GBDTCDA: Predicting circRNA-disease Associations Based on Gradient Boosting Decision Tree with Multiple Biological Data Fusion

Lei, Xiujuan; Fang, Zengqiang

doi:10.7150/ijbs.33806

Cited by 45 publications

(24 citation statements)

References 41 publications

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“…Within them, one circRNA hsa_circ_0006528 had been recognized as a known disease circRNA. Lei and Fang ( 2019 ) proposed a machine learning-based computational model named Gradient Boosting Decision Tree with multiple biological data to predict circRNA-disease associations (GBDTCDA). With this method, the results of those predicting circRNA-disease associations in BC such as circMYO9B and circRNA_100984 were validated to be consistent with the database circ2Disease and circRNADisease.…”

Section: Diagnostic Value Of Circrnas In Bcmentioning

confidence: 99%

Circular RNAs: Their Role in the Pathogenesis and Orchestration of Breast Cancer

Xiao

Tan

et al. 2021

Front. Cell Dev. Biol.

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As one of the most frequently occurring malignancies in women, breast cancer (BC) is still an enormous threat to women all over the world. The high mortality rates in BC patients are associated with BC recurrence, metastatic progression to distant organs, and therapeutic resistance. Circular RNAs (circRNAs), belonging to the non-coding RNAs (ncRNAs), are connected end to end to form covalently closed single-chain circular molecules. CircRNAs are widely found in different species and a variety of human cells, with the features of diversity, evolutionary conservation, stability, and specificity. CircRNAs are emerging important participators in multiple diseases, including cardiovascular disease, inflammation, and cancer. Recent studies have shown that circRNAs are involved in BC progress by regulating gene expression at the transcriptional or post-transcriptional level via binding to miRNAs then inhibiting their function, suggesting that circRNAs may be potential targets for early diagnosis, treatment, and prognosis of BC. Herein, in this article, we have reviewed and summarized the current studies about the biogenesis, features, and functions of circRNAs. More importantly, we emphatically elucidate the pivotal functions and mechanisms of circRNAs in BC growth, metastasis, diagnosis, and drug resistance. Deciphering the complex networks, especially the circRNA-miRNA target gene axis, will endow huge potentials in developing therapeutic strategies for combating BC.

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Section: Diagnostic Value Of Circrnas In Bcmentioning

confidence: 99%

Circular RNAs: Their Role in the Pathogenesis and Orchestration of Breast Cancer

Xiao

Tan

et al. 2021

Front. Cell Dev. Biol.

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“…Therefore, the pathogenesis of cervical cancer has been extensively explored, which has demonstrated human papillomavirus infection and genetic mutation as crucial to cervical cancer carcinogenesis [ 3 ]. Circular RNA (circRNA), a type of closed-loop structural non-coding RNA molecule, functions as a regulator of gene processes [ 4 ]. Our study aims to investigate the role of one specific circRNA, hsa_circ_0000520, and its underlying regulatory mechanism in cervical cancer, attempting to identify a potential therapeutic target for cervical cancer.…”

Section: Introductionmentioning

confidence: 99%

Hsa_circ_0000520 overexpression increases CDK2 expression via miR-1296 to facilitate cervical cancer cell proliferation

Zheng

Zhang²,

Zhang

et al. 2021

J Transl Med

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Background Circular RNA (circRNA) has been demonstrated to participate in cervical cancer development. In this study, we analyzed the role of hsa_circ_0000520 in cervical cancer. Methods Fifty-two pairs of cervical cancer and adjacent normal tissue samples were collected, and five human cervical cancer cell lines were obtained followed by the detection of hsa_circ_0000520 expression. Nuclear-cytoplasmic isolation and fluorescence in situ hybridization were performed to analyze the subcellular localization of hsa_circ_0000520 while linear RNA was digested by RNase R. Gain- or loss-of function experiments on hsa_circ_0000520 were performed, followed by detection of cell proliferation and cell cycle by EdU, Cell Counting Kit-8, colony formation assay, and flow cytometry respectively. Results Hsa_circ_0000520 and cyclin-dependent kinase 2 (CDK2) were highly expressed in cervical cancer tissues. Binding sites between microRNA-1296 (miR-1296) and hsa_circ_0000520 or CDK2 were verified. Antibody to Argonaute 2 (Ago2) could precipitate hsa_circ_0000520, indicating that hsa_circ_0000520 could competitively bind to miR-1296 via Ago2. Silencing hsa_circ_0000520 inhibited cervical cancer cell proliferation and promoted the inhibitory effects of miR-1296 on CDK2, thereby blocking cell cycle progression and promoting apoptosis. Conclusion These results support the premise that targeting hsa_circ_0000520 can be a potential approach to combat cervical cancer.

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“…Li et al [ 11 ] utilized the NCPCDA method to identify potential circRNA-disease associations using multi-view similarity data, including circRNA functional similarity, disease semantic similarity, and association profile similarity. Lei and Fang [ 12 ] proposed a gradient boosting decision tree algorithm to make the final prediction using multiple biological data on circRNAs and diseases. Wang et al [ 13 ] developed a method in which a numerical descriptor was constructed according to the similarity of diseases and circRNAs, and a deep learning convolutional neural network algorithm was used to extract the deep features of circRNA-disease descriptors.…”

Section: Introductionmentioning

confidence: 99%

Double matrix completion for circRNA-disease association prediction

et al. 2021

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Background Circular RNAs (circRNAs) are a class of single-stranded RNA molecules with a closed-loop structure. A growing body of research has shown that circRNAs are closely related to the development of diseases. Because biological experiments to verify circRNA-disease associations are time-consuming and wasteful of resources, it is necessary to propose a reliable computational method to predict the potential candidate circRNA-disease associations for biological experiments to make them more efficient. Results In this paper, we propose a double matrix completion method (DMCCDA) for predicting potential circRNA-disease associations. First, we constructed a similarity matrix of circRNA and disease according to circRNA sequence information and semantic disease information. We also built a Gauss interaction profile similarity matrix for circRNA and disease based on experimentally verified circRNA-disease associations. Then, the corresponding circRNA sequence similarity and semantic similarity of disease are used to update the association matrix from the perspective of circRNA and disease, respectively, by matrix multiplication. Finally, from the perspective of circRNA and disease, matrix completion is used to update the matrix block, which is formed by splicing the association matrix obtained in the previous step with the corresponding Gaussian similarity matrix. Compared with other approaches, the model of DMCCDA has a relatively good result in leave-one-out cross-validation and five-fold cross-validation. Additionally, the results of the case studies illustrate the effectiveness of the DMCCDA model. Conclusion The results show that our method works well for recommending the potential circRNAs for a disease for biological experiments.

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GBDTCDA: Predicting circRNA-disease Associations Based on Gradient Boosting Decision Tree with Multiple Biological Data Fusion

Cited by 45 publications

References 41 publications

Circular RNAs: Their Role in the Pathogenesis and Orchestration of Breast Cancer

Circular RNAs: Their Role in the Pathogenesis and Orchestration of Breast Cancer

Hsa_circ_0000520 overexpression increases CDK2 expression via miR-1296 to facilitate cervical cancer cell proliferation

Double matrix completion for circRNA-disease association prediction

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