.

show abstract

Section: Comprehensive Databases Of Circrnasmentioning

confidence: 99%

Disease-Associated Circular RNAs: From Biology to Computational Identification

Tang

Kui

Lu³

et al. 2020

BioMed Research International

View full text Add to dashboard Cite

show abstract

“…Renal cell carcinoma (RCC) is one of the most common malignant tumors of the urinary system, representing approximately 2% to 3% of all adult malignancies [1,2]. Clear cell renal cell carcinoma (ccRCC) is the most common type of RCC, which accounts for 60% to 85% of all RCC cases [3].…”

Section: Introductionmentioning

confidence: 99%

piRNA-31115 Promotes Cell Proliferation and Invasion via PI3K/AKT Pathway in Clear Cell Renal Carcinoma

Xie

et al. 2021

Disease Markers

Self Cite

View full text Add to dashboard Cite

PIWI-interacting RNAs (piRNAs) are small noncoding RNAs that play important roles in germline development and carcinogenesis. In this study, we used the deep sequencing of small RNA Transcriptome to explore the piRNA expression in six clear cell renal carcinoma (ccRCC) tissues and matched adjacent normal tissues and found that six piRNAs were upregulated and sixteen were downregulated in ccRCC tissues. Among them, piRNA-31115 (NCBI accession number: DQ571003) was the most upregulated piRNA in ccRCC tissues compared with matched adjacent normal tissues. Quantitative real-time PCR (qRT-PCR) was used to confirm piR-31115 expression in other ccRCC tissues ( n = 40 ) and ccRCC cell lines. Besides, function analysis demonstrated that silencing of piR-31115 inhibited ccRCC cell proliferation, motility, and invasiveness. Mechanistic investigations showed that piRNA-31115 may activate epithelial-mesenchymal transition (EMT) via the PI3K/AKT signaling pathway. Hence, piR-31115 may represent an oncogene in the development of ccRCC.

show abstract

“…As shown in Table 6, the predicted results contain five experimental verified associations among the top 10 ranked candidate circRNAs. For example, Li et al discovered that overexpression of circHIPK3 (top 1) substantially reduced CCRCC cell invasion and migration in vitro (Li H. et al, 2020). Zheng et al discovered that circPVT1 (top 7) promotes progression in CCRCC cells by regulating TBX15 expression and sponging miR-145-5p (Zheng et al, 2021).…”

Section: Case Studiesmentioning

confidence: 99%

Using Graph Attention Network and Graph Convolutional Network to Explore Human CircRNA–Disease Associations Based on Multi-Source Data

Li,

Wang,

Zhang

et al. 2022

Front. Genet.

View full text Add to dashboard Cite

Cumulative research studies have verified that multiple circRNAs are closely associated with the pathogenic mechanism and cellular level. Exploring human circRNA–disease relationships is significant to decipher pathogenic mechanisms and provide treatment plans. At present, several computational models are designed to infer potential relationships between diseases and circRNAs. However, the majority of existing approaches could not effectively utilize the multisource data and achieve poor performance in sparse networks. In this study, we develop an advanced method, GATGCN, using graph attention network (GAT) and graph convolutional network (GCN) to detect potential circRNA–disease relationships. First, several sources of biomedical information are fused via the centered kernel alignment model (CKA), which calculates the corresponding weight of different kernels. Second, we adopt the graph attention network to learn latent representation of diseases and circRNAs. Third, the graph convolutional network is deployed to effectively extract features of associations by aggregating feature vectors of neighbors. Meanwhile, GATGCN achieves the prominent AUC of 0.951 under leave-one-out cross-validation and AUC of 0.932 under 5-fold cross-validation. Furthermore, case studies on lung cancer, diabetes retinopathy, and prostate cancer verify the reliability of GATGCN for detecting latent circRNA–disease pairs.

show abstract

Comprehensive profiling of circRNAs and the tumor suppressor function of circHIPK3 in clear cell renal carcinoma

Cited by 17 publications

References 25 publications

Disease-Associated Circular RNAs: From Biology to Computational Identification

Disease-Associated Circular RNAs: From Biology to Computational Identification

piRNA-31115 Promotes Cell Proliferation and Invasion via PI3K/AKT Pathway in Clear Cell Renal Carcinoma

Using Graph Attention Network and Graph Convolutional Network to Explore Human CircRNA–Disease Associations Based on Multi-Source Data

Contact Info

Product

Resources

About