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

A detection model of testis-derived circular RNAs in serum for predicting testicular sperm retrieval rate in non-obstructive azoospermia patients

BACKGROUND

Microdissection testicular sperm extraction is an effective method to retrieve sperm from non-obstructive azoospermia patients. However, its successful rate is less than 50%.

OBJECTIVES

To identify the predictive value of circular RNAs in serum for sperm retrieval rate in non-obstructive azoospermia patients.

MATERIALS AND METHODS

180 non-obstructive azoospermia patients were recruited in this study, including 84 individuals with successful sperm retrieval and 96 individuals with failed sperm retrieval. Our study contained two phases. First, 20 patients, selected from the 180 patients, were included in screening cohort. In this cohort, the top 20 circular RNAs from our previous testicular circRNA profiles were verified between successful and failed sperm retrieval groups using real-time polymerase chain reaction. Six circular RNAs with the most significantly different expressions were selected for further verification. Second, the 180 patients were included as discovery cohort to verify the six circular RNAs. Circular RNAs were extracted from serum in each participant. Logistic regression analysis was further performed to identify the predictive value and the area under the curve analysis was used to evaluate diagnostic efficiency, sensitivity, and specificity.

RESULTS

Six circular RNAs including hsa_circ_0058058, hsa_circ_0008045, hsa_circ_0084789, hsa_circ_0000550, hsa_circ_0007422, and hsa_circ_0004099 showed aberrant expressions between the successful and failed sperm retrieval group. In addition, both single-circular RNA panels and multi-circular RNA panels were finally verified to be significant in predicting sperm retrieval rate. Notably, multi-circular RNAs panels demonstrated better predictive abilities compared with single-circRNA panels, and the combined panel of six-circular RNAs (risk score = 1.094×hsa_circ_0058058+0.697×hsa_circ_0008045+0.718×hsa_circ_0084789-0.591×hsa_circ_0000550-0.435×hsa_circ_0007422-1.017×hsa_circ_0004099-1.561) exhibited the best predictive ability in the present study with an AUC of 0.977, a sensitivity of 91.7% and a specificity of 86.5%. A higher risk score indicated a higher risk of failure in sperm retrieval.

DISCUSSION AND CONCLUSION

Our study was the first to report that testis-derived circular RNAs in serum have the ability to predict sperm retrieval rate in non-obstructive azoospermia patients, whether it is a single-circular RNA or a combination of multi-circular RNAs.

Integrative bioinformatics analysis of biomarkers and pathways for exploring the mechanisms and molecular targets associated with pyroptosis in type 2 diabetes mellitus

Introduction

Research has shown that pyroptosis contributes greatly to the progression of diabetes and its complications. However, the exact relationship between this particular cell death process and the pathology of type 2 diabetes mellitus (T2DM) remains unclear. In this study, we used bioinformatic tools to identify the pyroptosis-related genes (PRGs) associated with T2DM and to analyze their roles in the disease pathology.

Methods

Two microarray datasets, GSE7014 and GSE25724, were obtained from the GEO database and assessed for differentially expressed genes (DEGs). The T2DM-associated DEGs that overlapped with differentially expressed PRGs were noted as T2DM-PRGs. Subsequently, 25 T2DM-PRGs were validated and subjected to functional enrichment analysis through Gene Ontology annotation analysis, Kyoto Encyclopedia of Genes and Genomes pathway analysis, and gene set enrichment analysis (GSEA). The diagnostic and predictive value of the T2DM-PRGs was evaluated using receiver operating characteristic curves (ROC). Additionally, a single-sample GSEA algorithm was applied to study immune infiltration in T2DM and assess immune infiltration levels.

Results

We identified 25 T2DM-PRGs that were significantly enriched in the nuclear factor-kappa B signaling and prostate cancer pathways. The top five differentially expressed prognostic T2DM-PRGs targeted by miRNAs were PTEN, BRD4, HSP90AB1, VIM, and PKN2. The top five differentially expressed T2DM-PRGs associated with transcription factors were HSP90AB1, VIM, PLCG1, SCAF11, and PTEN. The genes PLCG1, PTEN, TP63, CHI3L1, SDHB, DPP8, BCL2, SERPINB1, ACE2, DRD2, DDX58, and BTK showed excellent diagnostic performance. The immune infiltration analysis revealed notable differences in immune cells between T2DM and normal tissues in both datasets. These findings suggest that T2DM-PRGs play a crucial role in the development and progression of T2DM and could be used as potential diagnostic biomarkers and therapeutic targets.

Discussion

Investigating the mechanisms and biomarkers associated with pyroptosis may offer valuable insights into the pathophysiology of T2DM and lead to novel therapeutic approaches to treat the disease.

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

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.

Potential biomarkers for retinopathy of prematurity identified by circular RNA profiling in peripheral blood mononuclear cells

Purpose

This study aims to reveal the altered expression profiles of circular RNAs (circRNAs) in the peripheral blood mononuclear cells (PBMCs) of patients with retinopathy of prematurity (ROP), and to identify potential biomarkers for ROP diagnosis.

Methods

Differentially expressed circRNAs in PBMCs of five infants with ROP and five controls were identified using microarray analysis. Twelve altered circRNAs were validated using reverse transcription-quantitative real-time polymerase chain reaction (RT-qPCR). Bioinformatic analyses were conducted to predict the circRNA/miRNA interactions, competing endogenous RNA (ceRNA) network, related biological functions, and signaling pathways. Four selected circRNAs in PBMCs were verified using RT-qPCR in another cohort, including 24 infants with ROP and 23 premature controls, and receiver operating characteristic (ROC) curves were used to estimate their potential as diagnostic biomarkers of ROP.

Results

A total of 54 and 143 circRNAs were significantly up- and down-regulated, respectively, in the PBMCs of patients with ROP compared with controls. Twelve of the significantly altered circRNAs were preliminarily validated by RT-qPCR, which confirmed the reliability of the microarray analysis. The circRNA/miRNA interactions and ceRNA network were displayed according to the altered circRNAs. Three circRNAs (hsa_circRNA_061346, hsa_circRNA_092369, and hsa_circRNA_103554) were identified as potential diagnostic biomarkers for ROP with certain clinical values.

Conclusions

CircRNAs were significantly altered in PBMCs of treatment-requiring ROP patients. CircRNAs may be used as potential biomarkers and possible therapeutic targets for ROP.

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

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.