Involvement of CircRNA Expression Profile in Diabetic Retinopathy and Its Potential Diagnostic Value

He, Hengqian; Zhang, Juntao; Gong, Wei; Liu, Mengyun; Líu, Hao; Li, Xiaoyong; Wu, Yufei; Lü, Qianqian

doi:10.3389/fgene.2022.833573

Cited by 9 publications

(6 citation statements)

References 61 publications

(63 reference statements)

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“…Further functional experiments found that has_circ_0005015 acts as a miR-519d-3p sponge to inhibit miR-519d-3p activity, leading to increased MMP-2, XIAP, and STAT3 expression ( Zhang et al, 2017 ). The latest research indicated that hsa_circ_0095008 and hsa_circ_0001883, as diagnostic biomarkers for the early diagnosis of DR patients, presented a diagnostic capability with AUCs of 0.671 and 0.607, respectively ( He et al, 2022 ).…”

Section: The Role Of Circrnas In Diabetic Complicationsmentioning

confidence: 99%

Circular RNAs in diabetes and its complications: Current knowledge and future prospects

Yin

Zhang²,

Xiao³

et al. 2022

Front. Genet.

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A novel class of non-coding RNA transcripts called circular RNAs (circRNAs) have been the subject of significant recent studies. Accumulating evidence points that circRNAs play an important role in the cellular processes, inflammatory expression, and immune responses through sponging miRNA, binding, or translating in proteins. Studies have found that circRNAs are involved in the physiologic and pathologic processes of diabetes. There has been an increased focus on the relevance of between abnormal circRNA expression and the development and progression of various types of diabetes and diabetes-related diseases. These circRNAs not only serve as promising diagnostic and prognostic molecular biomarkers, but also have important biological roles in islet cells, diabetes, and its complications. In addition, many circRNA signaling pathways have been found to regulate the occurrence and development of diabetes. Here we comprehensively review and discuss recent advances in our understanding of the physiologic function and regulatory mechanisms of circRNAs on pancreatic islet cells, different subtypes in diabetes, and diabetic complications.

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Section: The Role Of Circrnas In Diabetic Complicationsmentioning

confidence: 99%

Circular RNAs in diabetes and its complications: Current knowledge and future prospects

Yin

Zhang²,

Xiao³

et al. 2022

Front. Genet.

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“…Although considerable progress has been made, the precise mechanisms underlying DR pathogenesis are still not fully understood. Previous studies concerning circRNA expression have primarily been performed on the serum of DM patients [9][10] or peripheral blood mononuclear cells [15] . However, the expression of the circRNA pro le in retina tissues in the DR model has not been previously studied.…”

Section: Discussionmentioning

confidence: 99%

“…The most signi cant term of mRNAs in MF was "receptor binding," following our ndings [23] . He et al performed circular RNA microarray analysis in peripheral blood mononuclear cells (PBMCs) of DR patients and healthy controls, and circRNAs in DR were enriched in extracellular matrix (ECM)-receptor interaction and focal adhesion pathways [15] . Endothelial dysfunction is the pathological basis of DR and a risk factor for retinal vascular dysfunction [24] .…”

Section: Discussionmentioning

confidence: 99%

Analysis of circRNA and miRNA profiles in type-1 diabetic retinopathy

Dong,

Yang,

et al. 2023

Preprint

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Purpose Diabetic retinopathy (DR) is a microangiopathy of the retina, from which nearly all people with diabetes eventually suffer. However, its pathogenic mechanism remains to be elucidated. Circular RNAs (circRNAs) are endogenous non-coding RNAs that have recently been recognized to play vital roles in DR. This study aimed to explore the role of circRNAs and microRNA (miRNA) in the pathogenesis of DR and to analyze the circRNA-miRNA-mRNA regulation network. Methods Sixty rats were randomly assigned to the diabetic and control groups. The retina tissues were extracted for next-generation RNA sequencing and bioinformatics analysis. Results A total of 830 circRNAs and 37 miRNAs were differentially expressed in the two groups. Aberrantly expressed genes that may be enriched in the glutamatergic synapse GABAergic synapse, morphine addiction, phosphatidylinsitol signaling pathway, tight junctions, and dopaminergic synapse were revealed by functional annotation. The circRNA-miRNA co-expression networks and the circRNA-miRNA-mRNA co-regulated competing endogenous RNA (ceRNA) network were constructed. Novel-circRNA_0007196 was selected as the target gene. Conclusions Different coding and non-coding RNA profiles and genes in the co-regulation network are likely to play essential roles in DR. This study provides new insights into the pathogenic mechanisms and pathological processes of early DR.

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“…Sponging up miRNA and interacting with RNA-binding proteins (RBP), circRNA plays many pathological functions like regulating miRNA activity. He et al (He et al, 2022) performed circRNA microarray analysis and found its expression profile in diabetes. By acting as microRNA sponges for miR-7 (ciRS-7) and miR-124-3p and miR-338-3p (circHIPK3), ciRS-7 and circHIPK3 promote insulin secretion.…”

Section: Introductionmentioning

confidence: 99%

GCNCMI: A Graph Convolutional Neural Network Approach for Predicting circRNA-miRNA Interactions

Xiao

Chen

et al. 2022

Front. Genet.

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The interactions between circular RNAs (circRNAs) and microRNAs (miRNAs) have been shown to alter gene expression and regulate genes on diseases. Since traditional experimental methods are time-consuming and labor-intensive, most circRNA-miRNA interactions remain largely unknown. Developing computational approaches to large-scale explore the interactions between circRNAs and miRNAs can help bridge this gap. In this paper, we proposed a graph convolutional neural network-based approach named GCNCMI to predict the potential interactions between circRNAs and miRNAs. GCNCMI first mines the potential interactions of adjacent nodes in the graph convolutional neural network and then recursively propagates interaction information on the graph convolutional layers. Finally, it unites the embedded representations generated by each layer to make the final prediction. In the five-fold cross-validation, GCNCMI achieved the highest AUC of 0.9312 and the highest AUPR of 0.9412. In addition, the case studies of two miRNAs, hsa-miR-622 and hsa-miR-149-5p, showed that our model has a good effect on predicting circRNA-miRNA interactions. The code and data are available at https://github.com/csuhjhjhj/GCNCMI.

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Involvement of CircRNA Expression Profile in Diabetic Retinopathy and Its Potential Diagnostic Value

Cited by 9 publications

References 61 publications

Circular RNAs in diabetes and its complications: Current knowledge and future prospects

Circular RNAs in diabetes and its complications: Current knowledge and future prospects

Analysis of circRNA and miRNA profiles in type-1 diabetic retinopathy

GCNCMI: A Graph Convolutional Neural Network Approach for Predicting circRNA-miRNA Interactions

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