Biomarkers For Systemic Lupus Erythematosus Research Articles

LncRNA NRIR serves as a biomarker for systemic lupus erythematosus and participates in the disease progression.

Systemic lupus erythematosus (SLE) is a complex autoimmune disorder characterized by a malfunction of the body's immune defense system. The objective of the present investigation was to examine the expression and diagnostic significance of NRIR in SLE and to prove whether it is involved in the progression of SLE. The study involved 110 participants, including 55 healthy individuals and 55 SLE patients. The expression levels of NRIR, miR-31-5p, and ICAM-1 were measured using qRT-PCR. The ROC curve was performed to assess the diagnostic significance of NRIR in SLE patients. Pearson correlation analysis was utilized to explore the relationship between NRIR and other indicators. Cytokines including IL-4, IL-6, and IL-21, along with IgG levels, were assessed using ELISA. The interaction between NRIR and miR-31-5p was validated using a dual-luciferase reporter assay. Upregulated expression of NRIR was observed in individuals with SLE, serving a diagnostic function for SLE. Additionally, abnormal expression of NRIR impacted the viability of CD4+ T cells within SLE patients. NRIR could negatively modulate the expression of miR-31-5p. LncRNA NRIR may be a potential biomarker for SLE and is likely involved in the progression of SLE.

Immunoregulatory role of AC007278.3 and HOTAIR long non-coding RNAs in lupus nephritis: potential biomarkers and therapeutic targets.

Long non-coding RNAs (lncRNAs) have emerged as crucial regulators in various biological processes, including immune regulation and autoimmune pathologies. However, their specific significance in modulating the cytokine network in systemic lupus erythematosus (SLE) remains largely unexplored. This study assessed the expression patterns of immune-related lncRNAs, HOTAIR, and AC007278.3, along with their related protein-coding genes, TNF-α and IL18RAP, in nephritic SLE patients. Additionally, the potential of selected genes as diagnostic biomarkers for SLE was evaluated. Blood samples were obtained from SLE patients (n = 30) and age-sex-matched healthy controls (HCs) (n = 60). Subsequently, RNA was isolated from peripheral blood mononuclear cells (PBMCs), and cDNA was synthesized to analyze the expression levels of the target genes using real-time PCR. The correlation analysis between the relative expressions of different genes was examined in both the patient and HC groups. The diagnostic potential of the lncRNAs was determined by calculating the Area Under the Curve of the Receiver Operating Characteristics (AUC of ROC), Cut-off, sensitivity, and specificity. Our results indicated a significant upregulation of lncRNAs AC007278.3 (fold change [FC] = 14.13, p-value < 0.0001) and HOTAIR (FC = 14.1, p-value < 0.0001). Correspondingly, their associated target genes, TNF-α and IL18RAP, were also overexpressed in patients (FC = 2.66 and FC = 5.18, respectively, p-value < 0.001). Notably, a strong positive correlation was observed between IL18RAP and AC007278.3 in SLE patients. Moreover, the AUC of ROC analyses underscored the diagnostic efficacy of AC007278.3 alone and combined with HOTAIR, yielding values of 0.89 and 0.86, respectively. These findings highlight the potential immunoregulatory roles of lncRNAs AC007278.3 and HOTAIR, emphasizing their significance as promising diagnostic biomarkers and potential therapeutic targets for SLE. Additionally, they provide valuable insights into the molecular mechanisms underpinning the disease's pathogenesis.

Simultaneous Multi-miRNA Detection in Urinary Small Extracellular Vesicles Using Target-Triggered Locked Hairpin DNA-Functionalized Au Nanoprobes for Systemic Lupus Erythematosus Diagnosis.

Systemic lupus erythematosus (SLE) is a chronic autoimmune disease characterized by multiorgan involvement and complex clinical manifestations, leading to cumbersome diagnostic processes. MicroRNAs (miRNAs) in small extracellular vesicles (sEVs) have emerged as promising biomarkers for liquid biopsy. Herein, we constructed a simple multi-miRNA detection platform based on target-triggered locked hairpin DNA-functionalized Au nanoprobes (AuNP@LH) as a simple and noninvasive tool for the diagnosis and classification of SLE. The nanoprobes were prepared by modifying locked hairpin DNA designed for target miRNAs on gold nanoparticles. In the presence of target miRNAs, target-triggered hairpin assembly amplification was induced, and then fluorophore-labeled bolt DNA was released, resulting in a fluorescence signal responsive to miRNA concentration. Benefiting from the enzyme-free amplification strategy, the limits of detection (LOD) of three miRNA biomarkers for SLE were 19 pM for microRNA-146a, 66 pM for microRNA-29c, and 19 pM for microRNA-150. The proposed probes have been successfully applied to simultaneously detect multiple miRNAs in urinary sEVs from patients diagnosed with SLE and healthy controls, which exhibited good practicability in SLE diagnosis with the area under curve (AUC) of the receiver characteristic curve reaching 1.00. Furthermore, SLE patients with different disease severity can be differentiated with 81.2% accuracy. Predictably, with the advantages of low cost, rapidity, high sensitivity, and noninvasiveness, our multi-miRNA detection platform is a potential tool for multiple miRNA analysis and related clinical applications.

Screening and validating genes associated with cuproptosis in systemic lupus erythematosus by expression profiling combined with machine learning.

Cell death has long been a focal point in life sciences research, and recently, scientists have discovered a novel form of cell death induced by copper, termed cuproptosis. This paper aimed to identify genes associated with cuproptosis in Systemic Lupus Erythematosus (SLE) through machine learning, combined with single-cell sequencing (scRNA-seq), to screen and validate related genes. The analytical results were then experimentally verified. Two published microarray gene expression datasets (GSE65391 and GSE61635) from SLE and control peripheral blood samples were downloaded from the GEO database. The GSE65391 dataset was used as the training group, while the GSE61635 dataset served as the validation group. Differentially expressed genes (DEGs) from GSE65391 identified 12 differential genes. Nine diagnostic genes, considered potential biomarkers, were selected using the least absolute shrinkage and selection operator (LASSO) and support vector machine recursive feature elimination (SVM-RFE) analysis. The Receiver Operating Characteristic (ROC) curves for both the training and validation groups were used to calculate the Area Under the Curve (AUC) to assess discriminatory properties. CIBERSORT was used to assess the relationship between these diagnostic genes and a reference set of infiltrating immune cells. Single-cell RNA sequencing data (GSE162577) from SLE patients were also obtained from the GEO database and analyzed. Experimental validation of the most important SLE biomarkers was performed. Twelve significantly different cuproptosis-related genes were identified in the GSE65391 training set. Immune cell analysis revealed 12 immune cell types and identified nine signature genes, including PDHB, GLS, DLAT, LIAS, MTF1, DLST, DLD, LIPT1, and FDX1. In the GSE61635 validation set, seven genes were weakly expressed, and two genes were strongly expressed in the treatment group. According to the ROC curves, PDHB and GLS demonstrated significant diagnostic value. Additionally, correlation analysis was conducted on the nine characteristic genes in relation to immune infiltration. The distribution of key genes in immune cells was determined using single-cell RNA sequencing data. Finally, the mRNA expression of the nine diagnostic genes was validated using qPCR.

Biomarkers for systemic lupus erythematosus: A scoping review.

In recent years, newly discovered potential biomarkers have great research potential in the diagnosis, disease activity prediction, and treatment of systemic lupus erythematosus (SLE). In this study, a scoping review of potential biomarkers for SLE over several years has identified the extent to which studies on biomarkers for SLE have been conducted, the specificity, sensitivity, and diagnostic value of potential biomarkers of SLE, the research potential of these biomarkers in disease diagnosis, and activity detection is discussed. In PubMed and Google Scholar databases, "SLE," "biomarkers," "predictor," "autoimmune diseases," "lupus nephritis," "neuropsychiatric SLE," "diagnosis," "monitoring," and "disease activity" were used as keywords to systematically search for SLE molecular biomarkers published from 2020 to 2024. Analyze and summarize the literature that can guide the article. Recent findings suggest that some potential biomarkers may have clinical application prospects. However, to date, many of these biomarkers have not been subjected to repeated clinical validation. And no single biomarker has sufficient sensitivity and specificity for SLE. It is not scientific to choose only one or several biomarkers to judge the complex disease of SLE. It may be a good direction to carry out a meta-analysis of various biomarkers to find SLE biomarkers suitable for clinical use, or to evaluate SLE by combining multiple biomarkers through mathematical models. At the same time, advanced computational methods are needed to analyze large data sets and discover new biomarkers, and strive to find biomarkers that are sensitive and specific enough to SLE and can be used in clinical practice, rather than only staying in experimental research and data analysis.

Exploring the transcriptomic landscape of moyamoya disease and systemic lupus erythematosus: insights into crosstalk genes and immune relationships.

Systemic Lupus Erythematosus (SLE) is acknowledged for its significant influence on systemic health. This study sought to explore potential crosstalk genes, pathways, and immune cells in the relationship between SLE and moyamoya disease (MMD). We obtained data on SLE and MMD from the Gene Expression Omnibus (GEO) database. Differential expression analysis and weighted gene co-expression network analysis (WGCNA) were conducted to identify common genes. Subsequently, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed on these shared genes. Hub genes were further selected through the least absolute shrinkage and selection operator (LASSO) regression, and a receiver operating characteristic (ROC) curve was generated based on the results of this selection. Finally, single-sample Gene Set Enrichment Analysis (ssGSEA) was utilized to assess the infiltration levels of 28 immune cells in the expression profile and their association with the identified hub genes. By intersecting the important module genes from WGCNA with the DEGs, the study highlighted CAMP, CFD, MYO1F, CTSS, DEFA3, NLRP12, MAN2B1, NMI, QPCT, KCNJ2, JAML, MPZL3, NDC80, FRAT2, THEMIS2, CCL4, FCER1A, EVI2B, CD74, HLA-DRB5, TOR4A, GAPT, CXCR1, LAG3, CD68, NCKAP1L, TMEM33, and S100P as key crosstalk genes linking SLE and MMD. GO analysis indicated that these shared genes were predominantly enriched in immune system process and immune response. LASSO analysis identified MPZL3 as the optimal shared diagnostic biomarkers for both SLE and MMD. Additionally, the analysis of immune cell infiltration revealed the significant involvement of activation of T and monocytes cells in the pathogenesis of SLE and MMD. This study is pioneering in its use of bioinformatics tools to explore the close genetic relationship between MMD and SLE. The genes CAMP, CFD, MYO1F, CTSS, DEFA3, NLRP12, MAN2B1, NMI, QPCT, KCNJ2, JAML, MPZL3, NDC80, FRAT2, THEMIS2, CCL4, FCER1A, EVI2B, CD74, HLA-DRB5, TOR4A, GAPT, CXCR1, LAG3, CD68, NCKAP1L, TMEM33, and S100P have been identified as key crosstalk genes that connect MMD and SLE. Activation of T and monocytes cells-mediated immune responses are proposed to play a significant role in the association between MMD and SLE.

Exploring genetic associations in systemic lupus erythematosus through Mendelian randomization: implications for novel biomarkers and therapeutic targets.

Systemic lupus erythematosus (SLE) is a chronic autoimmune disease characterized by a significant health burden. There is an essential need for novel biomarkers and therapeutic targets to improve diagnosis and management. Mendelian randomization (MR) was applied to explore causal links between SLE and various biomarkers like immune cells, metabolites, and inflammatory cytokines using multiple databases. Initially, biomarkers significantly associated with SLE were identified. Bidirectional MR helped clarify these relationships, and a two-step mediation MR examined their effects on SLE risk. Intersection analysis was used to identify biomarkers with consistent effects across datasets. Four biomarkers were identified as having significant associations with SLE risk: 1-palmitoyl-2-arachidonoyl-GPI levels [odds ratio (OR), 1.379; 95% confidence interval (CI), 1.180 to 1.613; FDR, 0.046], IL-17A levels (OR, 2.197; 95% CI, 1.412 to 3.418; FDR, 0.044), N-acetyl-aspartyl-glutamate (NAAG) levels (OR, 0.882; 95% CI, 0.831 to 0.936; FDR, 0.030), and ribitol levels (OR, 0.743; 95% CI, 0.644 to 0.857; FDR, 0.012). Bidirectional MR showed an inverse effect of NAAG on IL-17A levels (OR, 0.978; 95% CI, 0.962 to 0.994; p = 0.006). Mediation analysis indicated that NAAG influenced SLE risk both directly (beta = - 0.108) and indirectly through IL-17A (beta = - 0.018), highlighting the potential mediating role of IL-17A. After expanding the significance criteria to p < 0.05, intersection analysis across multiple datasets revealed 29 biomarkers with consistent beta directions, including 19 potential risk factors (beta > 0) and 10 protective factors (beta < 0) for SLE. This research has revealed significant genetic associations with SLE and demonstrated that IL-17A mediates the relationship between NAAG levels and SLE risk, highlighting potential new targets for personalized therapeutic interventions. Key Points • This study employs MR to identify significant genetic associations between various biomarkers and SLE, providing novel insights into potential biomarkers and therapeutic targets. • Four key biomarkers were identified as significantly associated with SLE risk: 1-palmitoyl-2-arachidonoyl-GPI, IL-17A, N-acetyl-aspartyl-glutamate (NAAG), and ribitol. • The findings suggest that NAAG levels have a protective effect against SLE, partly mediated through IL-17A, indicating a complex interplay between these biomarkers in the pathogenesis of SLE. • Intersectional analysis across multiple datasets revealed 29 biomarkers with consistent effects on SLE risk, highlighting new directions for future research and potential personalized therapeutic strategies.

Anti-TCP1 Antibody Is a Potential Biomarker for Diagnosing Systemic Lupus Erythematosus.

Systemic lupus erythematosus (SLE) is a chronic inflammatory disease caused by autoantibodies. Serum samples from patients with SLE (n = 10) were compared with those from normal controls (NCs, n = 5) using 21K protein chip analysis to identify a biomarker for SLE, revealing 63 SLE-specific autoantibodies. The anti-chaperonin-containing t-complex polypeptide-1 (TCP1) antibody exhibited higher expression in patients with SLE than in NCs. To validate the specificity of the anti-TCP1 antibody in SLE, dot blot analysis was conducted using sera from patients with SLE (n = 100), rheumatoid arthritis (RA; n = 25), Behçet's disease (BD; n = 28), and systemic sclerosis (SSc; n = 30) and NCs (n = 50). The results confirmed the detection of anti-TCP1 antibodies in 79 of 100 patients with SLE, with substantially elevated expression compared to both NCs and patients with other autoimmune diseases. We performed an enzyme-linked immunosorbent assay to determine the relative amounts of anti-TCP1 antibodies; markedly elevated anti-TCP1 antibody levels were detected in the sera of patients with SLE (50.1 ± 17.3 arbitrary unit (AU), n = 251) compared to those in NCs (33.9 ± 9.3 AU), RA (35 ± 8.7 AU), BD (37.5 ± 11.6 AU), and SSc (43 ± 11.9 AU). These data suggest that the anti-TCP1 antibody is a potential diagnostic biomarker for SLE.

High Interleukin 21 Levels in Patients with Systemic Lupus Erythematosus: Association with Clinical Variables and rs2221903 Polymorphism.

Background: Systemic lupus erythematosus (SLE) is an autoimmune disease characterized by autoantibody production and diverse tissue and organ inflammatory affections. Interleukin 21 (IL-21) is implicated in B cell survival, proliferation, differentiation, class switching, and immunoglobulin production; therefore, it is considered a key cytokine in the pathogenesis of SLE. However, its association with disease activity and clinical phenotypes remains unclear. We aimed to evaluate the association of IL-21 levels with the disease activity and clinical phenotypes in patients with SLE. Also, we analyzed the IL21 polymorphisms associated with increased IL-21 levels. Methods: The IL-21 serum levels were determined using the enzyme-linked immunosorbent assay (ELISA) method. The rs2221903 and rs2055979 polymorphisms were assessed in 300 healthy controls (HCs) and 300 patients with SLE by the polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) technique. The levels of IL-21 were monitored during follow-up visits in 59 patients with SLE. Results: The patients with SLE showed higher IL-21 levels compared to the HCs. The IL-21 levels did not correlate with Mex-SLEDAI and were not different in patients with inactive, mild-moderate, and severe disease. The IL-21 levels were increased in patients with hematological affection. The ROC curve analysis revealed that the IL-21 levels had good predictive power in discriminating among patients with SLE and HCs. In a follow-up analysis, the levels of IL-21 remained higher in the patients with SLE even when the patients were in remission. Also, the rs2221903 polymorphism was associated with increased IL-21 levels. Conclusions: This study highlights the importance of IL-21 as a key cytokine in SLE. IL-21 levels are higher in patients with SLE and remain increased regardless of disease activity. According to the ROC analysis, IL-21 is a potential biomarker of SLE. Further longitudinal studies are needed to explore the relationship between IL-21 and the clinical phenotypes of SLE.

Assay for interferon gamma release as a novel marker in pediatric patients with systemic lupus erythematosus

BackgroundThe interferon-gamma (IFN-γ) release assay (IGRA) is an important laboratory diagnosis for latent Mycobacterium tuberculosis (TB) infection. The TB-IGRA measures the release of IFN-γ from peripheral blood cells, who are exposed to TB antigen (Ag), mitogen (MT), or negative/nil control (NL) in vitro. While, an exceptional higher TB Ag-NL level will reflect an elevation of peripheral lymphocytes released IFN-γ in a same condition. Therefore, we found that the elevated levels of TB Ag-NL could become a new biomarker for the diagnosis and treatment of pediatric systemic lupus erythematosus (SLE) patients.MethodsWe have analyzed the clinical data of 776 children who are underwent TB-IGRA testing in the Department of Allergy and Rheumatology of Guangzhou Women and Children’s Medical Center from 2018 to 2020. To investigate the association between TB Ag-NL and SLE, we have analyzed the clinical data of 47 SLE patients and TB Ag-NL testing results, and then evaluated the association between TB Ag-NL and SLE disease activity.ResultsThe TB Ag-NL levels were significantly higher in patients with active SLE than those in inactive SLE (p = 0.0002). The TB Ag-NL levels were positively correlated with the SLE disease activity index (SLEDAI) and laboratory diagnosis parameters. The mean value of TB Ag-NL in SLE patients (0.04191 ± 0.07955, IU/mL) were significantly higher than those in patients with juvenile dermatomyositis (JDM) (0.0158 ± 0.0337, IU/mL, p = 0.036), juvenile idiopathic arthritis (JIA) (0.0162 ± 0.0388, IU/mL, p = 0.001), and healthy controls (HC) (0.0001 ± 0.0027, IU/mL, p = 0.0003). Therefore, the elevated TB Ag-NL levels could serve as a potential diagnostic biomarker of SLE, especially for the active SLE.ConclusionThe detection of IFN-γ release levels by the TB-IGRA may be useful to assess SLE disease activity in pediatric patients with active SLE.

WNT16 as a promising biomarker for systemic lupus erythematosus and its role in regulating cell proliferation and apoptosis.

To investigate the expression and function of WNT16, a member of the WNT family protein, in the context of systemic lupus erythematosus (SLE). WNT16 expression was assessed in peripheral blood mononuclear cells (PBMCs) from 35 SLE patients and 25 healthy individuals using quantitative polymerase chain reaction. Additionally, serum WNT16 protein levels were quantified via enzyme-linked immunosorbent assay in 162 SLE patients, 96 healthy controls (HC), and disease controls comprised 154 individuals with rheumatoid arthritis (RA) and Sjögren's syndrome (SS). We investigated the associations between WNT16 protein levels and clinical manifestations, laboratory indices, and disease activity in SLE patients. Receiver operating characteristic (ROC) curve analysis was employed to evaluate the diagnostic potential of serum WNT16 for SLE. Furthermore, we performed a knockdown assay on Jeko-1 cells and assessed cell proliferation and apoptosis using Cell Counting Kit-8 and flow cytometry. WNT16 mRNA in SLE patients' PBMCs were significantly lower than those in HC. Furthermore, serum WNT16 in SLE patients were markedly reduced compared to HC, RA, and SS cohorts. ROC curve analysis indicated that plasma WNT16 levels could serve as a potential biomarker for SLE identification (AUC=0.809, SLE vs. HC; AUC=0.760, SLE vs. RA; AUC=0.710, SLE vs. SS). Notably, a weak positive correlation was observed between WNT16 protein and both alkaline phosphatase and lymphocyte percentages. Conversely, a weak negative correlation existed between WNT16 and low-density lipoprotein, neutrophil percentage, and the incidence of pleurisy and disease activity. Additionally, our study confirmed that WNT16 knockdown impairs cell proliferation and enhances apoptosis. Serum WNT16 levels effectively differentiate SLE patients from healthy controls and individuals with other autoimmune disorders. WNT16 serves as a potential biomarker with high sensitivity. The diminished expression of WNT16 in SLE may have a significant role in its pathogenesis through the regulation of cell proliferation and apoptosis.

Mitochondrial Dysfunction in Systemic Lupus Erythematosus with a Focus on Lupus Nephritis.

Systemic lupus erythematosus (SLE) is an autoimmune disease affecting mostly women of child-bearing age. Immune dysfunction in SLE results from disrupted apoptosis which lead to an unregulated interferon (IFN) stimulation and the production of autoantibodies, leading to immune complex formation, complement activation, and organ damage. Lupus nephritis (LN) is a common and severe complication of SLE, impacting approximately 30% to 40% of SLE patients. Recent studies have demonstrated an alteration in mitochondrial homeostasis in SLE patients. Mitochondrial dysfunction contributes significantly to SLE pathogenesis by enhancing type 1 IFN production through various pathways involving neutrophils, platelets, and T cells. Defective mitophagy, the process of clearing damaged mitochondria, exacerbates this cycle, leading to increased immune dysregulation. In this review, we aim to detail the physiopathological link between mitochondrial dysfunction and disease activity in SLE. Additionally, we will explore the potential role of mitochondria as biomarkers and therapeutic targets in SLE, with a specific focus on LN. In LN, mitochondrial abnormalities are observed in renal cells, correlating with disease progression and renal fibrosis. Studies exploring cell-free mitochondrial DNA as a biomarker in SLE and LN have shown promising but preliminary results, necessitating further validation and standardization. Therapeutically targeting mitochondrial dysfunction in SLE, using drugs like metformin or mTOR inhibitors, shows potential in modulating immune responses and improving clinical outcomes. The interplay between mitochondria, immune dysregulation, and renal involvement in SLE and LN underscores the need for comprehensive research and innovative therapeutic strategies. Understanding mitochondrial dynamics and their impact on immune responses offers promising avenues for developing personalized treatments and non-invasive biomarkers, ultimately improving outcomes for LN patients.

Serum long non-coding Ribonucleic Acid H19 serves as a biomarker for systemic lupus erythematosus and participates in the disease progression.

This study aimed to investigate the expression of H19 and its possible molecular mechanism in systemic lupus erythematosus (SLE). The expression of H19 and miR-19b in serum and peripheral blood mononuclear cells (PBMCs) were detected by reverse transcription quantitative polymerase chain reaction (RT-qPCR). Receiver operator characteristic (ROC) curve was constructed to evaluate the diagnostic value of serum H19 in SLE. Pearson correlation coefficient was used to analyze the correlation between serum levels of H19 and miR-19b. Flow cytometry and Cell counting kit-8 (CCK-8) assay were performed to detect cell apoptosis and viability. The levels of pro-inflammatory and anti-inflammatory factors were measured by enzyme-linked immunosorbent assay (ELISA). Luciferase reporter gene assay was conducted to verify the interaction between H19 and miR-19b. The expression of H19 and miR-19b in SLE group were up-regulated and down-regulated, respectively. Serum H19 has certain clinical diagnostic value in SLE. In in vitro studies, overexpression of H19 can significantly inhibit the viability of PBMCs and promote apoptosis and inflammatory response of PBMCs by interacting with miR-19b. The expression of H19 is upregulated in patients with SLE and plays a role in cell function and inflammation by targeting miR-19b in PBMCs, which may be one of the pathological mechanisms of SLE.

Targeted metabolomics combined with machine learning to identify and validate new biomarkers for early SLE diagnosis and disease activity

BackgroundThe early diagnosis of systemic lupus erythematosus (SLE) and the assessment of disease activity progression remain a great challenge. Targeted metabolomics has great potential to identify new biomarkers of SLE. MethodsSerum from 44 healthy participants and 89 SLE patients were analyzed using HM400 high-throughput targeted metabolomics. Machine learning (ML) with seven learning models and trained the model several times iteratively selected the two best prediction model in a competitive way, which were independent validated by enzyme-linked immunosorbent (ELISA) with 90 SLE patients. ResultsIn this study, 146 differential metabolites, most of them organic acids, amino acids, and bile acids, were detected between patients with initial SLE and healthy participants, and 8 potential biomarkers were found by intersection of ML and statistics (area under the curve [AUC] > 0.95) showing a significant positive correlation with clinical indicators. In addition, we identified and validated 2 potential biomarkers for SLE classification (P < 0.05, AUC > 0.775; N-Methyl-L-glutamic acid, L-2-aminobutyric acid) showing a significant correlation with the SLE Disease Activity Index. These differential metabolites were mainly involved in metabolic pathways, amino acid biosynthesis, 2-oxocarboxylic acid metabolism and other pathways. ConclusionThis study indicated that the tricarboxylic acid cycle might be associated with SLE drug therapy. We identified 8 diagnostic models biomarkers and 2 biomarkers that could be used to identify initial SLE and distinguish different activity degree, which will promote the development of new tools for the diagnosis and evaluation of SLE.

Serum Uric Acid Combined with Homocysteine as a Predictive Biomarker of Lupus Nephritis.

Serum uric acid (UA) and homocysteine (Hcy) are potential biomarkers of systemic lupus erythematosus (SLE). In this study, the expressions of UA and Hcy in SLE patients and the predictive value of these two parameters for lupus nephritis (LN) were studied. A total of 476 SLE patients were recruited to this case-control study, of which 176 SLE patients diagnosed with LN and 300 without LN. Serum UA and Hcy levels were analyzed. Multivariate logistic regression analysis was used to evaluate the relationship between serum UA and Hcy and LN. The receiver operating characteristic (ROC) curves were used to predict the role of combination of serum UA and Hcy in LN. We found that serum UA and Hcy levels in SLE patients with LN were significantly higher than those in controls (p<0.05). Multivariate logistic regressions showed that serum UA (OR+=+1.003, 95+% CI: 1.001-1.006, p+=+0.003), apolipoprotein B (Apo B) (OR+=+21.361, 95+% CI: 2.312-195.373, p+=+0.007) and Hcy (OR+=+1.042, 95+% CI: 1.011-1.080, p+=+0.014) were independent markers of LN. Combined serum UA and Hcy revealed a better result (AUC+=+0.718, 95+% CI: 0.670-0.676, p<0.001) in prediction of LN compared to that of the serum UA (AUC+=+0.710) and Hcy (AUC+=+0.657) independently. In conclusion, serum UA and Hcy could be predictive biomarkers of LN, and joint detection of serum UA and Hcy might be useful in the clinical setting.

ITRAQ-based mass spectrometry screen to identify serum biomarkers in systemic lupus erythematosus

ObjectiveSystemic lupus erythematosus (SLE) is a complex systemic autoimmune disorder with no reliable serum biomarkers currently available other than autoantibodies.MethodsIn the present study, isobaric tags for relative and absolute quantitation-based...

Urinary metabolomic profiling of a cohort of Colombian patients with systemic lupus erythematosus

Systemic lupus erythematosus (SLE) is an autoimmune and multisystem disease with a high public health impact. Lupus nephritis (LN), commonly known as renal involvement in SLE, is associated with a poorer prognosis and increased rates of morbidity and mortality in patients with SLE. Identifying new urinary biomarkers that can be used for LN prognosis or diagnosis is essential and is part of current active research. In this study, we applied an untargeted metabolomics approach involving liquid and gas chromatography coupled with mass spectrometry to urine samples collected from 17 individuals with SLE and no kidney damage, 23 individuals with LN, and 10 clinically healthy controls (HCs) to identify differential metabolic profiles for SLE and LN. The data analysis revealed a differentially abundant metabolite expression profile for each study group, and those metabolites may act as potential differential biomarkers of SLE and LN. The differential metabolic pathways found between the LN and SLE patients with no kidney involvement included primary bile acid biosynthesis, branched-chain amino acid synthesis and degradation, pantothenate and coenzyme A biosynthesis, lysine degradation, and tryptophan metabolism. Receiver operating characteristic curve analysis revealed that monopalmitin, glycolic acid, and glutamic acid allowed for the differentiation of individuals with SLE and no kidney involvement and individuals with LN considering high confidence levels. While the results offer promise, it is important to recognize the significant influence of medications and other external factors on metabolomics studies. This impact has the potential to obscure differences in metabolic profiles, presenting a considerable challenge in the identification of disease biomarkers. Therefore, experimental validation should be conducted with a larger sample size to explore the diagnostic potential of the metabolites found as well as to examine how treatment and disease activity influence the identified chemical compounds. This will be crucial for refining the accuracy and effectiveness of using urine metabolomics for diagnosing and monitoring lupus and lupus nephritis.

LncRNA SNHG1 serves as a biomarker for systemic lupus erythematosus and participates in the disease progression.

LncRNAs play an important role in autoimmune diseases. The purpose of this study was to explore the role of lncRNA SNHG1 in systemic lupus erythematosus (SLE), and laid a theoretical foundation for the study of SLE. The basic clinical information of all subjects was first collected for statistical analysis, and SNHG1 expression in the serum of all subjects was detected by RT-qPCR. The value of SNHG1 in the diagnosis of SLE was assessed by ROC. The correlation between SNHG1 and each blood sample index was analyzed by Pearson correlation analysis. The role of SNHG1 in primary peripheral blood mononuclear cells (PBMCs) apoptosis was explored. SNHG1 expression is relatively upregulated in patients with SLE compared to healthy people. SNHG1 expression was positively correlated with SLEDAI score, IgG, CRP, and ESR, and negatively correlated with C3 and C4. ROC indicated that SNHG1 has the potential to assistin the diagnosis of SLE. PBMCs apoptosis in SLE was higher than that in control group, the knockdown and overexpression of SNHG1 could correspondingly inhibit and promote PBMCs apoptosis. SNHG1 has the potential to be a diagnosis marker for SLE and may be involved in regulating PBMCs apoptosis.

Mapping theme evolution and identifying hotspots in biomarkers of systemic lupus erythematosus based on global research.

Objective: To perform a bibliometric analysis in the field of biomarkers for systemic lupus erythematosus. Methods: Publications were from Web of Science. Microsoft Excel, VOSviewer, Science Mapping Analysis software Tool, CiteSpace and Tableau were used for analysis. Results: A total of 1112 publications were identified; 1503 institutions from 69 countries contributed, with the highest outputs from China and Karolinska University Hospital. Petri had a tremendous impact. Academic collaborations were localized. Lupus and Arthritis & Rheumatology were the top two journals in terms of publications and citations. Lymphocyte, autoantibody, type I interferon, genetic polymorphisms and urinary biomarkers have been high-frequency themes. Conclusion: Global collaboration needs to be further strengthened. Immune cell, cytokine and gene-level research as a whole and noninvasive tests are the future trends.

Biomarkers in Systemic Lupus Erythematosus along with Metabolic Syndrome.

Metabolic syndrome (MetS) is a group of physiological abnormalities characterized by obesity, insulin resistance (IR), and hypertriglyceridemia, which carry the risk of developing cardiovascular disease (CVD) and type 2 diabetes (T2D). Immune and metabolic alterations have been observed in MetS and are associated with autoimmune development. Systemic lupus erythematosus (SLE) is an autoimmune disease caused by a complex interaction of environmental, hormonal, and genetic factors and hyperactivation of immune cells. Patients with SLE have a high prevalence of MetS, in which elevated CVD is observed. Among the efforts of multidisciplinary healthcare teams to make an early diagnosis, a wide variety of factors have been considered and associated with the generation of biomarkers. This review aimed to elucidate some primary biomarkers and propose a set of assessments to improve the projection of the diagnosis and evolution of patients. These biomarkers include metabolic profiles, cytokines, cardiovascular tests, and microRNAs (miRs), which have been observed to be dysregulated in these patients and associated with outcomes.