To explore the role of hsa_circ_0053004/hsa-miR-646/CBX2 in diabetic retinopathy based on bioinformatics analysis and experimental verification

Bioinformatics analysis

The datasets of GSE193974 and GSE185011 were from the GEO database (https://www.ncbi.nlm.nih.gov/geo/). The downstream genes of hsa_circ_0053004 were predicted by Circular RNA Interactom (https://circinteractome.nia.nih.gov/), miRDB (https://mirdb.org/custom.html) and circBank (http://www.circbank.cn/searchCirc.html) databases. The target genes of hsa-miR-646 were predicted by the TargetScan8.0 (https://www.targetscan.org/) and miRDB databases. The binding site of hsa_circ_0053004 and hsa-miR-646 was from the Circular RNA Interactom database. The binding sequence of hsa-miR-646 and CBX2 mRNA was predicted by the TargetScan 8.0 database. The gene ontology (GO) analysis was performed in a Weishengxin platform (https://www.bioinformatics.com.cn/).

Patients

All participants in this retrospective study were from Army Center of PLA, including 168 patients with DM, 110 patients with NDR, and 102 patients with PDR. The clinical characteristics of all subjects were collected in Supplementary Table 1. The informed consent forms signed by all the volunteers and the samples from them were in good condition. This study was approved by the Ethics Committee of Army Center of PLA (No.20190201) and performed in line with the principles of the Declaration of Helsinki.

Cells

HRMECs were from Pricella biological company (China) and were cultured with the dedicated complete medium from the same company, at a 37 °C incubator with 5 % CO₂. The cells were cultured with 5.5 mM glucose (normal glucose group, NG) or 25 mM glucose (high glucose group, HG).

RT-qPCR

Total RNA was isolated from cells by TRIzol Reagent (Invitrogen, USA). qPCR was performed in an ABI 7,500 system using SYBR^®Green Realtime PCR Master Mix (Toyobo Biologics, China). The relative expression of genes was calculated by the ΔΔCT method. Hsa_circ_0053004 and CBX2 were normalized using GAPDH, and hsa-miR-646 was normalized by U6.

Detection of cell viability

The Cell Counting Kit-8 (CCK-8) kit (Solarbio, China) was used to assess cell viability. The 10 % CCK-8 solution prepared with the medium was added to the cells and stood still for 3 h. The absorbance at 450 nm was detected by an enzymoleter. The cell viability was calculated according to the instructions.

Detection of cell migration

Transwell assay was used to evaluate cell migration. Cells, starved for 12 h, were diluted with FBS-free medium containing 0.1 % BSA to a solution with a concentration of 1 × 10⁵. The Transwell chamber (Corning, USA) containing 200 μL cell fluid was incubated in a 24-well plate containing the complete medium for 12 h. The migrated cells were fixed and stained with 4 % paraformaldehyde and 0.1 % crystal violet, respectively. Finally, the migrated cells were counted.

Dual luciferase reporter gene assay

Wild-type (wt) and mutant (mut) dual luciferase reporter vectors of hsa_circ_0053004 or CBX2 were co-transfected into HRMECs with hsa-miR-646 mimic or negative control (NC) and incubated for 24 h. Luciferase activity was checked by a luciferase reporter gene kit (Zeye Biotechnology LTD., China).

Western blotting

The protein samples were extracted using cell lysate. The protein concentration was detected by the BCA kit (Solarbio). Proteins were isolated by SDS-PAGE. The proteins were then transferred to the PVDF membrane (Millipore, Germany). PVDF membrane was incubated in 5 % skim milk powder for 1 h at room temperature. Then it was placed in the primary antibody to oscillate overnight at 4 °C. PVDF membrane was cleaned with TBST buffer for three times, then placed in the second antibody to incubate for 2 h at room temperature. Finally, the protein signal was detected by a chemiluminescence apparatus.

Evaluation of the predictive value of genes for DR

SPSS software was used to construct the receiver operating characteristic (ROC) curve and perform binary logistic regression analysis to evaluate the predictive value of genes for DR.

Statistical analysis

All data were analyzed using GraphPad Prism software and expressed as mean ± SD. The Student’s t-test was applied to measure the difference between the two groups. One-way ANOVA was used for multiple groups. p<0.05 was to be statistically remarkable.

Hsa_circ_0053004 promoted HG-induced enhancement of HRMEC function

There were 584 circRNAs differentially expressed in patients with DM or DR, identified by GEO2R analysis in the GSE193974 dataset, including 115 down-regulated and 469 up-regulated (Figure 1A). Hsa_circ_0053004 expressed with the most multiple change (p=0.003, log2 FC=3.23), so we were interested in it. Hsa_circ_0053004 was up-regulated in the blood of DR patients, and it represented the more serious retinopathy (Figure 1B). HG increased the hsa_circ_0053004 expression in HRMECs (Figure 1C). The small interfering RNA of hsa_circ_0053004 (si-circ_0053004) significantly decreased the expression of hsa_circ_0053004, while its plasmid (ov-circ_0053004) obviously increased its level (Figure 1D). The down-regulation of hsa_circ_0053004 hindered the enhancement of HRMEC function induced by HG, and its up-regulation aggravated the effects of HG (Figure 1E and F).

Figure 1:

Hsa_circ_0053004 in HRMEC function. (A) Differentially expressed circRNAs between patients with DR and DM from the GSE19397 dataset. (B) Expression of hsa_circ_0053004 in the blood of patients. (C) Effect of HG on the expression of hsa_circ_0053004. (D) Efficiency of si-circ_0053004 and ov-circ_0053004. Effect of hsa_circ_0053004 on the cell viability (E) and migration (F) of HRMECs. **p<0.01; ***p<0.001.

Hsa-miR-646 eliminated the enhancement of hsa_circ_0053004 on HRMEC function

The downstream miRNAs of hsa_circ_0053004 were predicted by different databases, in which hsa-miR-646 was shared (Figure 2A). Bioinformatics analysis revealed the binding sequence of hsa_circ_0053004 and hsa-miR-646 (Supplementary Figure 1A). Hsa-miR-646 significantly reduced the luciferase activity of cells transfected with the wt-circ_0053004 luciferase vector; however, no significant changes were observed in cells transfected with mut-circ_0053004 (Figure 2B). Knockdown of hsa_circ_0053004 decreased the level of hsa-miR-646 in cells, and its overexpression increased the hsa-miR-646 level (Figure 2C).

Figure 2:

Hsa_circ_0053004 promoted the function of HRMECs by regulating hsa-miR-646. (A) The downstream miRNAs of hsa_circ_0053004 predicted by different databases. (B) Effect of hsa-miR-646 mimic on the luciferase activity of HRMECs transfected with wt-circ_0053004 or mut-circ_0053004. (C) Hsa_circ_0053004 regulated the level of hsa-miR-646 in HRMECs. (D) Hsa-miR-646 level in the blood of DM and DR patients (E) Effect of HG on hsa-miR-646 expression. (F-G) Efficiency of hsa-miR-646 mimic and inhibitor. (H-I) Effect of hsa-miR-646 on the cell viability and migration of HRMECs. **p<0.01; ***p<0.001.

The expression of hsa-miR-646 was deficient in the blood of DR patients (Figure 2D). HG decreased the hsa-miR-646 expression of HRMECs (Figure 2E). Hsa-miR-646 mimic (mim-646) and inhibitor were introduced for functional study (Figure 2F and G). Overexpression of hsa-miR-646 reversed the increased ability to proliferate and migrate in HRMECs induced by HG, and inhibition of hsa-miR-646 contributed to HG-induced cell proliferation and migration. Moreover, the increase of hsa-miR-646 level reversed the enhanced function of HRMECs induced by ov-circ_0053004 transfection (Figure 2H and I), suggesting that hsa_circ_0053004 promoted HRMECs to grow and migrate by negatively regulating hsa-miR-646.

CBX2 was the target of hsa-miR-646

There were 9,088 and 880 downstream genes of hsa-miR-646 were predicted by the TargetScan 8.0 database and miRDB database, respectively, among which 27 genes were identified to be the differentially expressed gene in DR and DM patients through the GEO2R analysis in the GSE185011 dataset (Supplementary Figure 1B). The GO analysis results of these 27 genes showed that CBX2 was a component of the polycomb repressive complex 1 (PRC1) (Figure 3A). Other components of PRC1 have been reported to be associated with insulin sensitivity [14], so we were very interested in the role of CBX2 in DR. The gene expression profile in the GSE185011 dataset revealed that CBX2 expressed highly in patients with DR compared to DM patients (Figure 3B), indicating that CBX2 may be a risk factor for DR. The binding sequence of hsa-miR-646 and CBX2 mRNA was displayed in the TargetScan 8.0 database (Supplementary Figure 1C). The mimic of hsa-miR-646 reduced luciferase activity in cells transfected with wt-CBX2, but hardly affected cells transfected with mut-CBX2 (Figure 3C). Moreover, hsa-miR-646 mimic significantly inhibited CBX2 expression (Figure 3D and E).

Figure 3:

CBX2 was a target gene of hsa-miR-646 (A) The GO analysis of common genes. (B) The expression map of common genes in GSE185011 dataset. (C) Effect of hsa-miR-646 mimic on the luciferase activity of HRMECs transfected with wt-CBX2 or mut-CBX2 (D-E) Hsa-miR-646 regulated CBX2 expression. ***p<0.001.

CBX2 aggravated HG-induced enhanced function of HRMECs

The expression of CBX2 was up-regulated in the blood of DR patients (Figure 4A). HG led to increased expression of CBX2 (Figure 4B and C). Transfecting plasmid of CBX2 (ov-CBX2) markedly promoted its expression (Figure 4D and E), which aggravated the HG-induced enhancement in HRMEC proliferation and migration, which was reversed by overexpression of hsa-miR-646 (Figure 4F and G).

Figure 4:

CBX2 aggravated HG-induced enhancement of HRMEC function. (A) CBX2 expression in the blood of DM and DR patients. (B-C) HG promoted the CBX2 expression. (D-E) The working efficiency of ov-CBX2. (F-G) Effects of the co-transfection of ov-CBX2 and hsa-miR-646 mimic on the HRMEC viability and migration. ***p<0.001.

Prediction function of hsa_circ_0053004/hsa-miR-646/CBX2 axis for DR

The ROC curve showed that hsa_circ_0053004, hsa-miR-646, and CBX2 were good indicators for predicting the occurrence of DR, with AUC of 0.872, 0.805, and 0.817, respectively (Figure 5A–C). The combination of three indicators showed a higher accuracy in predicting the occurrence of DR (AUC = 0.942) (Figure 5D). In addition, multivariate logistic regression analysis showed that hsa_circ_0053004, hsa-miR-646, and CBX2 were independent influencing factors of DR (Table 1). Among them, hsa_circ_0053004 and CBX2 were risk factors, while hsa-miR-646 was a protective factor for DR.

Figure 5:

Prediction function of hsa_circ_0053004/hsa-miR-646/CBX2 axis for DR. The ROC curve of hsa_circ_0053004 (A), hsa-miR-646 (B), CBX2 (C), and their combination (D) in predicting DR. AUC, area under curve.