KGANCDA: predicting circRNA-disease associations based on knowledge graph attention network DOI
Wei Lan, Yi Dong, Qingfeng Chen

et al.

Briefings in Bioinformatics, Journal Year: 2021, Volume and Issue: 23(1)

Published: Oct. 27, 2021

Abstract Increasing evidences have proved that circRNA plays a significant role in the development of many diseases. In addition, researches shown can be considered as potential biomarker for clinical diagnosis and treatment disease. Some computational methods been proposed to predict circRNA-disease associations. However, performance these is limited sparsity low-order interaction information. this paper, we propose new method (KGANCDA) associations based on knowledge graph attention network. The graphs are constructed by collecting multiple relationship data among circRNA, disease, miRNA lncRNA. Then, network designed obtain embeddings each entity distinguishing importance information from neighbors. Besides neighbor information, it also capture high-order multisource associations, which alleviates problem sparsity. Finally, multilayer perceptron applied affinity score experiment results show KGANCDA outperforms than other state-of-the-art 5-fold cross validation. Furthermore, case study demonstrates an effective tool

Language: Английский

Circular RNA PIP5K1A promotes colon cancer development through inhibiting miR-1273a DOI Creative Commons
Qu Zhang, Chi Zhang,

Jianxin Ma

et al.

World Journal of Gastroenterology, Journal Year: 2019, Volume and Issue: 25(35), P. 5300 - 5309

Published: Sept. 18, 2019

Circular RNAs (circRNAs) are considered to be highly stable due the closed structure, which predominately correlated with development and progression of a wide variety cancers. Colon cancer is one most common malignancies worldwide. A recent study demonstrated upregulated expression circPIP5K1A in non-small cell lung cancer. However, few studies have investigated relationship between circ_0014130 level colon Therefore, elucidating underlying mechanisms circPIP5K1A's role may help identification novel diagnostic therapeutic targets for cancer.To investigate status cancers its effects on modulation development.The tissue serum samples from patients, as well human colonic lines was detected by real-time quantitative reverse transcription-polymerase chain reaction. Following transfection specifically synthesized small interfering RNA (siRNA) into lines, we used Hoechst staining assay measure ratio death absence circPIP5K1A. Moreover, also Transwell assess migratory function cells overexpressing Additionally, employed series bioinformatics prediction programs predict potential circPIP5K1A-targeted miRNAs mRNAs. The miR-1273a vector constructed, then transfected or without cells. Afterwards, activator protein 1 (AP-1), interferon regulating factor 4 (IRF-4), caudal type homeobox 2 (CDX-2), zinc finger cerebellum (Zic-1) western blotting.CircPIP5K1A significantly relative their adjacent normal tissues. Knockdown impaired viability suppressed invasion migration, while enforced exhibited opposite migration. Bioinformatics program predicted that association miR-1273a, AP-1, IRF-4, CDX-2, Zic-1. Subsequent showed overexpression augmented AP-1 but attenuated Reciprocally, abrogated oncogenic cancers.Overall, our data demonstrate circPIP5K1A-miR-1273a axis regulation development, provides insights pathogenesis.

Language: Английский

Citations

77
Deep Matrix Factorization Improves Prediction of Human CircRNA-Disease Associations DOI
Chengqian Lu, Min Zeng, Fuhao Zhang

et al.

IEEE Journal of Biomedical and Health Informatics, Journal Year: 2020, Volume and Issue: 25(3), P. 891 - 899

Published: June 3, 2020

In recent years, more and evidence indicates that circular RNAs (circRNAs) with covalently closed loop play various roles in biological processes. Dysregulation mutation of circRNAs may be implicated diseases. Due to its stable structure resistance degradation, provide great potential diagnostic biomarkers. Therefore, predicting circRNA-disease associations is helpful disease diagnosis. However, there are few experimentally validated between Although several computational methods have been proposed, precisely representing underlying features grasping the complex structures data still challenging. this paper, we design a new method, called DMFCDA (Deep Matrix Factorization CircRNA-Disease Association), infer associations. takes both explicit implicit feedback into account. Then, it uses projection layer automatically learn latent representations With multi-layer neural networks, can model non-linear grasp data. We assess performance using leave-one cross-validation 5-fold on two datasets. Computational results show efficiently infers according AUC values, percentage retrieved top ranks, statistical comparison. also conduct case studies evaluate DMFCDA. All provides accurate predictions.

Language: Английский

Citations

77
Circular RNAs: Emerging Role in Cancer Diagnostics and Therapeutics DOI Creative Commons

Anuva Rajappa,

Sucharita Sen Banerjee,

Vivek Sharma

et al.

Frontiers in Molecular Biosciences, Journal Year: 2020, Volume and Issue: 7

Published: Oct. 28, 2020

Circular RNAs (circRNAs) are rapidly coming to the fore as major regulators of gene expression and cellular functions. They elicit their influence

Language: Английский

Citations

71
Circular RNA in colorectal cancer DOI Creative Commons

Toni Li,

Wei Cen Wang,

Vivian C. McAlister

et al.

Journal of Cellular and Molecular Medicine, Journal Year: 2021, Volume and Issue: 25(8), P. 3667 - 3679

Published: March 9, 2021

Circular RNA (circRNA) is a highly abundant type of single-stranded non-coding RNA. Novel research has discovered many roles circRNA in colorectal cancer (CRC) including proliferation, metastasis and apoptosis. Furthermore, circRNAs also play role the development drug resistance have unique associations with tumour size, staging overall survival CRC that lend potential to serve as diagnostic prognostic biomarkers. Among cancers worldwide, ranks second mortality third incidence. In order better understanding influence on progression, this review summarizes specific evaluates their value therapeutic targets biomarkers for CRC. We aim provide insight therapy clinical decision-making.

Language: Английский

Citations

62
KGANCDA: predicting circRNA-disease associations based on knowledge graph attention network DOI
Wei Lan, Yi Dong, Qingfeng Chen

et al.

Briefings in Bioinformatics, Journal Year: 2021, Volume and Issue: 23(1)

Published: Oct. 27, 2021

Abstract Increasing evidences have proved that circRNA plays a significant role in the development of many diseases. In addition, researches shown can be considered as potential biomarker for clinical diagnosis and treatment disease. Some computational methods been proposed to predict circRNA-disease associations. However, performance these is limited sparsity low-order interaction information. this paper, we propose new method (KGANCDA) associations based on knowledge graph attention network. The graphs are constructed by collecting multiple relationship data among circRNA, disease, miRNA lncRNA. Then, network designed obtain embeddings each entity distinguishing importance information from neighbors. Besides neighbor information, it also capture high-order multisource associations, which alleviates problem sparsity. Finally, multilayer perceptron applied affinity score experiment results show KGANCDA outperforms than other state-of-the-art 5-fold cross validation. Furthermore, case study demonstrates an effective tool

Language: Английский

Citations

62