This study is a meta-analysis aimed at pooling reported data and clarifying the association between circulating level of interleukin-18 and systemic lupus erythematosus (SLE). We searched medical databases including Medline/Pubmed, Embase, Scopus, The Cochrane Library, and Web of Science thoroughly to obtain all related articles published before July 15th, 2020. We pooled computed standardized mean difference (SMD) and its 95% confidence interval using STATA 13.0 and exhibited in the form of forest graph. Meta-regression and subgroup analysis were also performed to explore the source of heterogeneity. Publication bias was first evaluated by the symmetry of the funnel plot and then Egger’s linear regression test. Thirty eligible studies from eighteen regions were finally included and the relevant data from these studies were pooled. The analysis results displayed that SLE patients showed a significantly higher level of circulating IL-18 level in comparison with healthy controls (SMD = 1.56, 95% CI [1.20–1.93]; I² = 94.9%,p < 0.01). The conclusion was equally applicable in subgroups divided based on sample type, mean age, disease duration, and testing method. Patients with SLEDAI score higher than five, or who were Asian, White, Arab, or mixed ethnicity had an elevated level of IL-18, while the others didn’t. This meta-analysis has elucidated that compared with healthy people, the circulating level of IL-18 is considerably higher in SLE patients, which indicates the underlying role of IL-18 in SLE pathogenesis.

Background: Systemic lupus erythematosus (SLE) is an immune-mediated disease with widespread involvement, and its pathogenesis remains incompletely understood. Recent studies suggest that modifications such as acetylation and lactylation play crucial roles in SLE progression, with potential interrelationships between them. This study aimed to identify biomarker genes co-associated with both lactylation and acetylation and to explore their potential mechanisms in SLE pathogenesis. Methods: Microarray data from peripheral blood mononuclear cells (PBMCs) of SLE patients and healthy controls were obtained from the Gene Expression Omnibus (GEO) database. In the training dataset (GSE81622), differential expression analysis was performed to compare SLE samples with healthy controls. Lactate- and acetylation-related genes were used to identify differentially expressed lactate-related genes (LR-DEGs) and acetylation-related genes (AR-DEGs). Genes co-associated with both lactylation and acetylation were further examined. LASSO regression, support vector machine recursive feature elimination (SVM-RFE), and ROC curve analysis were used to identify hub genes. Immune infiltration analysis and a clinical nomogram model were developed for accurate diagnosis and treatment prediction. qPCR was used to validate the hub genes. Results: A total of 1181 differentially expressed genes (DEGs) were identified between SLE and healthy groups. Of these, 33 LR-DEGs and 28 AR-DEGs were identified. Seven genes were found to be co-associated with both lactylation and acetylation. Using LASSO and SVM-RFE, two hub genes, CDCA5 and MCTS1, were identified and validated in the GSE24706 dataset. ROC curve analysis and clinical nomogram revealed significant associations of these biomarkers with SLE pathogenesis. Conclusions: Our study identifies CDCA5 and MCTS1 as potential biomarkers for SLE, potentially influencing its pathogenesis through histone lactylation and acetylation. Experimental validation confirmed their differential expression between SLE patients and healthy controls. These findings underscore the role of epigenetic modifications in SLE, offering new insights into its regulatory mechanisms and immune interactions.

Background/Objectives: Epidemiological studies link autoimmune diseases (AIDs) to follicular lymphoma (FL) risk, but their shared genetic architecture and causal mechanisms remain unclear. Methods: A two-sample Mendelian randomization (MR) analysis was employed to assess causal relationships between 15 AIDs and FL. Pleiotropic loci were identified through the Pleiotropy Analysis under Composite Null (PLACO). Bayesian colocalization analysis, functional mapping, and Multi-marker Analysis of GenoMic Annotation were applied to fine-map shared genetic variants and identify their target genes. Summary data-based MR was used with multitissue expression quantitative trait locus data to infer causal effects of gene expression. HyPrColoc analysis was applied to decipher shared genetic regulation of immune cell phenotypes. Results: MR revealed that rheumatoid arthritis increased FL risk (OR_IVW = 1.55, nominal p = 7.16 × 10⁻⁵, FDR-corrected p = 1.07 × 10⁻³), whereas composite autoimmune disease reduced FL risk (OR_IVW = 0.70, nominal p = 4.61 × 10⁻⁵, FDR-corrected p = 6.92 × 10⁻⁴). Hypothyroidism showed only a nominally suggestive protective trend (OR_IVW = 0.88, nominal p = 0.015), which did not survive Benjamini–Hochberg multiple-testing correction (FDR-corrected p = 0.075). Fifty-five pleiotropic loci shared between FL and AIDs were identified, among which key loci such as 1p36.32, 6p21.32, 17p13.1, and 11q23.3 exhibited strong colocalization evidence. Core pleiotropic genes (e.g., TNFRSF14, MMEL1, CXCR5, and RNASET2) were prioritized, which implicated pathways related to MHC class II antigen presentation, interferon signaling, and T cell activation. HyPrColoc analysis demonstrated that these loci colocalized with the expression of immune receptors, including BAFF-R on B cells and HVEM (TNFRSF14) on naïve CD8⁺ T cells. Conclusions: Our study identifies divergent causal effects of selected AIDs on FL risk and demonstrates localized pleiotropy at key loci, providing novel insights into shared immunogenetic mechanisms.