Identification of circRNA expression signatures correlated with disease severity in pediatric systemic lupus erythematosus

Abstract

Background: Systemic lupus erythematosus (SLE) is a complex systemic autoimmune disease with no current cure. Developing diagnostic biomarkers is crucial for improving patient outcomes. Circular RNAs (circRNAs) are a class of noncoding RNAs that are more stable, abundant, and structurally distinct compared to linear RNAs. While circRNAs have shown promise as biomarkers in various diseases, their potential in pediatric SLE remains unclear.

Methods: We performed RNA sequencing on peripheral blood mononuclear cells (PBMCs) from pediatric SLE patients categorized into mild, moderate, and severe groups. CircRNA expression profiles were analyzed for differential expression. The potential of circRNAs as biomarkers for SLE severity was evaluated through receiver operating characteristic (ROC) analysis. Additionally, Spearman correlation analysis was used to assess the relationship between circRNA expression levels and the SLE Disease Activity Index (SLEDAI) score.

Results: Our analysis revealed significant differential expression of circRNAs across different SLE severity groups. The circRNA expression patterns were closely associated with various biological processes, including signaling pathways, metabolism, and transcriptional regulation. Furthermore, ROC analysis demonstrated the potential of circRNAs to predict SLE severity. Spearman correlation analysis showed a significant correlation between dysregulated circRNA expression and SLEDAI scores.

Conclusion: Our findings strongly suggest that circRNAs could play a pivotal role in predicting pediatric SLE severity, offering a promising avenue for early diagnosis and personalized treatment strategies. This research lays the groundwork for future studies exploring circRNAs in pediatric SLE pathogenesis and prognosis, with the potential to significantly improve patient outcomes and therapeutic interventions.

Keywords: RNA sequencing; biomarker; circRNA; disease activity; pediatric systemic lupus erythematosus.

Figure 1

Heatmap and Volcano Plot of differentially expressed circRNAs between mild and moderate SLE groups. (A) The heatmap analysis revealed distinct circRNA expression profiles between the two groups. (B) The volcano plot shows differentially expressed circRNAs based on RNA-seq analysis. (C) The bar graph displays 331 downregulated circRNAs and 251 upregulated circRNAs between the two groups. N=6 each group.

Figure 2

KEGG and GO Pathway enrichment analysis of differentially expressed circRNAs between mild and moderate pediatric SLE patients. (A) KEGG pathway enrichment analysis showing the top pathways significantly associated with differentially expressed circRNAs. (B) Molecular Function Gene Ontology (GO) terms enriched among the differentially expressed circRNAs. (C) Cellular Component GO terms enriched among the differentially expressed circRNAs.(D) Biological Process GO terms enriched among the differentially expressed circRNAs.N=6 each group.

Figure 3

Characterization of differential expressed circRNAs in PBMCs from SLE mild and moderate patients. (A) Comparison of expression levels of differentially expressed circRNAs between the two groups. (B) ROC curve analysis of differentially expressed circRNAs in the mild and moderate groups, with AUC values displayed. N=6 each group. *P < 0.05; **P < 0.01; ***P < 0.001; ****P < 0.0001.

Figure 4

The expression of dysregulated circRNAs is closely related to the SLEDAI Score. Spearman correlation analysis was performed to show the association between differentially expressed circRNAs and SLEDAI scores in mild and moderate SLE patients. N=6 each group.

Figure 5

Heatmap and Volcano Plot of differentially expressed circRNAs between moderate and severe SLE groups. (A) The heatmap analysis revealed distinct circRNA expression profiles between the two groups. (B) The volcano plot shows differentially expressed circRNAs based on RNA-seq analysis. (C) The bar graph displays 99 downregulated circRNAs and 100 upregulated circRNAs between the two groups. N=6 each group.

Figure 6

KEGG and GO Pathway enrichment analysis of differentially expressed circRNAs between moderate and severe SLE Patients. (A) KEGG enrichment analysis of differentially expressed circRNAs. (B) Molecular function GO analysis of differentially expressed circRNAs. (C) Cell component GO analysis. (D) Biological process GO analysis. N=6 each group.

Figure 7

Characterization of differential expressed circRNAs in PBMCs from SLE moderate and severe patients. (A) Comparison of expression levels of differentially expressed circRNAs between the two groups. (B) ROC curve analysis of differentially expressed circRNAs in the moderate and severe groups, with AUC values displayed. N=6 each group. *P < 0.05; **P < 0.01.

Figure 8

The expression of dysregulated circRNAs is closely related to the SLEDAI Score. Spearman correlation analysis was performed to show the association between differentially expressed circRNAs and SLEDAI scores in moderate and severe SLE patients. N=6 each group.