Introduction: Focal segmental glomerulosclerosis (FSGS) is a common glomerulopathy with an unclear mechanism. The demand for FSGS clinical diagnostic biomarkers has not yet been met. Circular RNA (circRNA) is a novel non-coding RNA with multiple functions, but its diagnostic value for FSGS remains unexplored. This study aimed to identify circRNAs that could aid in early clinical diagnosis and to investigate their mechanisms in podocyte injury. Methods: The signature of plasma circRNAs for FSGS was identified by circRNA microarray. The existence of circRNAs was confirmed by quantitative real-time polymerase chain reaction (qRT-PCR), RNase R assay, and DNA sequencing. Plasma levels of circRNAs were evaluated by qRT-PCR. The diagnostic value was appraised by the receiver operating characteristic curve. The circRNA-miRNA-mRNA network was built with Cytoscape 7.3.2. Statistically significant differences were calculated by the Mann-Whitney U test. Results: A total of 493 circRNAs (165 upregulated, 328 downregulated) were differentially expressed in the plasma of FSGS patients (n = 3) and normal controls (n = 3). Eight candidate circRNAs were demonstrated to be circular and stable transcripts. Among them, hsa_circ_0001230 and hsa_circ_0023879 were significantly upregulated in FSGS patients (n = 29) compared to normal controls (n = 51). The areas under the curve value of hsa_circ_0001230 and hsa_circ_0023879 were 0.668 and 0.753, respectively, while that of the two-circRNA panel was 0.763. The RNA pull-down analysis revealed that hsa_circ_0001230 and hsa_circ_0023879 could sponge hsa-miR-106a. Additionally, hsa_circ_0001230 and hsa_circ_0023879 positively regulated hsa-miR-106a target genes phosphatase and tensin homolog (PTEN) and Bcl-2-like protein 11 (BCL2L11) in podocytes. Conclusion: hsa_circ_0001230 and hsa_circ_0023879 are novel blood biomarkers for FSGS. They may regulate podocyte apoptosis by competitively binding to hsa-miR-106a.

Focal segmental glomerulosclerosis (FSGS) is a common chronic glomerulopathy caused by podocyte injury and characterized by significant proteinuria. In adults and children, FSGS is the most common cause of end-stage renal disease and hormone-resistant nephrotic syndrome, accounting for about 20% of nephrotic syndrome in children [1]. FSGS can be divided into idiopathic or primary FSGS and secondary FSGS. Secondary FSGS includes adaptive, genetic, inflammation/infection, and drug-related FSGS [2]. FSGS is a histological lesion of podocyte structure defects, and the extent of podocyte loss is highly correlated with the severity of proteinuria and decrease in renal function [3, 4]. As a result, preventing podocyte injury and loss is critical for delaying or resisting the progression of FSGS.

Circular RNAs (circRNAs) are a special group of non-coding RNAs with a covalently closed circle structure. As opposed to traditional linear RNAs, circRNAs are produced by pre-mRNA backsplicing of exonic, intronic, or intergenic areas and are resistant to exoribonuclease [5]. Multitudinous studies have revealed that circRNAs are dysregulated in plenty of human diseases, such as gastric carcinoma, esophageal squamous cell carcinomas, bladder cancer, hepatic ischemia/reperfusion injury, renal cell carcinoma and acute kidney injury [6‒10]. Additionally, circRNAs act as competitive endogenous RNAs, parental gene transcriptional regulators, RNA splicing regulators, microRNA (miRNA) decoys or sponges, protein scaffolding, protein baits or antagonists, protein recruitment, and even templates for translation to participate in multiple vital biological pathways [11‒16].

The plasma miRNA panel has been reported to be a potentially independent prognostic and diagnostic indicator for FSGS [17]. In the pathogenesis of FSGS, miRNAs regulate podocyte function. However, there are few reports on circRNAs regulating FSGS. This research aims to authenticate differentially expressed circRNAs (DECs) in FSGS, identify their regulatory network, and assess their potential to influence podocyte function.

Human Samples

The normal plasma samples (n = 51) and the FSGS plasma samples (n = 29) used in this research were gathered from Southwest Hospital of Army Medical University (Chongqing, China) in 2020. All recruited samples were aged 16–70 years and did not have pregnancy, lactation, severe heart, brain, liver or hematopoietic system diseases, cancer or concurrence of other chronic diseases. And the patients with FSGS were diagnosed with idiopathic FSGS by clinical examination and renal pathological biopsy. The clinical data of the patients was shown in online supplementary Table S1 (for all online suppl. material, see https://doi.org/10.1159/000538825).

CircRNA Expression Profile Analysis

The plasma circRNA of 3 patients with FSGS and three normal controls were analyzed using Human CircRNA Array v2 (CapitalBio, Beijing, China). CircRNAs that met the cutoff criteria, |FC| > 2.0 (FC, fold change) and p < 0.05, were considered as differentially expressed circRNAs. The heatmap of.

DECs was plotted using Tbtools (Toolbox for Biologists) v1.098689. The volcano map of DECs was plotted by https://www.bioinformatics.com.cn, an online platform for data analysis and visualization.

Screening of DECs

Candidate circRNAs were the DECs which have gene symbols, a relatively high expression level (raw signal >200), and reasonable length (<5,000 bp) in plasma were screened (shown in online suppl. Table S2).

Definition of Differentially Expressed Genes

Four microarrays (GSE104066, GSE108109, GSE108112, and GSE133288) extracted from the Gene Expression Omnibus (GEO) Datasets (https://www.ncbi.nlm.nih.gov/geo/) were analyzed. The differentially expressed genes (DEGs) were defined using the GEO2R Analysis tool (https://www.ncbi.nlm.nih.gov/geo/geo2r/). Genes that met the cutoff criteria, p < 0.05 and |logFC| > 1.0, were recognized as DEGs.

Venn Analysis

Each dataset was carried out statistical analysis, and the intersections were defined using the Venn diagram web tool (https://www.bioinformatics.psb.ugent.be/webtools/Venn/).

Gene Ontology Function and Kyoto Encyclopedia of Genes and Genomes Pathway Analysis

Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis of the parental genes of 89 DECs and DEGs were performed by Sangerbox 3.0 (http://test.sangerbox.com/home.html), and the data was shown in online supplementary Tables S3–S4. p < 0.05 and false-discovery rate < 0.1 were considered statistically significant. Sangerbox 3.0 and the bioinformatics online platform (http://www.bioinformatics.com.cn) were used to plot enrichment bubble map and histogram, respectively.

Weighted Correlation Network Analysis

The scale-free co-expression network was built with Weighted Correlation Network Analysis (WGCNA) tool of Sangerbox 3.0. The genes with similar expression profiles were divided into gene modules with a sensitivity of 3 and a minimal module size ≥30. The gray module was considered to be a set of genes that could not be assigned to any module.

Gene Set Enrichment Analysis

Sangerbox 3.0 was used for Gene Set Enrichment Analysis (GSEA). For the overall analysis, the samples were divided into normal and FSGS groups. For a single gene, according to the expression level of BCL2L11 or PTEN, the samples were divided into low expression group (<50%) and high expression group (≥50%). p < 0.05 and the false-discovery rate <0.25 were considered statistically significant. The enrichment information of pathways was shown in online supplementary Tables S5–S7.

Prediction of circRNA-Binding miRNA and miRNA Target Genes

CircRNA-binding miRNAs were predicted by using online tools Circular RNA Interactome (https://circinteractome.nia.nih.gov/index.html) and miRanda (http://www.bioinformatics.com.cn/local_miranda_miRNA_target_prediction_120). The target genes of these miRNAs were predicted by using TargetScanHuman online tool (https://www.targetscan.org/vert_72/).

Protein-Protein Interaction Network and circRNA-miRNA-mRNA Network Construction

Protein-protein interaction (PPI) networks were constructed by using the DEGs and hub genes in different modules. The network node information of salmon and cyan modules was shown in online supplementary Tables S8 and S9, respectively. The PPI and circRNA-miRNA-mRNA networks were visualized using Cytoscape 3.7.2.

RNA Isolation and Quantitative Real-Time Polymerase Chain Reaction

Total RNA was isolated from 200 μL of plasma using miRNeasy Serum/Plasma Kit (217184, Qiagen, Germany). To each sample, 50 nm miDETECT cel-miR-39-3p Standard RNA (synthetic Caenorhabditis elegans miRNA) (miRB0000010, RiboBio, Guangzhou, China) was added as the spike-in normalization control. For cel-miR-39 detection, RNA was reverse transcribed using Premix ExTaq (RR390A, TaRaRa, Dalian, China) and a TaqMan microRNA Reverse Transcription Kit (4366596, Thermo Fisher, USA). SYBR Green qPCR Master Mix (HY-K0501, MCE, Shanghai, China) was used for quantitative real-time polymerase chain reaction (qRT-PCR). Method of 2−ΔΔCT was used to estimate the relative expression levels of circRNAs [18]. The primer sequences were shown in online supplementary Table S10.

RNase R Assay

Total RNA extracted from a normal plasma sample was treated with or without RNase R and was incubated at 37°C for 15 min (RNR07250, Epicentre, USA).

Cell Culture

RPMI 1640 medium added to 1% 100 ITS Liquid Media Supplement (13146, Sigma-Aldrich, Germany) and 10% fetal calf serum (10099141, Gibco, Australia), was used to culture the immortalized human podocyte. The podocytes were cultured at 33°C with 5% CO2 for proliferation and at 37°C with 5% CO2 for differentiation.

Cell Transfection Assays

SiRNAs of hsa_circ_0001230, hsa_circ_0023879 and negative controls were synthesized by Ribobio (Guangzhou, China). For cell transfection assays, 2 × 105 cells were seeded into 12 well plates overnight, and then were transfected with 100 nmol siRNAs using Lipofectamine 2000 (11668019; Invitrogen, Carlsbad, CA, USA). The medium was then replaced after incubation for 4–6 h. Cells were collected for RNA isolation 24 h after transfection, while protein extraction 48 h after transfection. The siRNA sequences were shown in online supplementary Table S11.

Sample Size Calculation

PASS 15.0.5 software was used for minimum sample size calculation. According to the pre-experimental results, the areas under the curve (AUC) of hsa_circ_0023879 and hsa_circ_0001230 were 0.750 and 0.740, respectively (shown in online suppl. Fig. S1a, b). 95% confidence (type I error α = 0.05), tolerable error β of 0.10 and group allocation of equal were set to calculate the minimum sample size. And the results showed that the minimum sample size for both the FSGS group and the control group was 27 (shown in online suppl. Fig. S1c, d).

RNA Pull-Down Assay

The Waals RNA Pull-Down Kit (W21RPF01, Waals, Chongqing, China) was used for an RNA pull-down assay to verify the binding of the two circRNAs to miR-106a. According to the manufacturer’s instructions, 2 × 107 cells were lysed by Waals Protein IP (Co-IP) Lysis Buffer (PILB01-100 mL, Waals, Chongqing, China) on ice for 20 min. Cell lysates were incubated with 40 nm biotin-labeled hsa_circ_0001230/hsa_circ_0023879 probes or control probes (Gene Pharma, Shanghai, China) at room temperature for 1 h to pull-down hsa_circ_0001230/hsa_circ_0023879, and then it was incubated with streptavidin magnetic beads at room temperature for 1 h. Following bead separation and washing, RNA complexes that specifically combined with hsa_circ_0001230/hsa_circ_0023879 were subsequently extracted and were purified using Waals RNA Purification Kit (W21RP01, Waals, Chongqing, China). The probe sequences for RNA pull-down were listed in online supplementary Table S12.

Western Blot

The protein samples were extracted using RIPA lysis buffer (P0013B, Beyotime, Shanghai, China) and were separated by SDS-PAGE electrophoresis. The membranes were blocked with Protein-free QuickBlock Solution (G2052-500 ML; Servicebio, Wuhan, China) for 15 min at room temperature, and then incubated with PTEN rabbit mAb (1:1,000, A21892; Abclonal, Wuhan, China), BCL2L11 rabbit mAb (1:1,000, A19702; Abclonal, Wuhan, China) and GAPDH (1:50,000, 60004-1-Ig; Proteintech, Wuhan, China) primary antibodies overnight at 4°C. The membranes were washed with PBST and incubated with HRP Goat Anti-Rabbit IgG (H + L) (1:5,000, AS014; Abclonal, Wuhan, China) or HRP Goat Anti-Mouse IgG (H + L) (1:5,000, AS003; Abclonal, Wuhan, China) secondary antibodies at room temperature for 1 h. The membranes were incubated with ECL Enhanced Kit (RM00021; Abclonal, Wuhan, China) for visualization.

Statistical Analysis

GraphPad Prism 8.4.2 was used for statistical analyses. Statistically significant differences between normal control and FSGS groups were analyzed using Mann-Whitney U test. The combined circRNA panel was constructed by using Logistic regression analysis. The specificity and sensitivity of the circRNA biomarkers were evaluated by receiver operating characteristic (ROC) curves. p < 0.05 indicated statistical significance.

Identification of DECs in FSGS Plasma Samples

To identify the plasma circRNAs expression profile for FSGS, the circRNA microarray was applied to compare the plasma circRNA of 3 FSGS patients with three normal plasma samples. The study design flowchart is presented in Figure 1. A total of 493 DECs were identified, including 165 upregulated circRNAs and 328 downregulated circRNAs (shown in Fig. 2a, b). Their transcripts were mainly distributed on chromosome (chr) 1, followed by chr17, chr3, chr5, and chr12 (shown in Fig. 2c) and most DECs were shorter than 2,000 bp in length (shown in Fig. 2d). Among 493 DECs, 89 candidate circRNAs (83 upregulated and 6 downregulated) that have gene symbols, a relatively high expression level (raw signal >200), and reasonable length (<5,000 bp) in plasma were screened for identification and verification (shown in Fig. 3a; online suppl. Table S2). Their transcripts were mainly distributed on chr5 and chr8 (shown in Fig. 3b). CircRNAs have been reported to play an important role in cis-regulating transcription of parent genes [12]. To clarify the function of DECs, GO and KEGG analysis of the parental genes of these circRNAs were performed (shown in Fig. 3c). The results showed that these parent genes were mainly involved in the regulation of protein ubiquitination.

Fig. 1.

A flowchart of study design. An overview of experimental design to identify potential plasma circRNA biomarkers of FSGS and their mechanisms for regulating podocyte injury.

Fig. 1.

A flowchart of study design. An overview of experimental design to identify potential plasma circRNA biomarkers of FSGS and their mechanisms for regulating podocyte injury.

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Fig. 2.

Characterization of DECs in the plasma of patients with FSGS. a Heatmap of DECs. CircRNA profiling in plasma from 3 FSGS patients and three normal controls was performed by using Human CircRNA Array v2. b Volcano map of DECs. c The number of DECs on each chromosome. d The number of DECs in a range of different lengths.

Fig. 2.

Characterization of DECs in the plasma of patients with FSGS. a Heatmap of DECs. CircRNA profiling in plasma from 3 FSGS patients and three normal controls was performed by using Human CircRNA Array v2. b Volcano map of DECs. c The number of DECs on each chromosome. d The number of DECs in a range of different lengths.

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Fig. 3.

Characterization of 89 screened DECs in the plasma of patients with FSGS. a Heatmap of screened DECs. b Double tracks circos histogram of screened DECs. c GO function and KEGG pathway enrichment circle diagram of parental genes of screened DECs.

Fig. 3.

Characterization of 89 screened DECs in the plasma of patients with FSGS. a Heatmap of screened DECs. b Double tracks circos histogram of screened DECs. c GO function and KEGG pathway enrichment circle diagram of parental genes of screened DECs.

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Validation of the Presence of Candidate circRNAs

Specific divergent primers were devised to obtain PCR products for Sanger sequencing. However, some circRNAs had homologous sequences or could not be amplified using PCR, so it was difficult to design appropriate divergent primers, resulting in only eight circRNAs (hsa_circ_0001230, hsa_circ_0022304, hsa_circ_0023879, hsa_circ_0041878, hsa_circ_0083343, hsa_circ_0112102, hsa_circ_0116554, and hsa_circ_0129094) being selected for additional verification. The annotations of the eight circRNAs were shown in Figure 4a. The results of Sanger sequencing revealed that the eight circRNAs existed in plasma (shown in Fig. 4b). RNase R assay showed that they were resistant to RNase R, indicating that they are circular (shown in Fig. 4c). These results implied that the eight circRNAs were circular and stable transcripts. And circRNA array analysis showed that except for hsa_circ_0022304, other 7 circRNAs were upregulated in FSGS (shown in Fig. 4d).

Fig. 4.

Identification of eight candidate DECs. a The origin of eight DECs (hsa_circ_0001230, hsa_circ_0022304, hsa_circ_0023879, hsa_circ_0041878, hsa_circ_0083343, hsa_circ_0112102, hsa_circ_0116554, and hsa_circ_0129094). b Sanger sequencing of qRT-PCR products of eight DECs. c qRT-PCR was performed using RNase R pre-treatment products and the mock products. d The expression level of DECs in the sequencing results.

Fig. 4.

Identification of eight candidate DECs. a The origin of eight DECs (hsa_circ_0001230, hsa_circ_0022304, hsa_circ_0023879, hsa_circ_0041878, hsa_circ_0083343, hsa_circ_0112102, hsa_circ_0116554, and hsa_circ_0129094). b Sanger sequencing of qRT-PCR products of eight DECs. c qRT-PCR was performed using RNase R pre-treatment products and the mock products. d The expression level of DECs in the sequencing results.

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hsa_circ_0001230 and hsa_circ_0023879 Were Upregulated in FSGS Plasma Samples

Twelve FSGS and sixteen normal plasma samples were performed qRT-PCR analysis to preliminarily determine the expression levels of eight circRNAs in FSGS patients. Among them, hsa_circ_0001230 and hsa_circ_0023879 were found to be significantly increased in FSGS patients, but the expression of hsa_circ_0041878 and hsa_circ_0112102 showed no significant difference (shown in Fig. 5a–d). And the cycle threshold values of the other four circRNAs (hsa_circ_0022304, hsa_circ_0083343, hsa_circ_0116554 and hsa_circ_0129094) were too high to reflect the reality, leading to the removal of these circRNAs from the candidates. Subsequently, ROC analysis was performed on hsa_circ_0001230 and hsa_circ_0023879, and the AUC was found to be 0.740 and 0.750 (shown in online suppl. Fig. S1a, b), respectively. The calculation results of PASS 15 software showed that the minimum sample size was 27 in both FSGS and control groups (shown in online suppl. Fig. S1c, d).

Fig. 5.

Determination of expression levels of eight candidate DECs in FSGS patients. Relative expression level of cicrRNAs in plasma of FSGS patients (n = 12) and normal samples (n = 16), including hsa_circ_0001230 (a), hsa_circ_0023879 (b), hsa_circ_0041878 (c), and hsa_circ_0112102 (d). *p < 0.05. ns, no significance.

Fig. 5.

Determination of expression levels of eight candidate DECs in FSGS patients. Relative expression level of cicrRNAs in plasma of FSGS patients (n = 12) and normal samples (n = 16), including hsa_circ_0001230 (a), hsa_circ_0023879 (b), hsa_circ_0041878 (c), and hsa_circ_0112102 (d). *p < 0.05. ns, no significance.

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In total, twenty-nine FSGS and fifty-one normal plasma samples were used to detect the expression of hsa_circ_0001230 and hsa_circ_0023879 by qRT-PCR. hsa_circ_0001230 and hsa_circ_0023879 were still found to be observably increased in FSGS plasma compared with normal plasma samples and the fold changes were 2.263 and 3.063, respectively (shown in Fig. 6a, b). However, the correlation analysis indicated that there was no statistical correlation between hsa_circ_0001230 and hsa_circ_0023879 (shown in Fig. 6c). Subsequently, ROC curve analysis was performed to appraise the sensitivity and specificity of two circRNAs for FSGS diagnosis. The AUC of hsa_circ_0001230 and hsa_circ_0023879 were found to be 0.668 (95% CI, 0.539–0.798) and 0.753 (95% CI, 0.639–0.867), respectively (shown in Fig. 6d, e; online suppl. Table S13). Logistic regression was used to determine the diagnostic value of combining the two circRNAs. The AUC value of the two-circRNA panel was 0.763 (95% CI, 0.652–0.874) (shown in Fig. 6f; online suppl. Table S13). These findings suggested that hsa_circ_0001230 and hsa_circ_0023879 may be potential diagnosis index for FSGS and the two-circRNA panel had a higher diagnostic value.

Fig. 6.

Validation of expression level of hsa_circ_0001230 and hsa_circ_0023879 in the plasma of FSGS patients. Relative expression level of cicrRNAs in plasma of FSGS patients (n = 29) and normal samples (n = 51), including hsa_circ_0001230 (a), hsa_circ_0023879 (b). c Correlation analysis of hsa_circ_0001230 and hsa_circ_0023879 in FSGS. d Receiver operating characteristic (ROC) analysis of hsa_circ_0001230. e ROC analysis of hsa_circ_0023879. f ROC curve of the two-circRNA panel. All of the qRT-PCR assays used cel-miR-39 as the external reference. *p < 0.05, ***p < 0.001.

Fig. 6.

Validation of expression level of hsa_circ_0001230 and hsa_circ_0023879 in the plasma of FSGS patients. Relative expression level of cicrRNAs in plasma of FSGS patients (n = 29) and normal samples (n = 51), including hsa_circ_0001230 (a), hsa_circ_0023879 (b). c Correlation analysis of hsa_circ_0001230 and hsa_circ_0023879 in FSGS. d Receiver operating characteristic (ROC) analysis of hsa_circ_0001230. e ROC analysis of hsa_circ_0023879. f ROC curve of the two-circRNA panel. All of the qRT-PCR assays used cel-miR-39 as the external reference. *p < 0.05, ***p < 0.001.

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Construction of the circRNA-miRNA-mRNA Network

To search after the functional regulation network of the two circRNAs, the circRNA-miRNA-mRNA network was established. The miRNAs that may bind to hsa_circ_0001230 or hsa_circ_0023879, and target genes of miRNAs were predicted by online tools. A total of 46 miRNAs were predicted, of which 13 bound to hsa_circ_0001230 and the other 37 bound to hsa_circ_0023879. However, many target genes of these miRNAs are not significantly associated with FSGS. Therefore, four chips (GSE104066, GSE108109, GSE108112, and GSE133288) were screened for FSGS from GEO Datasets and the DEGs in each series were analyzed independently. It was found that 169 DEGs (69 upregulated and 100 downregulated) overlapped across the four chip series (shown in Fig. 7a, b). The results of GO and KEGG pathway enrichment analysis revealed that these DEGs primarily involved the inflammatory response and metabolism (shown in Fig. 7c–f).

Fig. 7.

Analysis of DEGs in FSGS. a Venn diagram of the overlapping upregulated genes of GSE104066, GSE108109, GSE108112, and GSE133288 data series. b Venn diagram of the overlapping downregulated genes of GSE104066, GSE108109, GSE108112, and GSE133288 data series. c Summary diagram of KEGG pathway classification of DEGs. d Biology process enrichment dot bubble of DEGs. e Cellular component enrichment dot bubble of DEGs. f Molecular function enrichment dot bubble of DEGs. g Gene clustering of co-expression modules identified by WGCNA. h Correlation analysis between each module and FSGS group or normal group. Positive numbers indicate positive correlations, negative numbers indicate negative correlations, and the absolute value of the number indicates the strength of the correlation. PPI network of the two modules with the strongest correlation with FSGS, including salmon module (i) and cyan module (j). The red nodes represent upregulated genes, green nodes represent downregulated genes, and blue represent mRNAs that were not evaluated at the level of expression. The light gray lines represent the connections between genes. k The pathway of significant enrichment in FSGS was analyzed using GSEA, including notch signaling pathway, apoptosis, VEGF signaling pathway, Wnt signaling pathway, p53 signaling pathway, mTOR signaling pathway, TGF-beta signaling pathway, phosphatidylinositol signaling system, Jak stat signaling pathway, and MAPK signaling pathway.

Fig. 7.

Analysis of DEGs in FSGS. a Venn diagram of the overlapping upregulated genes of GSE104066, GSE108109, GSE108112, and GSE133288 data series. b Venn diagram of the overlapping downregulated genes of GSE104066, GSE108109, GSE108112, and GSE133288 data series. c Summary diagram of KEGG pathway classification of DEGs. d Biology process enrichment dot bubble of DEGs. e Cellular component enrichment dot bubble of DEGs. f Molecular function enrichment dot bubble of DEGs. g Gene clustering of co-expression modules identified by WGCNA. h Correlation analysis between each module and FSGS group or normal group. Positive numbers indicate positive correlations, negative numbers indicate negative correlations, and the absolute value of the number indicates the strength of the correlation. PPI network of the two modules with the strongest correlation with FSGS, including salmon module (i) and cyan module (j). The red nodes represent upregulated genes, green nodes represent downregulated genes, and blue represent mRNAs that were not evaluated at the level of expression. The light gray lines represent the connections between genes. k The pathway of significant enrichment in FSGS was analyzed using GSEA, including notch signaling pathway, apoptosis, VEGF signaling pathway, Wnt signaling pathway, p53 signaling pathway, mTOR signaling pathway, TGF-beta signaling pathway, phosphatidylinositol signaling system, Jak stat signaling pathway, and MAPK signaling pathway.

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Four chip data series were also combined and removed the batch effect for subsequent analysis (shown in online suppl. Fig. S2). WGCNA was used to establish a scale-free co-expression network (scale-free R2 > 0.8) with a soft thresholding power of 2 and a total of 12 co-expression modules were obtained (shown in Fig. 7g; online suppl. Fig. S3). The correlation analysis between each module and FSGS group or normal group showed that salmon (0.71, p < 0.001) and cyan (−0.63, p < 0.001) were the most positive and negative modules related to FSGS, respectively (shown in Fig. 7h). The PPI network of salmon module was constructed with 64 genes, and that of the cyan module was constructed with 47 genes (shown in Fig. 7i, j). Then, GSEA was used to evaluate relevant pathways and molecular mechanisms based on gene expression profile and phenotypic grouping. The results showed that 73 pathways were remarkably enriched in the FSGS group, of which 10 pathways, notch signaling pathway [19], apoptosis [20], VEGF signaling pathway [21], Wnt signaling pathway [22], p53 signaling pathway [20, 23], mTOR signaling pathway [24, 25], TGF-beta signaling pathway [26, 27], phosphatidylinositol signaling system [28], Jak stat signaling pathway [27, 29] and MAPK signaling pathway [30], had been reported to be involved in positive regulation of FSGS (shown in Fig. 7k). The notch signaling pathway had the highest enrichment score of 0.634, which may play a crucial role in FSGS.

A circRNA-miRNA-mRNA network with 468 edges and 101 nodes was then built to evaluate the functional correlation among circRNA, miRNA, and mRNA (shown in Fig. 8). Connectivity degree showed that hsa-miR-548c-3p was the most prominent RNA (degree = 35), followed by hsa_circ_0023879 (degree = 34). The FKBP prolyl isomerase 5 (FKBP5) was the mRNA with the highest connection. It suggested that they may be involved in important functional regulation.

Fig. 8.

CircRNA-miRNA-mRNA network construction. A total of 2 circRNAs, 46 miRNAs and 53 mRNAs were involved in the network. Red nodes represent upregulated genes, green nodes represent downregulated genes and blue represent miRNAs that were not evaluated at the level of expression. The light gray lines represent the connection between mRNAs and miRNAs, and the dark gray lines represent the connection between circRNAs and miRNAs.

Fig. 8.

CircRNA-miRNA-mRNA network construction. A total of 2 circRNAs, 46 miRNAs and 53 mRNAs were involved in the network. Red nodes represent upregulated genes, green nodes represent downregulated genes and blue represent miRNAs that were not evaluated at the level of expression. The light gray lines represent the connection between mRNAs and miRNAs, and the dark gray lines represent the connection between circRNAs and miRNAs.

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hsa_circ_0001230 and hsa_circ_0023879 Probably Function as the Sponge of hsa-miR-106a

While exploring the regulatory mechanism, it was noticed that hsa-miR-106a was predicted to bind to hsa_circ_0001230 and hsa_circ_0023879 in the circRNA-miRNA-mRNA network (shown in Fig. 8). Our previous study reported that hsa-miR-106a suppressed podocyte apoptosis by targeting chemokine (C-X-C motif) ligand 14 (CXCL14), phosphatase and tensin homolog (PTEN), and Bcl-2-like protein 11 (BCL2L11) [17]. To evaluate the possibility of hsa-miR-106a binding to two circRNAs, the origin of two circRNAs was reanalyzed. The annotations showed that hsa_circ_0001230 was of a 251-nt length and originated from exon 2 and exon 3 of DDX17 (DEAD-box helicase 17), which was located at chr22:38895404-38897285, whereas hsa_circ_0023879 was of a 778-nt length and originated from exon 2, 3, and 4 of SYTL2 (synaptotagmin like 2), which was located at chr11:85456679-85469157 (shown in Fig. 4a). This indicated that hsa_circ_0001230 and hsa_circ_0023879 may sponge with hsa-miR-106a. It was found that hsa_circ_0001230 can partially match the seed sequence of hsa-miR-106a-3p (3′-GUAACGU-5′) and hsa-miR-106a-5p (3′-CGUGAAA-5′), while hsa_circ_0023879 can fully match the seed sequence of hsa-miR-106a-5p and partially match the seed sequence of hsa-miR-106a-3p by using MiRanda online prediction tool (shown in Fig. 9a, b). Subsequently, RNA pull-down experiments confirmed that hsa_circ_0001230 and hsa_circ_0023879 could bind to has-miR-106a-5p but not hsa-miR-106a-3p, suggesting two circRNAs may regulate podocyte apoptosis mainly through competitive binding of has-miR-106a-5p (shown in Fig. 9c, d; online suppl. Fig. S4). When siRNA of hsa_circ_0001230 or hsa_circ_0023879 were transiently transfected into the human podocyte, the target genes of hsa-miR-106a, PTEN, and BCL2L11 were both significantly downregulated (shown in Fig. 9e–k). This suggested that both hsa_circ_0001230 and hsa_circ_0023879 may participate in regulating podocyte apoptosis. Moreover, GSEA was performed to analyze the pathways regulated by PTEN and BCL2L11 in FSGS. The results revealed that PTEN negatively regulated spliceosome and positively regulated melanoma, while BCL2L11 negatively regulated notch signaling pathway, Wnt signaling pathway, apoptosis, and TGF-beta signaling pathway (shown in Fig. 9i–m, online suppl. Tables S6, S7).

Fig. 9.

hsa_circ_0001230 and hsa_circ_0023879 probably functioned as the sponge of hsa-miR-106a. Schematic diagram of hsa_circ_0001230 (a) or hsa_circ_0023879 (b) sponge hsa-miR-106a. Enrichment of has-miR-106a-5p by RNA pull-down using hsa_circ_0001230 (c) or hsa_circ_0023879 (d) probes. e The expression of hsa_circ_0001230 in human podocyte cells transfected with siRNA of hsa_circ_0001230 or negative control. f The expression of hsa_circ_0023879 in human podocyte cells transfected with siRNA of hsa_circ_0023879 or negative control. Relative expression of PTEN (g) and BCL2L11 (h) in podocytes after hsa_circ_0001230 knockdown. Relative mRNA expression level of PTEN (i) and BCL2L11 (j) in podocytes after hsa_circ_0023879 knockdown. k The protein levels of PTEN and BCL2L11 in podocytes after hsa_circ_0001230 or hsa_circ_0023879 knockdown. The pathways regulated by PTEN (l) or BCL2L11 (m) in FSGS. *p < 0.05, ***p < 0.001. L, low expression group; H, high expression group.

Fig. 9.

hsa_circ_0001230 and hsa_circ_0023879 probably functioned as the sponge of hsa-miR-106a. Schematic diagram of hsa_circ_0001230 (a) or hsa_circ_0023879 (b) sponge hsa-miR-106a. Enrichment of has-miR-106a-5p by RNA pull-down using hsa_circ_0001230 (c) or hsa_circ_0023879 (d) probes. e The expression of hsa_circ_0001230 in human podocyte cells transfected with siRNA of hsa_circ_0001230 or negative control. f The expression of hsa_circ_0023879 in human podocyte cells transfected with siRNA of hsa_circ_0023879 or negative control. Relative expression of PTEN (g) and BCL2L11 (h) in podocytes after hsa_circ_0001230 knockdown. Relative mRNA expression level of PTEN (i) and BCL2L11 (j) in podocytes after hsa_circ_0023879 knockdown. k The protein levels of PTEN and BCL2L11 in podocytes after hsa_circ_0001230 or hsa_circ_0023879 knockdown. The pathways regulated by PTEN (l) or BCL2L11 (m) in FSGS. *p < 0.05, ***p < 0.001. L, low expression group; H, high expression group.

Close modal

FSGS is a lesion caused by podocyte injury and is characterized by focal (partial) and segmental (partial) sclerosis of the glomeruli [31]. It is hard to assess histological injury in unitary kidney sections because focal sclerosis lesions exist in multiple glomeruli but influence only 12.5% of the total glomerular volume [32]. The emergence of biomarkers aids in the clinical diagnosis of FSGS. Aside from proteins that are abnormal expressed in the podocyte of patients with FSGS, some non-coding RNAs have been studied to aid in the diagnosis of FSGS in recent years. Previous studies have shown that urinary and plasma miRNAs can act as biomarkers for detecting primary FSGS [17, 33‒37].

Currently, plenty of studies have shown that circRNAs were capable of regulating the pathophysiology of various illnesses. Plasma circRNAs could function as biomarkers in certain diseases such as cancers, acute ischemic stroke, and rheumatoid arthritis [38‒42]. Also, the function of circRNAs in renal disease progression has been gradually recognized in recent years. A previous report suggested that circZNF609 can serve as a therapeutic target for FSGS [43]. However, there are no reports on the diagnosis value of plasma circRNAs in FSGS.

In this study, circRNA Transcriptome sequencing was performed in the plasma of 3 FSGS patients and three normal controls. 493 DECs were identified by circRNA microarray and 83.36% of which was less than 2,000 bp in length (shown in Fig. 2d). Eighty-nine DECs were screened for subsequent validation. The results of GO and KEGG analysis suggested that these 89 circRNAs may be involved in the regulation of protein ubiquitination (shown in Fig. 3c). However, out of 89 DECs, only 8 circRNAs (hsa_circ_0001230, hsa_circ_0022304, hsa_circ_0023879, hsa_circ_0041878, hsa_circ_0083343, hsa_circ_0112102, hsa_circ_0116554, and hsa_circ_0129094) were proved to be circular and stable transcripts (shown in Fig. 4).

Further investigation revealed that only hsa_circ_0001230 and hsa_circ_0023879 were significantly upregulated in FSGS with the fold changes of 2.263 and 3.063, respectively (shown in Fig. 6a, b). The FSGS diagnosis value was evaluated by performing a ROC curve analysis, and hsa_circ_0001230 and hsa_circ_0023879 produce the AUC value of 0.668 and 0.753, respectively (shown in Fig. 6d, e). However, although hsa_circ_0001230 had no significant correlation with hsa_circ_0023879 in FSGS (shown in Fig. 6c), the two-circRNA panel produced an AUC value of 0.763 (shown in Fig. 6f), which was superior to either of them.

Subsequently, targeting these two circRNAs, a circRNA-miRNA-mRNA network consisting of 101 nodes and 468 edges was constructed, with a total of 2 circRNAs, 46 miRNAs, and 53 mRNA involved, to explore the relationship between mRNA, miRNA and these two circRNAs in FSGS. Interestingly, both hsa_circ_0001230 and hsa_circ_0023879 were predicted to bind to hsa-miR-106a, which was reported to be downregulated in FSGS and suppressed podocyte apoptosis by targeting CXCL14, PTEN, and BCL2L11 [17]. However, hsa_circ_0001230 and hsa_circ_0023879 have not been reported so far. Therefore, the origin of hsa_circ_0001230 and hsa_circ_0023879 were then analyzed. It was found that hsa_circ_0001230 originates from exon 2 and exon 3 of DDX17, with a 251-nt length, and partially matched with the seed sequence of hsa-miR-106a-3p and hsa-miR-106a-5p, whereas hsa_circ_0023879 originates from exon 2, 3, and 4 of SYTL2, with a 778-nt length, which is exactly matched with the seed sequence of hsa-miR-106a-5p and partially matched with the seed sequence of hsa-miR-106a-3p (shown in Fig. 4a, Fig. 9a, b). These suggested that their function in FSGS was related to hsa-miR-106a. It is well known that PTEN and BCL2L11 are important proteins that regulate apoptosis [44‒46]. Notably, when hsa_circ_0001230 or hsa_circ_0023879 were knocked down in the human podocyte, the expression level of PTEN and BCL2L11 were significantly downregulated (shown in Fig. 9e–k). These data suggested that both hsa_circ_0001230 and hsa_circ_0023879 may regulate PTEN and BCL2L11 mediated podocyte apoptosis by sponging with hsa-miR-106a.

In addition, FKBP5 as the mRNA with the highest connectivity degree was downregulated in FSGS. It had been reported that the knockdown of FKBP5 attenuated the sepsis-induced cell apoptosis and the inflammatory response in mice with acute kidney injury by inactivating the nuclear factor kappaB signaling pathway [47]. The correlation analysis of co-expression network showed that FKBP5 belonged to salmon module (shown in Fig. 7g, h), which was positively correlated with FSGS (online suppl. Table S8). These suggested that FKBP5 may play an important role in negative regulation of FSGS, but the specific mechanism still needed to be explored.

This study identified hsa_circ_0001230 and hsa_circ_0023879 as novel diagnostic biomarkers for FSGS. However, the sample size used was still relatively small, and more eligible samples need to be collected to verify the reliability and potential role of circRNAs biomarkers. The two-circRNA panel may be more valuable for clinical diagnosis of FSGS. In addition, both hsa_circ_0001230 and hsa_circ_0023879 may regulate podocyte apoptosis in FSGS by competitively binding to hsa-miR-106a. And functional validation is expected to be carried out through multiple experimental methods in future studies.

We thank Prof. MoinA Saleem (University of Bristol, Bristol, UK) for providing the human podocyte cell line. We thank Biobank of Southwest Hospital, Army Medical University for the preservation of plasma samples. We thank Bullet Edits Limited for the linguistic editing and proofreading of the manuscript.

Written informed consent was obtained from all patients before being enrolled in the study. For underaged human participants, written informed consent was obtained from the participants’ parent/legal guardian/next of kin to participate in the study. The study was approved by (the Ethics Committee of the First Affiliated Hospital of Army Military Medical University, PLA), approval number (B) KY2021059.

The authors have no conflicts of interest to declare.

This study was supported by Chongqing Natural Science Foundation Project (cstc2020jcyj-msxmX0022, and cstc2020jcyj-msxmX0337), Chongqing Talents-Exceptional Young Talents Project (CQYC202005044 and cstc2021ycjh-bgzxm0094), Future Medical Youth Innovation Team Project of Chongqing Medical University (W0042), Science-Health Joint Medical Scientific Research Project of Chongqing (2018ZDXM018), Construction of Clinical Key Specialty Infectious Diseases Department in the Whole Army (51561Z23785), and Clinical technology Innovation Cultivation Project of Army Medical University (CX2019LC104).

H.-W.Z. and B.X. designed the study. L.-Y.R., W.L., H.-H.Z., J.L., H.-P.L., L.-L.X., and M.G. performed the experiments. H.-W.Z., B.X., L.-Y.R., W.L., and H.-H.Z. participated in data analysis and writing the manuscript. J.L., L.-Y.Z., H.-P.L., L.-L.X., L.-P.C., Q.-X.L., Y.-H.H., and M.G. contributed reagents/materials/analytic tools and sample collection. All authors reviewed and approved the manuscript.

Additional Information

Lingyu Ran, Wei Li and Huhai Zhang contributed equally to this work.

All data generated or analyzed during the study are included in this article and its online supplementary material. Further inquiries can be directed to the corresponding author.

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