ABSTRACT
Aims/Introduction
Diabetic retinopathy (DR) is a prevalent chronic complication of type 2 diabetes mellitus (T2DM), contributing significantly to vision impairment. Circular RNAs (circRNAs) have emerged as key regulators in the pathogenesis of DR. This study aimed to investigate the differential expression profiles and potential functions of circRNAs in patients with DR compared to those without DR.
Materials and Methods
In this single‐center, prospective study, serum samples from 20 T2DM patients with DR and 20 without DR were analyzed using the Arraystar Human circRNA Expression Profile V2.0 microarray. An additional cohort of 40 patients (20 DR and 20 without DR) was used to validate selected circRNAs via quantitative reverse transcription PCR (qRT‐PCR). Enriched signaling pathways were identified using Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses. CircRNA–miRNA–mRNA interaction networks were constructed using bioinformatics tools.
Results
A total of 376 circRNAs were differentially expressed, including 169 upregulated and 207 downregulated circRNAs. qRT‐PCR validation confirmed five downregulated circRNAs and three upregulated circRNAs, which were selected for competing endogenous RNA analysis. The circRNA–miRNA–mRNA network analysis revealed that hsa_circRNA_407262 (also known as circRNA LOC389906) may act as a miRNA sponge regulating key signaling pathways, including wingless/integrated, gonadotropin‐releasing hormone, tumor necrosis factor, and rat sarcoma pathways.
Conclusions
This study identified five downregulated and three upregulated circRNAs associated with DR. Among them, hsa_circRNA_407262 may play a crucial role in the pathological process of DR and holds potential as a candidate biomarker for early diagnosis.
Keywords: Biomarker, Circular RNAs, Diabetic retinopathy
INTRODUCTION
Diabetic retinopathy (DR) is a predominant chronic complication of diabetes mellitus, and its incidence continues to rise in tandem with the global diabetes epidemic. Currently, approximately 100 million individuals worldwide are affected by DR 1 . In China, the prevalence of DR among diabetic patients is approximately 23%, making it a leading cause of vision loss in the working‐age population and a substantial threat to the quality of life of individuals with diabetes 2 , 3 .
The onset and progression of DR are closely associated with the duration of diabetes 4 . Patients with type 2 diabetes mellitus (T2DM) who have had the disease for over 10 years face a threefold higher risk of developing DR‐related visual impairment 5 . The pathogenesis of DR is multifactorial, involving chronic hyperglycemia‐induced activation of the retinal polyol pathway, metabolic dysregulation in capillary endothelial cells, hemodynamic changes, and abnormalities in coagulation and fibrinolysis. Additionally, excessive production of oxidative free radicals and upregulation of pro‐angiogenic factors significantly contribute to vascular damage observed in DR 6 , 7 , 8 .
The clinical management of DR is challenged by late diagnosis, limited predictive biomarkers, and incomplete understanding of the molecular mechanisms driving disease progression 9 . Current therapeutic strategies primarily target advanced pathological changes rather than early regulatory events 10 . Circular RNAs (circRNAs) are a class of endogenous, closed‐loop, non‐coding RNAs formed through back‐splicing of precursor mRNA 11 , 12 . This circular structure makes circRNAs highly stable and less prone to degradation. circRNA can function as sponges for microRNAs (miRNA), regulate miRNA splicing and transcription, interact with RNA‐binding proteins, form functional RNA–protein complexes, and modulate protein function 13 . CircRNAs, owing to their high stability, tissue specificity, and regulatory roles in gene expression, have emerged as potential biomarkers for a variety of diseases, including cerebral ischemia–reperfusion injury, cancers (e.g., gastric, liver, bladder, and colorectal), atherosclerosis, osteoarthritis, and pulmonary fibrosis 11 , 14 . However, the specific roles of circRNA in DR are not fully understood.
This study aimed to investigate the expression profiles of circRNAs in patients with T2DM, with and without DR, and to identify specific circRNAs associated with DR.
MATERIALS AND METHODS
Participants and sample collection
In this single‐center, prospective study, 40 T2DM patients (20 with DR and 20 without DR) who were admitted to the Department of Endocrinology, Haidian Hospital, Peking University Third Hospital, China, from January to December 2021 were recruited. The inclusion criteria for this study were clinically diagnosed T2DM patients aged 18–80 years. The exclusion criteria included patients with severe non‐proliferative DR or proliferative DR, significant diabetic macular edema, coexisting glaucoma or other primary eye diseases, planned eye surgery in the near future, pregnancy and lactation, a history of drug or alcohol abuse, malignant hypertension, severely abnormal liver and kidney function, and a history of tumors. T2DM was diagnosed according to the Standards of Medical Care for Type 2 Diabetes in China (2019) 15 . DR was diagnosed based on the International Clinical Grading Standards for Diabetic Retinopathy issued by the American Ophthalmic Association and the International Society of Ophthalmology in 2003 16 . The duration of illness was defined as the interval between the first diagnosis and the time of enrollment into the present study.
Blood samples were obtained from all participants following a minimum 12‐h fast. After collection, they were left to stand at 4°C for 2 h, then centrifuged at 2000 × g for 20 min at the same temperature. The precipitates were discarded, and a minimum of 3 mL of serum was isolated from each sample and stored at −80°C.
Ethical approval was obtained from the Beijing Haidian Hospital (approval number 2018036), and this study adhered to the tenets of the Declaration of Helsinki. Informed consent was obtained from all participants.
CircRNA expression profile chip experiment and data processing
Serum samples were collected from another 20 DR and 20 non‐DR patients, who met the same inclusion and exclusion criteria, for quantitative real‐time PCR (qRT‐PCR) validation. The Trizol LS kit (Invitrogen) was used to extract total RNA from the serum samples of T2DM patients. NanoDrop ND‐1000 was used to determine the purity and concentration of total RNA from each sample. Total RNA was digested with RNase R (Epicenter, Inc.) to remove linear RNA and enrich circRNAs. The extracted RNA was amplified and labeled with the Arraystar Super RNA Labeling Kit (Arraystar, Rockville, MD, USA). Labeled cRNAs were hybridized to Arraystar Human CircRNAs Array V2 (8 × 15K, Arraystar, Rockville, MD, USA). CircRNA microarray results were analyzed using the Agilent Scanner G2505C (Agilent).
Agilent Feature Extraction software (Agilent, Version 11.0.1.1) was used to analyze the obtained circRNA expression profile chip microarray images. The raw data were processed and normalized using the limma package in R software. The differential expression of circRNA between the two groups was determined using Volcano Plot filtering. circRNAs with a fold change of ≥1 and P < 0.05 were considered as significantly upregulated or downregulated in the microarray analysis.
qRT‐PCR
Selected circRNAs were quantified by qRT‐PCR using a 2X PCR master mix (Arraystar: AS‐MR‐006‐5) on QuantStudio™ 5 Real‐time PCR System (Applied Biosystems), according to the manufacturer's instructions. In brief, total RNA was treated with RNase R to remove linear RNAs, followed by reverse transcription into cDNA. qRT‐PCR was then performed, with each reaction in a final volume of 20 μL, containing 2× master mix, specific divergent primers, cDNA template, and nuclease‐free water. The thermal cycling conditions consisted of an initial denaturation at 95°C for 10 min, followed by 40 cycles of denaturation at 95°C for 10 s and annealing/extension at 60°C for 60 s. Relative circRNA expression levels were calculated using the 2−ΔΔCt method.
Gene ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG) pathway analyses
To identify the potential functions of differentially expressed circRNAs in the endogenous RNA (ceRNA) network, GO and KEGG pathway enrichment analyses were performed using the “clusterProfiler” package in R software. A P‐value of less than 0.05 indicated statistical significance.
circRNA–miRNA network construction
To explore the regulatory relationships between differentially expressed circRNAs and their potential target miRNAs, a circRNA‐miRNA interaction network was constructed and visualized using Cytoscape software (version 3.9.1). Potential miRNA binding sites were predicted using Arraystar's proprietary miRNA target prediction software.
Statistical analysis
Statistical analysis was conducted using SPSS version 18.0 (SPSS, Chicago, IL, USA). Normality was tested using the Shapiro–Wilk test. Quantitative variables were presented as mean ± standard deviation (SD) or median with interquartile range (Q25%–Q75%). Independent samples of continuous data were analyzed using the t‐test or Mann–Whitney U test. The chi‐squared test was used for categorical variables. All tests were two‐sided, and a P‐value of less than 0.05 was regarded as statistically significant.
RESULTS
Patient characteristics
According to the inclusion and exclusion criteria, a total of 40 patients were enrolled in this study. The baseline characteristics are shown in Table 1. No significant differences were observed in the baseline characteristics between the DR and non‐DR groups.
Table 1.
Basic characteristics of participants
| Non‐DR (n = 20) | DR (n = 20) | P‐value | |
|---|---|---|---|
| Age (years) | 57.57 ± 19.39 | 54.66 ± 9.35 | 0.550 |
| Sex | 0.750 | ||
| Male | 7 | 9 | |
| Female | 13 | 11 | |
| Duration of illness (years) | 8.97 ± 4.79 | 10.49 ± 5.39 | 0.354 |
| BMI (kg/m2) | 27.00 ± 5.07 | 27.09 ± 3.59 | 0.950 |
| Hemoglobin (g/L) | 136.74 ± 19.99 | 147.85 ± 29.17 | 0.168 |
| HbA1c (%) | 11.50 ± 2.17 | 11.26 ± 1.96 | 0.720 |
| Glucose (mmol/L) | 8.27 ± 2.47 | 8.37 ± 3.57 | 0.916 |
| TCHO (mmol/L) | 5.09 ± 1.96 | 4.38 ± 1.73 | 0.233 |
| TG (mmol/L) | 2.44 ± 1.99 | 1.60 ± 0.68 | 0.088 |
| HDL (mmol/L) | 1.01 ± 0.53 | 0.89 ± 0.33 | 0.387 |
| LDL (mmol/L) | 2.68 ± 0.87 | 2.42 ± 1.07 | 0.406 |
| Creatinine (mmol/L) | 72.27 ± 9.00 | 67.40 ± 11.48 | 0.144 |
BMI, body mass index; DR, diabetic retinopathy; HbA1c, glycated hemoglobin A1c; HDL, high‐density lipoprotein; LDL, low‐density lipoprotein; TCHO, total cholesterol; TG, triglyceride.
CircRNA expression profiles in DR and non‐DR patients
Blood samples were extracted from DR (n = 20) and non‐DR (n = 20) patients. A total of 376 circRNAs were identified, of which 169 were upregulated, and 207 were downregulated (Figure 1a). Principal component analysis demonstrated good consistency in biological replicates between the DR and non‐DR groups (Figure 1b and Table S1). Heatmaps of total circRNA expression and the top 25 differentially expressed circRNAs are shown in Figure 1c,d. The top 25 upregulated and downregulated circRNAs in DR patients compared to non‐DR patients are listed in Tables S2 and S3, respectively.
Figure 1.
Profile of circRNA level in DR and non‐DR patients. (a) Differentially expressed circRNAs between DR and non‐DR groups by Volcano map analysis. (b) Principal components analysis between DR and non‐DR patients. (c) Heatmap of all circRNAs. (d) Heatmap of the top 25 upregulated and downregulated circRNAs.
The chromosome distribution of 376 differentially expressed circRNAs between DR and non‐DR patients was analyzed, and the results are shown in Figure 2a. The distribution of upregulated and downregulated circRNA subtypes was also assessed. Most downregulated circRNAs were exon‐derived (Figure 2b,c). The box plot of circRNA expression showed that the median line positions of biological replicates in each group were consistent, indicating good sample comparability (Figure 2d).
Figure 2.
Differential expression of circRNA. (a) Chromosome distribution of differentially expressed circular RNA. The outermost ring represents the chromosome. The inner ring red column represents the upregulated circular RNA, and the green column represents the downregulated circular RNA. (b) Statistical map of the distribution of differential circular RNA subtypes with upregulation. (c) Statistical map of the distribution of differential circular RNA subtypes with downregulation. (d) The results of the circular RNA box pattern showed that the position of the median line of biological repeat samples was almost the same in each group.
qRT‐PCR validation of differentially expressed circRNAs
To confirm the reliability of differentially expressed circRNAs, an additional cohort of 20 DR and 20 non‐DR patients was included for qRT‐PCR validation. circRNAs with the greatest fold change and the highest original signal values were selected for verification. A total of 10 circRNAs with significant differential expression were identified. Among them, five were downregulated, three were upregulated, and two showed opposite expression trends compared to the microarray results (Figure 3).
Figure 3.
Validation of circRNA expression by qRT‐PCR between non‐DR and DR patients. *P < 0.01, **P < 0.001, ***P < 0.0001.
Functional analysis of differential circRNAs by GO and KEGG analyses
The five downregulated and three upregulated circRNAs were selected for competing ceRNA mechanism analysis. Eight predicted genes of target circRNAs were subjected to GO and KEGG analyses. The top 30 enriched GO pathways in DR patients are shown in Figure 4. These pathways are mainly related to the regulation of cell communication, signaling, and nervous system development. KEGG analysis of the predicted mRNA revealed enrichment in 20 signaling pathways, including the wingless/integrated (Wnt), gonadotropin hormone‐releasing hormone (GnRH) secretion, tumor necrosis factor (TNF), and rat sarcoma (Ras) signaling pathways (Table S4).
Figure 4.
Top 30 differentially expressed circRNAs in DR and non‐DR patients by Gene Ontology (GO) analysis.
circRNA–miRNA network construction
The circRNA–microRNA–mRNA network was predicted and visualized. hsa_circRNA_407262 (circRNA LOC389906) was found to competitively bind miRNAs associated with the Wnt, GnRH secretion, TNF, and Ras signaling pathways. By binding hsa‐miR‐877‐3p, hsa_circRNA_087631 (circRNA HABP4) regulates the expression of WNT10A. hsa_circRNA_101698 (CARHSP1) regulates the expression of NOTUM, TNF, HCN2, PAK4, and FGF17 by binding hsa‐miR‐92a‐2‐5p and hsa‐miR‐939‐5p, thereby participating in the aforementioned pathways (Figure 5).
Figure 5.
CircRNA–miRNA–mRNA network. The blue ellipse represented circRNA, the red V represented miRNA (microRNA), and the green diamond represented messenger RNA (mRNA).
DISCUSSION
DR is one of the most common complications of diabetes and the leading cause of vision impairment in diabetic patients 17 . As the prevalence of diabetes continues to rise, the number of patients with DR is expected to increase significantly. However, the early diagnosis of DR remains challenging due to the lack of specific and overt symptoms in its early stages 18 . Identifying reliable biomarkers for early detection is therefore crucial. An increasing number of studies have explored the relationship between circRNAs and metabolic diseases. Recent evidence has suggested that circRNAs may be involved in the pathophysiological process of DR 19 . In this study, we found that circRNA LOC389906 is downregulated in DR and associated with the Wnt, GnRH, TNF, and Ras pathways.
The pathogenesis of DR is multifactorial, involving several key mechanisms such as the heightened activity of the retinal polyol pathway due to hyperglycemia, metabolic disturbances in capillary wall cells, hemodynamic imbalances, as well as abnormalities in coagulation and fibrinolysis 6 . Additionally, oxidative stress, characterized by the accumulation of free radicals, contributes to the pathophysiology of DR, along with the overexpression of pro‐angiogenic factors that drive neovascularization 6 . CircRNAs play a crucial role in the biological functions of the ceRNA network 20 . Alterations in circRNA expression are closely linked to the progression of DR via the ceRNA mechanism. For instance, hsa_circ_0041795 competes with miR‐646, leading to the upregulation of VEGFC and exacerbating retinal pigment epithelial cell damage 21 . Yan et al. identified 529 circRNAs with abnormal expression in DR patients 22 . Circ_0005015 has been implicated in retinal endothelial cell angiogenesis, influencing key processes such as endothelial cell proliferation, migration, and tubule formation 22 . A study examining 19 DR patients, 15 non‐DR patients, and 21 non‐T2DM controls found that DR patients exhibited upregulated circRNA expression 23 . Li et al. further demonstrated an aberrant profile of exosomal circRNAs in the serum of PDR patients, suggesting that circFndc3b, derived from high‐glucose‐induced endothelial cells, plays a pivotal role in angiogenesis during the progression of DR 24 .
In this study, we identified several differentially expressed circRNAs in serum samples from T2DM patients with and without DR. The circRNAs identified in our study, including five downregulated circRNAs (hsa_circ_087631; hsa_circ_101698; hsa_circ_102889; hsa_circ_406551; hsa_circ_407262) and three upregulated circRNAs (hsa_circ_005739; hsa_circ_103491; hsa_circ_405317), were significantly associated with key pathways involved in DR, such as the Wnt signaling pathway, TNF signaling, and Ras signaling pathway 25 . These findings are consistent with previous research suggesting that circRNAs play important roles in regulating inflammatory responses, oxidative stress, and angiogenesis, all of which are critical in the development and progression of DR. Hsa_circRNA_407262 participates in the Wnt signaling and TNF signaling pathways, both of which are known to be involved in inflammation and angiogenesis during DR progression 25 . LOC389906 is a pseudogene of ZNF839, a zinc finger protein (ZFP). Emerging evidence has suggested that ZFPs play critical roles in gene regulation, cell differentiation, apoptosis, and signal transduction 26 . ZFPs are also involved in the pathogenesis of DR by regulating inflammatory responses, angiogenesis, and oxidative stress, all of which are key mechanisms in DR progression 27 . Exploring the role of ZFP‐related circRNAs may provide new insights into the molecular mechanisms underlying retinal damage in DR.
Wnt signaling has been recognized as a key regulator of inflammation and angiogenesis in DR. Dysregulation of this pathway can lead to abnormal retinal blood vessel growth, contributing to diabetic macular edema and proliferative diabetic retinopathy 25 . Antagonizing Wnt signaling could therefore represent a promising therapeutic strategy for DR. Our findings also suggested that circRNA_407262 may serve as a molecular regulator of Wnt signaling in DR, opening up new avenues for potential therapeutic interventions. Furthermore, the TNF signaling pathway is crucial for mediating inflammation in diabetic retinopathy 28 . Inflammatory molecules regulated by TNF‐α promote retinal vascular permeability and pathological angiogenesis, which are hallmarks of advanced DR. The identification of circRNA‐involved signaling pathways in this study further underscores the potential of these molecules in modulating the inflammatory processes underlying DR.
Current screening approaches for DR largely rely on detecting established anatomical or functional retinal changes, which limits their ability to capture early molecular alterations before visible lesions appear. Circulating circRNAs may address this gap by reflecting subclinical disease activity, enabling earlier risk stratification and dynamic monitoring of disease progression or treatment response. In addition, circRNAs are involved in key pathogenic processes such as inflammation, angiogenesis, and metabolic dysregulation, providing mechanistic insight beyond structural imaging. As blood‐based biomarkers, circRNAs may be readily incorporated into routine diabetes care and are particularly valuable when imaging quality or access is limited, thereby complementing existing screening strategies and supporting more personalized disease management.
While our study provides valuable insights into the circRNA profile of DR, several limitations should be noted. The relatively small sample size and the cross‐sectional nature of the study limit the generalizability of our findings. Larger, longitudinal studies are needed to confirm the role of circRNAs as reliable biomarkers for DR diagnosis and to explore their potential as therapeutic targets. Additionally, the functional interpretations derived from GO/KEGG enrichment analyses and the circRNA‐miRNA‐mRNA regulatory network are based on bioinformatics predictions. Further molecular and functional studies are therefore necessary to establish the precise mechanisms by which circRNAs contribute to DR pathogenesis. Future work should also validate key circRNAs in larger cohorts of DR patients and investigate their functional roles in vivo. Animal models of DR could provide valuable insights into the direct involvement of circRNAs in retinal inflammation, neovascularization, and vascular permeability. Moreover, exploring the potential of these circRNAs as diagnostic biomarkers or therapeutic targets could significantly improve the management of DR in diabetic patients.
In conclusion, the present study provides a comprehensive analysis of circRNA expression profiles in patients with DR and identifies several circRNAs that may play key roles in the pathogenesis of DR. These findings provide valuable insights into the use of circRNAs as novel biomarkers and therapeutic targets for DR.
AUTHOR CONTRIBUTIONS
WA and BW carried out the interpretation and analysis of data and drafted the article. HY participated in the conception and design of the work. ZS, LJ, BA, ZC, FC, XO, and WH participated in obtaining blood samples and analysis. HY critically revised the manuscript. All authors read and approved the final manuscript.
DISCLOSURE
There are no conflicts of interest to declare.
Approval of the research protocol: Ethical approval was obtained from the Beijing Haidian Hospital (approval number 2018036), and this study adhered to the tenets of the Declaration of Helsinki.
Informed Consent: Informed consent was obtained from all participants.
Approval date of Registry and the Registration No. of the study/trial: N/A.
Animal Studies: N/A.
FUNDING
Financial support was provided by the Bethune‐Merck Diabetes Research Foundation (G2018030).
Supporting information
Table S1. Results of total RNA purity and concentration determination in patients with DR (n = 20) and T2DM patients without DR (n = 20).
Table S2. Top 25 upregulated circRNA.
Table S3. Top 25 downregulated circRNA.
Table S4. Detail information of KEGG pathway analysis.
ACKNOWLEDGMENTS
Not applicable.
DATA AVAILABILITY STATEMENT
The original contributions presented in the study are publicly available. This data can be found here: NCBI, GSE276127, https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE276127.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Table S1. Results of total RNA purity and concentration determination in patients with DR (n = 20) and T2DM patients without DR (n = 20).
Table S2. Top 25 upregulated circRNA.
Table S3. Top 25 downregulated circRNA.
Table S4. Detail information of KEGG pathway analysis.
Data Availability Statement
The original contributions presented in the study are publicly available. This data can be found here: NCBI, GSE276127, https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE276127.