Potential therapeutic target genes for systemic lupus erythematosus: a bioinformatics analysis

The circRNA-miRNA-mRNA regulatory network in plasma and peripheral blood mononuclear cells and the potential associations with the pathogenesis of systemic lupus erythematosus

OBJECTIVES

This study aimed to explore the possible role of plasma and peripheral blood mononuclear cells (PBMCs) circular RNA (circRNA) in systemic lupus erythematosus (SLE).

METHOD

Total RNA was extracted from blood plasma samples obtained from 10 patients with SLE and 10 healthy controls and subjected to microarray analysis to define the profile of circRNA expression. The quantitative reverse transcription-polymerase chain reaction (qRT-PCR) amplification was conducted. The overlapped circRNA between PBMCs and plasma was performed, the interactions with microRNAs were predicted, the miRNA target mRNA was predicted, and the GEO database was used. The Gene ontology and pathway analysis was performed.

RESULTS

One hundred thirty-one upregulated and 314 significantly downregulated circRNAs were identified in the plasma of patients with SLE by the Fold change criteria (≥ 2.0) and P < 0.05. The qRT-PCR results showed that the expression of has-circRNA-102531, has-circRNA-103984, and has-circRNA-104262 was increased in plasma of SLE, and the expression of has-circRNA-102972, has-circRNA-102006, has-circRNA-104313 was decreased in plasma of SLE. Twenty-eight upregulated circRNAs and 119 downregulated circRNAs were overlapped from PBMCs and plasma, and ubiquitination was enriched. Furthermore, the circRNA-miRNA-mRNA network was constructed in SLE after analyzing dataset GSE61635 from GEO. The circRNA-miRNA-mRNA network comprises 54 circRNAs, 41 miRNAs, and 580 mRNAs. In addition, the TNF signaling pathway and the MAPK pathway were enriched from the mRNA of the miRNA target.

CONCLUSION

We first revealed the differentially expressed circRNAs in plasma and PBMCs, and then the circRNA-miRNA-mRNA network was constructed. The network's circRNAs could be a potential diagnostic biomarker and potentially play an important role in the pathogenesis and development of SLE. Key Points • This study analyzed the circRNAs expression profiles combined with the plasma and PBMCs, which provided a comprehensive overview of circRNAs expression patterns in SLE. • The network of the circRNA-miRNA-mRNA in SLE was constructed, which contributes to a better understanding of the pathogenesis and development of SLE.

Identification of key interferon-stimulated genes for indicating the condition of patients with systemic lupus erythematosus

Systemic lupus erythematosus (SLE) is a chronic autoimmune disease with highly heterogeneous clinical symptoms and severity. There is complex pathogenesis of SLE, one of which is IFNs overproduction and downstream IFN-stimulated genes (ISGs) upregulation. Identifying the key ISGs differentially expressed in peripheral blood mononuclear cells (PBMCs) of patients with SLE and healthy people could help to further understand the role of the IFN pathway in SLE and discover potential diagnostic biomarkers.

The differentially expressed ISGs (DEISG) in PBMCs of SLE patients and healthy persons were screened from two datasets of the Gene Expression Omnibus (GEO) database. A total of 67 DEISGs, including 6 long noncoding RNAs (lncRNAs) and 61 messenger RNAs (mRNAs) were identified by the “DESeq2” R package. According to Gene Ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis, those DEISGs were mainly concentrated in the response to virus and immune system processes. Protein-protein interaction (PPI) network showed that most of these DEISGs could interact strongly with each other. Then, IFIT1, RSAD2, IFIT3, USP18, ISG15, OASL, MX1, OAS2, OAS3, and IFI44 were considered to be hub ISGs in SLE by “MCODE” and “Cytohubba” plugins of Cytoscape, Moreover, the results of expression correlation suggested that 3 lncRNAs (NRIR, FAM225A, and LY6E-DT) were closely related to the IFN pathway.

The lncRNA NRIR and mRNAs (RSAD2, USP18, IFI44, and ISG15) were selected as candidate ISGs for verification. RT-qPCR results showed that PBMCs from SLE patients had substantially higher expression levels of 5 ISGs compared to healthy controls (HCs). Additionally, statistical analyses revealed that the expression levels of these ISGs were strongly associated to various clinical symptoms, including thrombocytopenia and facial erythema, as well as laboratory indications, including the white blood cell (WBC) count and levels of autoantibodies. The Receiver Operating Characteristic (ROC) curve demonstrated that the IFI44, USP18, RSAD2, and IFN score had good diagnostic capabilities of SLE.

According to our study, SLE was associated with ISGs including NRIR, RSAD2, USP18, IFI44, and ISG15, which may contribute to the future diagnosis and new personalized targeted therapies.

Predicted Immune-Related Genes and Subtypes in Systemic Lupus Erythematosus Based on Immune Infiltration Analysis

Objective

The present investigation is aimed at identifying key immune-related genes linked with SLE and their roles using integrative analysis of publically available gene expression datasets.

Methods

Four gene expression datasets pertaining to SLE, 2 from whole blood and 2 experimental PMBC, were sourced from GEO. Shared differentially expressed genes (DEG) were determined as SLE-related genes. Immune cell infiltration analysis was performed using CIBERSORT, and case samples were subjected to k-means cluster analysis using high-abundance immune cells. Key immune-related SLE genes were identified using correlation analysis with high-abundance immune cells and subjected to functional enrichment analysis for enriched Gene Ontology Biological Processes and KEGG pathways. A PPI network of genes interacting with the key immune-related SLE genes was constructed. LASSO regression analysis was performed to identify the most significant key immune-related SLE genes, and correlation with clinicopathological features was examined.

Results

309 SLE-related genes were identified and found functionally enriched in several pathways related to regulation of viral defenses and T cell functions. k-means cluster analysis identified 2 sample clusters which significantly differed in monocytes, dendritic cell resting, and neutrophil abundance. 65 immune-related SLE genes were identified, functionally enriched in immune response-related signaling, antigen receptor-mediated signaling, and T cell receptor signaling, along with Th17, Th1, and Th2 cell differentiation, IL-17, NF-kappa B, and VEGF signaling pathways. LASSO regression identified 9 key immune-related genes: DUSP7, DYSF, KCNA3, P2RY10, S100A12, SLC38A1, TLR2, TSR2, and TXN. Imputed neutrophil percentage was consistent with their expression pattern, whereas anti-Ro showed the inverse pattern as gene expression.

Conclusions

Comprehensive bioinformatics analyses revealed 9 key immune-related genes and their associated functions highly pertinent to SLE pathogenesis, subtypes, and identified valuable candidates for experimental research.

Clinical Value of Serum Neuron-Specific Enolase Combined with Serum S100B Protein in the Diagnosis of Systemic Lupus Erythematosus

Objective

To investigate the clinical value of serum neuron-specific enolase (NSE) combined with serum S100B protein in the diagnosis of systemic lupus erythematosus (SLE).

Methods

Sixty patients with SLE treated in our hospital from January 2019 to April 2021 were enrolled as the study group. According to the degree of activity, the study group was assigned into three groups: mild activity group (n = 20), moderate activity group (n = 20), and severe activity group (n = 20). A total of 60 healthy people who underwent physical examination in our hospital in the same period were enrolled as the control group. The NSE and serum S100B protein were detected in the two groups, and the correlation between serum nerve-specific enolase and serum S100B protein and the clinical value in the diagnosis of SLE were analyzed.

Results

First of all, we compared the general data of the two groups. There was no significant difference in sex, age, marital status, and education level, and no significant difference was exhibited (p > 0.05). There was no significant difference in sex, age, marital status, and education level among mild activity group, moderate activity group, and severe activity group, and no significant difference in data was exhibited (p > 0.05). Secondly, we compared the levels of serum S100B protein and NSE. The levels of serum S100B protein and NSE in the study group were higher compared to the control group (p < 0.05). The levels of serum S100B protein and NSE in patients with different activity levels of SLE were compared. The levels of serum S100B protein and NSE in mild activity group < moderate activity group < severe activity group were significantly different (p < 0.05). Correlation analysis between serum S100B, NSE levels, and SLE activity indicated that serum S100B and NSE levels were positively correlated with SLE activity. With the increase of SLE activity, serum S100B and NSE levels gradually increased, and the data difference was statistically significant (r = 0.855, 0.844, p < 0.05). Finally, we established the logistic prediction model, take the probability of generating prediction as the analysis index, and draw the ROC curve to evaluate the diagnostic value of different combinations to SLE. The highest AUC and sensitivity of the two indexes in the diagnosis of SLE were 0.773 and 0.836, respectively. The levels of serum S100B protein and NSE have a certain value in the diagnosis of SLE, while the combined diagnosis is of higher value, sensitivity, and specificity in the diagnosis of SLE.

Conclusion

Serum S100B protein and NSE are very sensitive indexes to judge the damage of central nervous system. However, due to the small number of cases in this study, there were as many as 19 kinds of NPSLE classification, so the relationship between serum S100B protein, NSE levels, and various NPSLE and their exact application value in diagnosing the disease and judging the prognosis needs to be confirmed by expanding the number of cases.

Identifying the shared genes and KEGG pathways of Resolvin D1-targeted network and osteoarthritis using bioinformatics

Osteoarthritis (OA) is a common chronic degenerative disease characterized by the loss of articular cartilage, which causes loss of joint function and reduce quality of life. Resolvin D1 (RvD1) has shown interesting anti-inflammatory effects; however, the mechanism of action of RvD1 in OA remains unclear. The aim of this study was to investigate the potential mechanism of RvD1 in OA by bioinformatics and partial in vitro mechanisms. Here, 106 shared differentially expressed genes (DEGs) were identified based on the GSE82107, GSE55235, GSE55457 dataset; 700 DEGs were identified based on GSE169077. Enrichment analyses of these genes were then successively conducted. RvD1-targeted genes and KEGG pathways are identified by STITCH. 27 shared KEGG pathways were identified among RvD1-targeted pathways and OA. Furthermore, cell apoptosis assay, western blotting, real-time fluorescent quantitative PCR (qRT-PCR), enzyme linked immunosorbent assay (ELISA) were used to confirm the expression levels of the key genes of shared Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways between RvD1-targeted and OA in IL-1β treated rat knee chondrocytes. The results showed that RvD1-targeted pathways and the expression of nuclear p65, p53, and p-JNK were inhibited in the RvD1 group compared with the IL-1β group. Thus, the findings indicate that RvD1 may inhibit the development of OA through NF/kB, p53, MAPK/JNK, PI3K-AKT signaling pathways, and act as a treatment for OA.

Lectin galactoside-binding soluble 3 binding protein mediates methotrexate resistance in choriocarcinoma cell lines

Choriocarcinoma is one of the most aggressive gestational trophoblastic neoplasias (GTN). Methotrexate (MTX) resistance is the main cause of treatment failure in choriocarcinoma. However, the mechanism of MTX resistance in choriocarcinoma is poorly known. This study aims to explore the function of Lectin galactoside-binding soluble 3 binding protein (LGALS3BP) in MTX-resistance in choriocarcinoma cells. Gradual dose escalation of MTX was used to establish MTX-resistant choriocarcinoma cells (JAR-MTX and JEG3-MTX cell lines). RNA-sequencing was used to explore the differentially expressed genes. Plasmids or SiRNA transfection was used to regulate the expression of LGALS3BP. ELISA was used to detect the concentrations of LGALS3BP in the serum of MTX-sensitive and MTX-resistant patients. qRT-PCR, Western blot, and CCK-8 assay were used to determine the effects of LGALS3BP on MTX-resistance in JAR and JEG3 cells. The results showed the relative resistance index (RI) of MTX is 791.50 and 1040.04 in JAR-MTX and JEG3-MTX, respectively. LGALS3BP was up-regulated in MTX-resistant cells compared to original cells in both RNA and protein level. The concentrations of LGALS3BP were higher in the sera of MTX-resistant patients than in MTX-sensitive patients. Knocking down LGALS3BP can reverse the MTX-resistance in JAR-MTX and JEG3-MTX cells. In summary, we preliminarily established two MTX-resistant cells, and performed RNA-sequencing, and found LGALS3BP may play important role in MTX-resistance. Our work not only provides a research tool (MTX-resistant cells) for other researchers, but gives some hint on how MTX resistance is regulated.

Identification and Validation of IFI44 as Key Biomarker in Lupus Nephritis

Lupus nephritis (LN) is a common and severe organ manifestation of systemic lupus erythematosus (SLE) and is a major cause of SLE related deaths. Early diagnosis is essential to improve the prognosis of patients with LN. To screen the potential biomarkers associated with LN, we downloaded the gene expression profile of GSE99967 from the Gene Expression Omnibus (GEO) database. Weighted gene co-expression network analysis (WGCNA) was utilized to construct a gene co-expression network and identify gene modules associated with LN. Gene Ontology (GO) analysis was also applied to explore the biological function of genes and identify the key module. Differentially expressed genes (DEGs) were identified and Maximal Clique Centrality (MCC) values were calculated to screen hub genes. Furthermore, we selected promising biomarkers for real-time PCR (qRT-PCR) and enzyme-linked immunosorbent assay (ELISA) validation in independent cohorts. Our results indicated that five hub genes, including IFI44, IFIT3, HERC5, RSAD2, and DDX60 play vital roles in the pathogenesis of LN. Importantly, IFI44 may considered as a key biomarker in LN for its diagnostic capabilities, which is also a promising therapeutic target in the future.