Neutrophil extracellular traps (NETs) is an important process involved in the pathogenesis of systemic lupus erythematosus (SLE), but the potential mechanisms of NETs contributing to SLE at the genetic level have not been clearly investigated. This investigation aimed to explore the molecular characteristics of NETs-related genes (NRGs) in SLE based on bioinformatics analysis, and identify associated reliable biomarkers and molecular clusters. Dataset GSE45291 was acquired from the Gene Expression Omnibus repository and used as a training set for subsequent analysis. A total of 1006 differentially expressed genes (DEGs) were obtained, most of which were associated with multiple viral infections. The interaction of DEGs with NRGs revealed 8 differentially expressed NRGs (DE-NRGs). The correlation and protein-protein interaction analyses of these DE-NRGs were performed. Among them, HMGB1, ITGB2, and CREB5 were selected as hub genes by random forest, support vector machine, and least absolute shrinkage and selection operator algorithms. The significant diagnostic value for SLE was confirmed in the training set and three validation sets (GSE81622, GSE61635, and GSE122459). Additionally, three NETs-related sub-clusters were identified based on the hub genes’ expression profiles analyzed by unsupervised consensus cluster assessment. Functional enrichment was performed among the three NETs subgroups, and the data revealed that cluster 1 highly expressed DEGs were prevalent in innate immune response pathways while that of cluster 3 were enriched in adaptive immune response pathways. Moreover, immune infiltration analysis also revealed that innate immune cells were markedly infiltrated in cluster 1 while the adaptive immune cells were upregulated in cluster 3. As per our knowledge, this investigation is the first to explore the molecular characteristics of NRGs in SLE, identify three potential biomarkers (HMGB1, ITGB2, and CREB5), and three distinct clusters based on these hub biomarkers.

Background: Systemic lupus erythematosus (SLE) is a multifaceted autoimmune disease characterized by clinical and pathological diversity. Mitochondrial dysfunction has been identified as a critical pathogenetic factor in SLE. However, the specific molecular aspects and regulatory roles of this dysfunction in SLE are not fully understood. Our study aims to explore the molecular characteristics of mitochondria-related genes (MRGs) in SLE, with a focus on identifying reliable biomarkers for classification and therapeutic purposes. Methods: We sourced six SLE-related microarray datasets (GSE61635, GSE50772, GSE30153, GSE99967, GSE81622, and GSE49454) from the Gene Expression Omnibus (GEO) database. Three of these datasets (GSE61635, GSE50772, GSE30153) were integrated into a training set for differential analysis. The intersection of differentially expressed genes with MRGs yielded a set of differentially expressed MRGs (DE-MRGs). We employed machine learning algorithms—random forest (RF), support vector machine (SVM), and least absolute shrinkage and selection operator (LASSO) logistic regression—to select key hub genes. These genes’ classifying potential was validated in the training set and three other validation sets (GSE99967, GSE81622, and GSE49454). Further analyses included differential expression, co-expression, protein-protein interaction (PPI), gene set enrichment analysis (GSEA), and immune infiltration, centered on these hub genes. We also constructed TF-mRNA, miRNA-mRNA, and drug-target networks based on these hub genes using the ChEA3, miRcode, and PubChem databases. Results: Our investigation identified 761 differentially expressed genes (DEGs), mainly related to viral infection, inflammatory, and immune-related signaling pathways. The interaction between these DEGs and MRGs led to the identification of 27 distinct DE-MRGs. Key among these were FAM210B, MSRB2, LYRM7, IFI27, and SCO2, designated as hub genes through machine learning analysis. Their significant role in SLE classification was confirmed in both the training and validation sets. Additional analyses included differential expression, co-expression, PPI, GSEA, immune infiltration, and the construction of TF-mRNA, miRNA-mRNA, and drug-target networks. Conclusions: This research represents a novel exploration into the MRGs of SLE, identifying FAM210B, MSRB2, LYRM7, IFI27, and SCO2 as significant candidates for classifying and therapeutic targeting.

To assess the efficacy/safety of ivarmacitinib, a selective Janus kinase (JAK) 1 inhibitor, in patients with moderate-to-severe active rheumatoid arthritis (RA) who had an inadequate response to conventional synthetic disease-modifying antirheumatic drugs (csDMARDs). Patients were randomised (1:1:1) to receive either placebo (n=188), ivarmacitinib 4 mg (n=189) or ivarmacitinib 8 mg (n=189) once daily, with background csDMARDs allowed. After 24 weeks, patients on placebo switched to ivarmacitinib 4 mg for an additional 28 weeks, while those on ivarmacitinib continued their initial dosage. The primary endpoint was the proportion of patients achieving a 20% improvement in the American College of Rheumatology response criteria (ACR20) at week 24. At week 24, ACR20 response rates were significantly higher in the ivarmacitinib 4 mg (70.4%) and 8 mg (75.1%) groups compared with the placebo group (40.4%; both p<0.0001). Both ivarmacitinib doses achieved numerically higher Disease Activity Score 28-joint count C reactive protein of <2.6/≤3.2 response rates compared with placebo. Improvements in efficacy and patient-reported outcomes were sustained through 52 weeks and were noted in patients who switched from placebo after week 24. During the placebo-controlled period, treatment-emergent adverse events (TEAEs) occurred in 81.5% and 90.5% of patients in the ivarmacitinib 4 mg and 8 mg groups, versus 79.3% in the placebo group. Infection-related TEAEs were slightly higher in the ivarmacitinib groups. Ivarmacitinib may offer a potential therapeutic option for patients with RA who have an inadequate response to csDMARDs, with a safety profile that was generally manageable over 1 year of treatment and similar to other JAK inhibitors. NCT04333771.

Background: We aimed to compare the clinical characteristics of patients with systemic sclerosis (SSc) with or without interstitial lung disease (ILD) to identify relationships with the presence of ILD in SSc at a single center in China.Methods: A cross-sectional study was conducted using retrospective data from the Chinese Rheumatology Data Center. Patients diagnosed with SSc at the Second Affiliated Hospital of Nanchang University between 2013 and 2022 were included. Demographic and clinical characteristics were compared between patients with SSc with and without ILD. Logistic regression analyses were performed to explore these associations.Results: A total of 227 patients with SSc were included (male:female ratio = 1:4.82), of which 121 (53.3%) were accompanied with ILD. SSc patients with ILD had a higher percentage of diffuse cutaneous systemic sclerosis (dcSSc), sclerodactyly, loss of finger pad, muscle involvement, left ventricular diastolic dysfunction (LVDD), and pulmonary hypertension (PAH), elevated Krebs von den Lungen-6 (KL-6), and elevated ferritin than those without ILD, and a higher modified Rodnan skin score (mRSS), neutrophil-to-lymphocyte ratio (NLR) and platelet-to-lymphocyte ratio (PLR) (all P < 0.05). Antinuclear antibody (ANA) and anti-scleroderma-70 (anti-Scl-70) positivity was presented frequently in SSc patients with ILD, while SSc patients without ILD were more often anti-centromere antibody (ACA) positive (all P < 0.05). On the multivariable analysis, muscle involvement [OR 2.551 (95% CI 1.054–6.175), P = 0.038], LVDD [OR 2.360 (95% CI 1.277–4.361), P = 0.006], PAH [OR 9.134 (95% CI 2.335–35.730), P = 0.001], dcSSc [OR 2.859 (95% CI 1.489–5.487), P = 0.002], PLR [OR 1.005 (95% CI 1.001–1.008), P = 0.020], elevated KL-6 [OR 2.033 (95% CI 1.099–3.763), P = 0.024], and anti-Scl-70 [OR 3.101 (95% CI 1.647–5.840), P < 0.001] were statistically significant associations with SSc patients with ILD.Conclusion: Systemic sclerosis was found mainly in females. Several important differences in clinical and laboratory characteristics have been demonstrated between SSc patients with or without ILD. Muscle involvement, LVDD, PAH, dcSSc, PLR, elevated KL-6, and Anti-Scl-70 antibody may be associated with SSc in patients with ILD.

The relationship between rheumatoid arthritis (RA) and diffuse large B-cell lymphoma (DLBCL) is well characterized, but the molecular mechanisms underlying this association have not been clearly investigated. Our study aimed to identify shared gene signatures and molecular mechanisms between RA and DLBCL. We selected multiple Gene Expression Omnibus (GEO) datasets (GSE93272, GSE83632, GSE12453, GSE1919) to obtain gene expression levels and clinical information about patients with RA and DLBCL. Weighted gene co-expression network analysis (WGCNA) was used to research co-expression networks associated with RA and DLBCL. Subsequently, we performed enrichment analysis of shared genes and screened the most significant core genes. We observed expression of the screened target gene, galectin 2 (LGALS2), in DLBCL patients and its impact on patient prognosis. Finally, we analyzed the molecular functional mechanism of LGALS2 and observed its relationship with the immune response in DLBCL using single-sample Gene Set Enrichment Analysis (ssGSEA). WGCNA recognized two major modules for RA and DLBCL, respectively. Shared genes (551) were identified for RA and DLBCL by observing the intersection. In addition, a critical shared gene, LGALS2, was acquired in the validation tests. Next, we found that the expression level of LGALS2 gradually decreased with tumor progression in DLBCL and that increased expression of LGALS2 predicted a better prognosis for DLBCL patients. ssGSEA revealed that LGALS2 is involved in immune-related pathways and has a significant regulatory effect on human immune responses. Additionally, we observed that LGALS2 is closely related to the sensitivity of multiple chemotherapeutic drugs. There is extremely little research on the molecular mechanism of correlation between RA and DLBCL. Our study identified that LGALS2 is a potential therapeutic target and an immune-related biomarker for patients with RA and DLBCL.