CircRNA expression profiles in ICH patients in the discovery samples
To determine the expression profile of circRNAs associated with the occurrence and development of ICH, we first carried out RNA sequencing in the discovery samples (Fig. 1). The demographics and characteristics of the patients and matched controls are shown in Table 1. A total of 37,637 circRNAs were detected in the peripheral blood cells in the discovery samples. The significantly differentially expressed circRNAs were determined by fold change > 1.5 and FDR < 0.05. In total, we detected 390 circRNAs that were significantly differentially expressed between ICH patients and HTN controls, including 229 upregulated circRNAs and 161 downregulated circRNAs (Fig.1, Additional file 2: Table S1). PCA plots were used to distinguish between the ICH and HTN groups (Fig. 2a). We constructed volcano plots to evaluate the variation and reproducibility of circRNA expression between ICH patients and HTN controls (Fig. 2b). There were 208 upregulated and 103 downregulated circRNAs that were classic exon back-splicing; a total of 20 alternate exons, 29 introns, 17 overlapping exons, and 9 antisense and 4 intergenic circRNAs were detected between ICH and HTN controls (Fig. 2c). In addition, we performed a circus plot indicating that the circRNAs were distributed in all chromosomes (Fig. 2d).
Functional analysis of circRNA host genes
The host genes of circRNAs are able to produce mRNA transcripts and are involved in diverse processes of pathological development, and their expression levels are also influenced by circRNAs. Hence, we performed functional analysis of the significantly altered circRNA host genes. GO categorized analysis indicated that the host genes were involved in biological regulation, regulation of metabolic process, cell communication, signal transduction protein binding and enzyme binding (Fig. 3a). The KEGG pathway analysis showed that these host genes were involved mainly in lysine degradation, inositol phosphate metabolism, phosphatidylinositol signaling system, endocytosis, cell cycle, base excision repair and the HIF-1 signaling pathway (Fig. 3b). These biological processes and pathways are associated with the pathology and pathogenesis of ICH.
Differentially expressed circRNAs in validation samples
Next, we validated the circRNA profile in an independent sample using RNA sequencing, the strategies and statistics used in the discovery samples. The demographics and characteristics of the patients and matched controls in the validation samples are shown in Table 1. Overall, we detected 125 circRNAs that were significantly differentially expressed between ICH patients and HTN controls, including 56 upregulated circRNAs and 69 downregulated circRNAs (Fig. 1, Additional file 2: Table S2). PCA plots were used to distinguish between the ICH and HTN groups (Fig. 4a). In the volcano plots, the variation in circRNA expression was evaluated between ICH patients and HTN controls (Fig. 4b). There were 45 upregulated and 51 downregulated circRNAs that were classic exon back-splicing, and a total of 3 alternate exons, 10 introns, 13 overlapping exons, and 2 antisense circRNAs and 1 intergenic circRNA were detected between ICH and HTN controls (Fig. 4c). In addition, we performed a circus plot indicating that the circRNAs were distributed in all chromosomes, similar to the discovery sample (Fig. 4d).
For CI versus HTN, we detected 110 and 66 circRNAs in the discovery and validation samples, respectively (Additional file 1: Fig. S1a, 1b, Additional file 2: Table S3 and S4). Furthermore, we compared ICH to HTN and CI to HTN and obtained ICH-specific circRNAs for further analysis. All 16 ICH-specific circRNAs overlapped in two samples, among which 15 circRNAs were consistently altered, including 5 upregulated circRNAs and 10 downregulated circRNAs (Additional file 1: Fig. S2a-e). Among which 3 circRNAs (1 up and 2 down) were not found in the circRNA database. We designated novel circRNAs with host gene symbols. The 5 upregulated circRNAs in ICH included hsa_circ_0084615, hsa_circ_0001240, hsa_circ_0091669, hsa_circ_0001947 and novel_circ_PLXNC1, whose host gene was PLXNC1(Table 2). The 10 downregulated circRNAs in ICH were hsa_circ_0008983, hsa_circ_0001306, hsa_circ_0001386, hsa_circ_0033144, hsa_circ_0005838, hsa_circ_0005044, hsa_circ_0004096, hsa_circ_0006491, novel_circ_ERBB2 (host gene ERBB2) and novel_circ_11364 (no host gene) (Table 2). The 15 circRNA expression variations were shown with hierarchical clustering heatmaps in the discovery and validation samples (Fig. 4e, f), indicating that circRNA expression profiles in ICH patients were distinctly different from those in HTN controls.
Evaluation of circRNAs as potential diagnostic biomarkers in ICH patients
To explore the potential diagnostic value of the 15 circRNAs, we performed ROC analysis and calculated the AUC of ROC using RNA sequencing data SRPBM in all samples. The top 3 AUCs in all samples were hsa_circ_0001240 (AUC = 0.8078), hsa_circ_0001386 (AUC = 0.8058) and hsa_circ_0001947 (AUC = 0.7981) (Additional file 2: Table S5). We selected these 3 candidate circRNAs to further validate the expression levels by RT-PCR in all samples. All 3 circRNAs were significantly altered in patients with ICH compared with HTN controls (hsa_circ_0001240, P < 0.001; hsa_circ_0001947, P < 0.001; hsa_circ_0001386, P < 0.001) (Fig. 5a). There was also significant differential expression in patients with ICH compared with patients with CI (hsa_circ_0001240, P = 0.006; hsa_circ_0001947, P = 0.002; hsa_circ_0001386, P = 0.009) (Fig. 5b). There was no significant differential expression in patients with CI compared to those with HTN (hsa_circ_0001240, P=0.64; hsa_circ_0001947, P= 0.29; hsa_circ_0001386, P=0.64) (Fig. 5c). These results are consistent with the RNA sequencing results.
We furtherly constructed a logistic regression model with the combination of 3 circRNAs to identify the potential diagnostic value. The signatures of 3 circRNAs for differentiating between patients with ICH and HTN controls with the AUC was 0.92 (95% CI: 0.869-0.966), the sensitivity was 86%, and the specificity was 88% (Fig. 5d). Furthermore, 3 circRNA combinations of the risk factors (age, sex, BMI, SBP, DBP, TG, TC, HDL-C, LDL-C, smoking and drinking) showed that the AUC was increased to 0.97 (95% CI: 0.94-0.99), the sensitivity was 94.5% and the specificity was 85.7% (Fig. 5e). The AUC for differentiating between ICH and CI patients was 0.77 (95% CI: 0.688-0.855), the sensitivity was 61% and the specificity was 86% (Fig. 5f). These results showed that the 3circRNAs could as biomarkers for diagnosis of ICH.
Identification of 3 circRNA as independent predictors of ICH
To further explore the potential value of hsa_circ_0001240, hsa_circ_0001947 and hsa_circ_0001386 as ICH biomarkers, we performed Spearman’s correlation analysis to test the correlation of these 3 circRNA expression levels with ICH patient clinical characteristics. The results show that hsa_circ_0001240 expression levels correlated with SBP, HDL-C, TC, TG and uric acid (UA) in ICH patients (p < 0.05); the hsa_circ_0001947 expression levels correlated with LDL-C, UA and TBIL (p<0.05); and the hsa_circ_0001386 expression levels correlated with TBIL (p<0.05). All 3 circRNA expression levels correlated with white blood cells (WBCs) (p < 0.05) (Additional file 2: Table S6). These results suggested that hsa_circ_0001240, hsa_circ_0001947 and hsa_circ_0001386 may be involved in the pathogenesis of ICH.
In addition, logistic regression models were performed to identify whether hsa_circ_0001240, hsa_circ_0001947 and hsa_circ_0001386 could be predictors of ICH occurrence. As shown in Table 3, with a unit of increase of hsa_circ_0001240, the odds ratio for ICH occurrence was 2.088 (95% CI: 1.418-3.075, p <0.001) after adjusting for age, sex, smoking, drinking, SBP, DBP, TG, TC, HDL-C, LDL-C and glucose. The adjusted OR was 4.382 (95% CI: 2.087-9.204, p < 0.001) with a 0.5 unit increase in hsa_circ_0001947. In addition, the adjusted OR was 0.062 (95% CI: 0.009-0.415, p = 0.004) with a unit increase of hsa_circ_0001386. These results imply that hsa_circ_0001240 and hsa_circ_0001947 might increase the risk of ICH and that hsa_circ_0001386 might protect the occurrence of ICH. The upregulated hsa_circ_0001240 and hsa_circ_0001947 and downregulated hsa_circ_0001386 might increase the risk of ICH.
ceRNA network and target miRNA and mRNA function analysis
CircRNAs are known to serve as miRNA sponges, which are expected to influence downstream miRNA function, further regulating target mRNA expression. The circRNA-miRNA-mRNA regulation network is thought to play important roles in many disorders. We further investigated hsa_circ_0001240, hsa_circ_0001947 and hsa_circ_0001386 target miRNAs with Circular RNA Interactome and miRanda. We found that hsa_circ_0001240 could target 3 miRNAs: hsa-miR-663b, hsa-miR-1270 and hsa-miR1184. Hsa_circ_0001947 has 3 miRNA target sites: hsa-miR-671-5p, hsa-miR-647, and hsa-miR-892b. Hsa_circ_0001386 has 5 miRNA target sites: hsa-miR-1265, hsa-miR-885-3p, hsa-miR-658, hsa-miR-296-5p and hsa-miR-490-5p. Next, we predicted the miRNA target mRNAs using miRwalk, selected the overlapping mRNAs among both miRwalk and previously differentially expressed mRNAs (fold change > 1.5 and FDR < 0.05), and selected the upregulated (downregulated) circRNAs corresponding to upregulated (downregulated) mRNAs to construct a network. Overall, we obtained 107 target mRNAs that were up/downregulated, consistent with circRNAs. Finally, there were 3 circRNAs, 11 miRNAs and 107 mRNAs network shown in Cytoscape (Fig. 6).
The DIANA-mirPath analysis indicated that the miRNAs were mainly associated with the immune response, lysine degradation, fatty acid metabolism and fatty acid metabolism, cell cycle, Wnt signaling pathway and TGF-β pathway (Fig. 7a, b, Additional file 1: Fig. S3a-d). Furthermore, we found 8 RNA binding proteins (RBPs) matched to hsa_circ_0001368 by CircInteractome: AGO2 with 5 tags, DGCR8 with 1 tag, EIF4A3 with 19 tags, FMRP with 3 tags, FUS with 2 tags, HuR with 1 tag, PTB with 4 tags and U2AF65 with 1 tag. Functional analysis of the RBPs involved in RNA processing, mRNA metabolic processes, and signaling RNA binding (Additional file 1: Fig. S4a, b). A diagram combining the RBPs and miRNAs with KEGG pathway analysis is shown in Additional file 1: Fig. S4c. Furthermore, the target mRNA pathways were enriched, and the results indicated that these genes were involved in cell communication, signal transduction, integrin cell surface interactions, the PDGF receptor signaling pathway, the EGF receptor signaling pathway and the immune system (Fig. 7c, d). All these biological processes are associated with the pathology of ICH. Therefore, we conjectured that circRNAs may act through target miRNAs and further regulate integrin cell surface interactions and vascular injury, thereby being involved in the pathogenesis and pathology of ICH.