Identification of JAK2 and CXCL10 as ammonia death related biomarkers in systemic lupus erythematosus based on transcriptomic analysis

Identification of JAK2 and CXCL10 as ammonia death related biomarkers in systemic lupus erythematosus based on transcriptomic analysis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Identification of JAK2 and CXCL10 as ammonia death related biomarkers in systemic lupus erythematosus based on transcriptomic analysis Manli Feng, Yu Lu, Jin Zou, Pengfei Cun, Yuan Liu, Chenxi Liao, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9439399/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Background Systemic lupus erythematosus (SLE) mechanisms and ammonia death (AD) roles remain unclear. Methods Data on SLE and AD-related genes (ADRGs) were collected from public databases. Candidate genes were acquired by intersecting ADRGs with differentially expressed genes (DEGs) between SLE and control groups. Biomarkers were screened via protein-protein interaction (PPI) network analysis, machine learning and receiver operating characteristic (ROC) curve analysis. A nomogram was constructed, followed by enrichment analysis, immune microenvironment evaluation, regulatory factor and drug prediction. Results Fifty-five candidate genes were identified, and Janus kinase 2 (JAK2) and C-X-C motif chemokine ligand 10 (CXCL10) were selected as key biomarkers with reliable diagnostic value. The nomogram showed favorable predictive performance. Gene set enrichment analysis (GSEA) revealed JAK2 participated in innate immune pathways, while CXCL10 was linked to immune and metabolic processes. JAK2 correlated significantly with neutrophils and resting natural killer (NK) cells. Hsa-miR-582-5p and peginterferon alfa-2b were identified as shared regulatory factors. Conclusion The study identified JAK2 and CXCL10 as biomarkers for SLE, offering potential therapeutic targets for patients with the condition. Health sciences/Biomarkers Biological sciences/Computational biology and bioinformatics Health sciences/Diseases Biological sciences/Immunology Health sciences/Rheumatology Ammonia death Biomarkers Machine learning Regulatory network Systemic lupus erythematosus Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1. Introduction Systemic Lupus Erythematosus (SLE) is an autoimmune disease that affects multiple organs and systems [1] . It causes inflammation and damage as the immune system abnormally attacks the body's own tissues and organs [2] . The global incidence of SLE is approximately 20-70 cases per 100,000 people, with variations across different regions and populations [3] . Notably, the incidence rate in women is significantly higher than that in men [4] . Currently, the main treatment approaches for SLE include glucocorticoids, immunosuppressants, and biological agents, which aim to control inflammation and inhibit abnormal immune responses. However, existing therapies have limitations: there are significant individual differences in treatment responses; long-term use leads to severe side effects (such as increased risk of infection and organ damage); some patients show poor response to medications. Moreover, these therapies can hardly halt disease progression or achieve a radical cure [5-8] . Chimeric Antigen Receptor T-cell Therapy (CAR-T Therapy), as a highly promising emerging treatment, has demonstrated potential for application, but its clinical translation still requires in-depth research and verification [9,10] . Recent studies have revealed that the molecular mechanisms of SLE are associated with multiple factors, involving two key mediator families: type I interferons (IFN-I) and autoantibodies targeting nucleic acids and nucleic acid-binding proteins [11] . Therefore, the identification of novel SLE biomarkers is crucial for elucidating disease pathogenesis and paving the way for more effective therapies. Under physiological conditions, ammonia is the nitrogen-containing end product of amino acid metabolism. It is mainly excreted from the body through urea synthesis in the liver, and simultaneously acts as a neurotransmitter to participate in signal transmission in the central nervous system. Studies indicate that excessive ammonia accumulation alkalinizes lysosomes, thereby disrupting their capacity for ammonia sequestration. The subsequent backflow of ammonia into mitochondria triggers cell death by inducing mitochondrial damage, a process characterized by lysosomal alkalinization, mitochondrial swelling, and impaired autophagic flux [12,13] . Excessively high ammonia concentration in the blood triggers hyperammonemia, which is closely associated with disrupted potassium homeostasis, mitochondrial dysfunction, oxidative stress, inflammation, hypoxemia, and dysregulated neurotransmission [14-16] . It has been reported that patients with SLE may complicate with hyperammonemia [17] . In SLE patients, chronic inflammation and autoimmune attack may cause hepatocellular injury and metabolic abnormalities, reducing the body’s ability to clear ammonia and thereby increasing the risk of hyperammonemia [18] . However, there is currently no research on the association between dysregulated ammonia concentration and SLE. To gain an in-depth understanding of this association, studies on AD-related genes (ADRGs) are particularly important. These genes hold promise as novel diagnostic or therapeutic targets for SLE, paving the way for developing more effective treatment strategies in the future. In this study, using SLE data and ADRGs retrieved from online databases and relevant literature, we identified biomarkers associated with AD in SLE through bioinformatics approaches including protein-protein interaction (PPI) networks, machine learning, and expression level validation. The study further explored the molecular mechanisms of these biomarkers in the disease, thereby providing new references for the precise diagnosis and personalized treatment regimens of SLE patients. 2. Results 2.1 Discovery of 55 candidate genes and investigation into their biological roles After performing differential analysis on the GSE112087 dataset to distinguish DEGs between SLE and control groups, a total of 1,460 DEGs were obtained. Specifically, 383 genes showed down-regulation, and 1,077 genes exhibited up-regulation. As an illustration, IFI27 and USP18 exhibited significant up-regulation in the SLE group, whereas SLC4A10 and GRIN1 showed notable down-regulation. Results were presented using a volcano plot and a heatmap (Figure 1a-b) . Taking the intersection of 1,460 DEGs and 467 ADRGs yielded 55 candidate genes, such as ZBP1, ADAR, and MYD88 (Fig ure 1c, Supplementary Table S1) . Moreover, candidate genes significantly accumulated within 321 GO terms (p.adjust < 0.05), comprising 266 BPs, 22 CCs, and 33 MFs ( Supplementary Table S2) . The top 10 of these BPs include "macroautophagy" and "response to virus". The top 10 enriched CCs include "endocytic vesicle" and "autophagosome". The top 10 of these MFs include "GTPase activity" and "quaternary ammonium group binding" (Fig ure 1d) . The 55 candidate genes were mapped to 14 KEGG pathways ( Supplementary Table S3) , such as the "NOD-like receptor signaling pathway" and "Herpes simplex virus 1 infection" (Figure 1e) . Through GO and KEGG analyses, these candidate genes were shown to correlate with multiple critical signaling pathways, like autophagy and viral response. 2.2 Acquisition of 2 biomarkers: JAK2 and CXCL10 The MNC algorithm identified genes such as CXCL10 and STAT1 as highly important (Fig ure 2a) , whereas the Degree algorithm highlighted genes such as GBP5 and TAP1 as core genes (Fig ure 2b) . By intersecting the results, 18 hub genes were obtained, including JAK2, STAT1, and CXCL11 (Fig ure 2c) . Furthermore, the top 10 genes ranked by MeanDecreaseGini in the RF algorithm included JAK2, CXCR6, and IRGM (Fig ure 2d-e) . Moreover, SVM-RFE screened 8 characteristic genes when the correct rate was maximum, such as CXCR6, GBP5, and JAK2 (Fig ure 2f) . In the LASSO algorithm, when the log(lambda.min) was 0.0271, the model was optimal and 6 characteristic genes were obtained, such as JAK2, CXCR6, and LAP3 (Fig ure 2g) . Following the application of the 3 algorithms, their resulting gene sets were intersected, yielding 3 key genes: CXCR6, JAK2, and CXCL10 (Fig ure 2h) . The gene expression profiles of these key genes were next inspected in two atasets. The results revealed that all 3 key genes (CXCR6, JAK2, and CXCL10) exhibited conserved expression profiles in both datasets and showed marked intergroup differences (p < 0.05), which could serve as candidate biomarkers (Fig ure 3a-b) . Among the 3 candidate biomarkers, CXCR6 demonstrated a significantly higher expression level in the control group. In contrast, JAK2 and CXCL10 exhibited notably higher levels of expression in the SLE group. The ROC curves demonstrated that only JAK2 (GSE112087: AUC = 0.84, GSE72509: AUC = 0.804) and CXCL10 (GSE112087: AUC = 0.74, GSE72509: AUC = 0.817) exhibited commendable predictive capabilities (Figure 3c-d) . Thus, JAK2 and CXCL10 were identified as biomarkers for SLE and included in subsequent analyses. 2.3 The nomogram performed favourably in diagnosing SLE To further assess the diagnostic utility of JAK2 and CXCL10, a predictive nomogram was developed. The nomogram assigned points to 2 biomarkers and calculated total points to predict an outcome. According to the nomogram, a total score of 159 points (from JAK2 and CXCL10) mapped to an SLE probability of 91.6%, revealing a direct association between higher composite scores and increased disease risk (Figure 3e) . In addition, the nomogram model demonstrated a low diagnostic error rate (HL test: p = 0.279), as substantiated by the calibration curve depicted in Figure 3f . The DCA curves confirmed that the nomogram had great clinical utility (Figure 3g) . And ROC curve revealed that nomogram showed high diagnostic value (AUC = 0.863) (Figure 3h) . To sum up, a well-constructed nomogram was developed, and calibration, DCA, and ROC curves demonstrated its favorable predictive performance. 2.4 The biological functions of JAK2 and CXCL10 were further explored GSEA evidenced that JAK2 was enriched in 42 pathways, and CXCL10 was present in 24 pathways (p.adjust 1) ( Supplementary Table S4) . Notably, JAK2 was primarily enriched in pathways associated with innate immune signaling, such as Nod like, Toll Like, and Rig I Like receptor signaling pathways (Figure 4a) . In contrast, CXCL10 was primarily enriched in pathways associated with physiological and pathological processes such as autoimmune disorders (systemic lupus erythematosus) and energy metabolism (oxidative phosphorylation) (Figure 4b) . The GeneMANIA prediction results showed that JAK2 and CXCL10 were primarily associated with 20 genes, such as CXCL9 and PPBP, and they interacted with each other mainly through physical interactions, shared protein domains, etc. Additionally, CXCL10 was potentially related to genes including CXCL9, CXCL11, and CCL13 in functions such as "cytokine activity" and "G protein-coupled receptor binding", while JAK2 was potentially associated with genes like CXCL6, PIK3R1, and XCL1 in the function of "cytokine receptor binding" (Figure 4c) . The above results facilitated in-depth understanding of the biological functions of JAK2 and CXCL10. 2.5 Significant relationships between immune infiltrating cells and biomarkers Immune cell infiltration is closely associated with SLE, influencing disease severity and immune responses. The immune infiltration landscape of 22 cell types in GSE112087 (SLE vs. controls) is depicted in Figure 4d , with five cell types showing significant differential abundance (p < 0.05), such as resting NK cells (p < 0.0001) and Neutrophils (p < 0.01) (Figure 4e) . A robust positive link was identified between resting memory CD4 T cells and CD8 T cells (cor = 0.711, p = 2.61e-10); conversely, a marked inverse correlation was found between CD8 T cells and neutrophils (cor = -0.752, p = 1.78e-10) (Figure 4f) . In addition, JAK2 demonstrated opposing correlations with neutrophils (positive; cor = 0.329, p = 0.0108) and resting NK cells (negative; cor = -0.447, p = 0.00039). CXCL10, however, was not significantly associated with any differentially infiltrated immune cells (Figure 4g) . Collectively, these results indicated that immune cell infiltration might play a significant regulatory role in SLE. 2.6 miRNA hsa-miR-582-5p and drug peginterferon alfa-2b were identified to simultaneously target JAK2 and CXCL10 Molecular regulatory networks offered additional insights into the regulatory factors affecting JAK2 and CXCL10. Specifically, 7 key miRNAs targeting JAK2 and 5 key miRNAs targeting CXCL10 were predicted, among which hsa-miR-582-5p was involved in the regulation of both JAK2 and CXCL10 (Figure 5a) . Meanwhile, as shown in Figure 5b , 5 key TFs targeting JAK2 and 2 key TFs targeting CXCL10 were predicted. Subsequently, potential drugs for SLE were explored. A total of 98 drugs targeting JAK2 and 16 drugs targeting CXCL10 were predicted, among which a common drug for both JAK2 and CXCL10, peginterferon alfa-2b, was identified (Figure 5c) . The above results facilitated the revelation of regulatory mechanisms of JAK2 and CXCL10 at the molecular level, and provided new scientific evidence for the treatment of SLE. 2.7 Clinical trial validation of biomarkers 3. Discussion According to existing research reports, in patients with SLE, chronic inflammation and autoimmune attack may cause hepatocellular injury and metabolic abnormalities, which reduce the body’s ability to clear ammonia, thereby increasing the risk of hyperammonemia [17] . Hyperammonemia is implicated in various pathological dysfunctions, including mitochondrial impairment and inflammatory responses, and is strongly associated with a spectrum of liver diseases [14-16,19] . Additionally, dysregulated ammonia concentration can trigger AD, impairing lysosomal and mitochondrial functions [12] . Consequently, elucidating the potential association between SLE and AD could broaden our understanding of SLE pathogenesis and provide a novel therapeutic rationale for its management. In this study, we identified 55 candidate genes associated with AD in SLE. Functional enrichment analysis revealed these genes are predominantly linked to immune and antiviral response pathways. Eventually, Janus kinase 2 (JAK2) and small inducible cytokine subfamily B (Cys-X-Cys), member 10 (CXCL10) were identified as SLE biomarkers. A systematic investigation of these biomarkers was conducted to elucidate their regulatory functions in SLE pathogenesis. JAK2 (Janus Kinase 2) , a non-receptor tyrosine kinase, primarily mediates cytokine (e.g., erythropoietin, growth hormone) signal transduction [20] . By phosphorylating/activating STAT proteins, it regulates cell proliferation, differentiation, apoptosis, and immune response, playing a key role in hematopoietic development and homeostasis [21-23] . As a critical node in immune-related pathways, JAK2 is involved in diseases like myeloproliferative neoplasms, osteoarthritis, and leukemia [24-30] . It has been reported that germline mutations and polymorphisms of JAK family members are associated with specific diseases like SLE [31,32] . Tyrosine kinase 2 (TYK2), another member of the JAK family, triggers a unique set of immune events targeting JAK1, JAK2, and JAK3 by participating in downstream signaling pathways of type I interferons, and is linked to susceptibility to SLE [33,34] . JAK2 inhibitor Baricitinib, approved for rheumatoid arthritis and atopic dermatitis, is effective in SLE and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) [35-39] . Additionally, tapinarof, an agonist of the aryl hydrocarbon receptor (AhR), can inhibit the differentiation of follicular helper T (Tfh) cells by regulating the JAK2-STAT3 signaling pathway, thereby alleviating lupus-related autoimmunity [40] . Further research is needed on JAK2’s regulatory mechanisms in SLE progression, and this study provides a new theoretical basis. CXCL10 (Small Inducible Cytokine Subfamily B (Cys-X-Cys) Member 10) primarily exerts its physiological functions in immune cell recruitment and inflammation regulation [41] . These functions include chemotactic migration of immune cells to inflammatory sites, participation in antiviral and antitumor immune responses, involvement in angiogenesis and tissue repair, and maintenance of immune regulation and inflammatory balance [42-47] . It has been reported that CXCL10 antagonists can limit inflammatory immune responses and promote neuronal regeneration and functional recovery, thereby participating in the regulation of spinal cord injury [48] . The IFN-γ-CXCL9/CXCL10-CXCR3 axis plays an important regulatory role in vitiligo, and CXCL10 is also one of the biomarkers for various diseases such as vitiligo, kidney transplantation, leprosy, and inflammatory rheumatic diseases [49-53] . Furthermore, elevated levels of CXCL10 are associated with cytokine storms, which in turn correlate with the severe course and disease progression of SARS-CoV-2 infection [54-56] ; meanwhile, CXCL10 can also serve as a detection biomarker for patients with pulmonary tuberculosis [57,58] . . Studies have shown that patients with early-stage SLE produce significantly higher levels of CXCL10, which may decrease after one year [59,60] . The chemokine family induces autoimmune responses and amplifies the induced inflammatory responses in SLE and related complications (especially lupus nephritis), thus playing a vital role in the pathogenesis [61,62] . For example, in lupus nephritis, CXCL10 can recruit human umbilical cord mesenchymal stem cells (hUC-MSCs) to migrate to the kidneys; blocking the CXCL10-CXCR3 axis impairs the migration of hUC-MSCs to the kidneys and exerts a negative impact on treatment [63] . The more specific regulatory mechanisms of CXCL10 in SLE progression still need in-depth exploration, and this study offers a new perspective for this field. Enrichment analysis showed JAK2 is enriched in antiviral innate immune pathways, including Nucleotide-binding and Oligomerization Domains (Nod), Toll-like receptors (TLR), and Retinoic Acid-Inducible Gene I Protein (RigI). NLRs maintain immune homeostasis by regulating TLR, RLR, and cyclic GMP-AMP synthase-stimulator of interferon genes (cGAS-STING) pathways: upon recognizing pathogen-associated molecules, these pathways induce proinflammatory cytokines/IFN-I. Most RIG-I-like receptors (RLRs) negatively regulate them (e.g., NLRX1 degrades MAVS) to prevent excessive activation, while a subset positively activates them for enhanced antiviral capacity [64] . The core pathology of SLE is excessive activation of innate immunity and hyperactivity of IFN-I. In the TLR pathway, SLE immune complexes activate TLR7/9; impaired NLRP11/NLRX1 function causes excessive pathway activation, increasing IFN-I and inflammatory cytokines. In the RLR pathway, SLE self-RNA activates the RIG-I/MDA5-MAVS axis; NLRC5/NLRX1 abnormalities induce sustained activation and IFN storms. In the cGAS-STING pathway, SLE abnormal cytoplasmic DNA triggers activation; NLRC3/NLRX1 loss-of-function leads to uncontrolled activation, exacerbating immune disorders and disease progression. The hepatocyte glucose/lipid metabolism-TLR3/RLR antiviral immunity cross-talk supports hepatocyte innate immunity and cellular homeostasis via metabolic reprogramming (e.g., glycolytic lactate production, PPARα/SREBPs regulation in lipid metabolism). It regulates disease via "metabolism-immunity balance": physiological metabolites inhibit excessive immune activation, while abnormal metabolism or viral hijacking causes immune evasion and liver injury [65] . This pathway may exacerbate SLE immune dysregulation/organ damage via glucose/lipid metabolic imbalance, indirectly affecting disease progression. CMV evades host innate immunity through PRRs, IFNAR-JAK-STAT, and intrinsic cellular defense (apoptosis/autophagy/endoplasmic reticulum stress) pathways, which maintain antiviral immunity by regulating IFN-I production, proinflammatory cytokine release, and cellular homeostasis. Normally, these pathways activate to resist infection; abnormally, CMV targets key molecules (e.g., US7 degrades TLR3, pM27 degrades STAT2) for immune evasion, leading to persistent infection [66] . Extensive evidence has established that NLRs are critically involved in the pathogenesis of various autoimmune diseases, including inflammatory bowel disease, rheumatoid arthritis, SLE, psoriasis, and multiple sclerosis [67-69] . Furthermore, in SLE, the breakdown of B-cell tolerance to self-antigens is intrinsically regulated by TLRs [70-72] . Our immune infiltration analysis revealed significant differences in immune cell infiltration (neutrophils, resting NK cells) between SLE patients and controls, which correlated with biomarker JAK2—suggesting these infiltration changes may play a key role in SLE progression. As the predominant innate immune leukocytes, neutrophils exert cytotoxic functions (pathogen phagocytosis, ROS/granular protease release, NET formation) and modulate innate/adaptive immunity via cytokine/chemokine secretion [73] . In SLE, neutrophils drive disease via multidimensional abnormal activation: SLE neutrophils show increased ROS production, which directly causes oxidative tissue damage (e.g., vascular endothelium, kidneys; oxidized DNA formsdamage-associated molecular pattern (DAMP) 8-hydroxyguanosine) and activates PAD4 to promote NET formation [73] . NET-exposed self-antigens (double-stranded DNA (dsDNA), citrullinated histones) activate plasmacytoid dendritic cells (pDCs) to produce interferon-alpha (IFN-α) and induce B cells to generate autoantibodies (anti-dsDNA, anti-histone), which form immune complexes depositing in organs like glomeruli, exacerbating injuries such as lupus nephritis [74] . Second, low-density granulocytes (LDGs)—a notably enriched proinflammatory subset in SLE patients’ peripheral blood—positively correlate with disease activity (assessed by SLEDAI scores). Compared with normal-density neutrophils, LDGs are more prone to spontaneous neutrophil extracellular trap (NET) formation and secrete cytokines like interferon-alpha (IFN-α) and tumor necrosis factor-alpha (TNF-α). They inhibit endothelial progenitor cell differentiation, disrupt vascular endothelial barrier function, amplify inflammation via enhanced mitochondrial reactive oxygen species (mtROS) release, and induce abnormal biomechanical properties by altering cytoskeletal protein expression. LDGs are more likely trapped in microvessels, forming microthrombi that contribute to SLE vascular lesions. Thus, subsequent studies can deeply explore specific regulatory mechanisms based on the correlation between JAK2 and neutrophils. 4. Conclusion In this study, two SLE biomarkers (JAK2 and CXCL10) associated with AD were identified through bioinformatics combined with experimental validation, and a nomogram was constructed. Subsequently, to elucidate the underlying mechanisms, a multi-method approach integrating enrichment and immune infiltration analyses was applied, thereby establishing a theoretical foundation for improving SLE diagnosis and treatment. However, this study still has certain limitations: the sample sizes of the database and collected clinical samples are limited, detailed sample information is lacking, and there is significant room for improvement in the accuracy of disease assessment and prediction. In addition, the identified potential interacting genes and the biological pathways involved in regulation need further validation to provide reliable evidence for clinically targeted treatment. In future studies, we will expand the sample scope and conduct in vivo and in vitro experimental validation by constructing animal models, aiming to gain deeper insights. 5. Methods 5.1 Data acquisition Two datasets, GSE112087 and GSE72509 (both on the GPL16791 platform), were sourced from the Gene Expression Omnibus (GEO) database (https://www.ncbi.nlm.nih.gov/geo/). From GSE112087, a total of 31 SLE and 29 control blood samples were randomly selected for this study. GSE72509 included 99 blood samples from SLE patients (SLE group) and 18 control samples (control group). Next, 2 ADRGs, GLS1 and RHCG, were retrieved from relevant literature [12] . Meanwhile, based on the summarization of signaling pathways involved in literatures research, a search was conducted in molecular signatures database (MSigDB) (https://www.gsea-msigdb.org/gsea/msigdb) utilizing keywords "CD8+ T cell", "lysosomal pH regulation", "ammonia", "ammonium", and "autophagy". With C5 as the filtering criterion, the ggvenn package (v 0.1.10) [75] was used to calculate the intersection of all retrieved results. Finally, a total of 467 ADRGs were obtained ( Supplementary Table S5) . All data were downloaded on March 26th, 2025. 5.2 Discernment and related functional analyses of candidate genes A comparative transcriptome analysis was performed on the GSE112087 dataset (SLE vs. control) using DESeq2 (v 1.40.2) [76] to uncover AD-relevant genes in SLE. Differentially expressed genes (DEGs) were defined by applying a threshold of |log2FC| > 0.5 and p.adjust 0.5 and p.adjust < 0.05. Furthermore, a volcano plot for DEGs was generated utilizing the ggplot2 (v 3.5.1) [77] and ggpubr package (v 0.6.0) [78] , and labeled with top 10 up/down-regulated genes by log2FC. A heatmap of top 10 DEGs was plotted via ComplexHeatmap (v2.16.0) [79] . DEGs and ADRGs were intersected using ggvenn (v0.1.10) to identify candidate genes. ClusterProfiler (v4.15.0.003) was used for GO and KEGG analyses (p.adjust < 0.05) to elucidate candidate gene functions [80] . Top 10 entries from each GO category — biological process (BP), cellular component (CC), molecular function (MF) and KEGG pathway were selected for display. 5.3 Discernment of biomarkers via protein-protein interaction (PPI) network, machine learning, expression validation, and receiver operating characteristic (ROC) curve analysis To further screen AD-associated biomarkers in SLE, PPI was first analyzed via STRING (https://www.string-db.org, interaction score > 0.4). MNC and Degree algorithms in Cytoscape’s CytoHubba plugin (v3.8.2) [81] computed node connectivity to construct the PPI network. Hub genes were the intersection of top 20 candidates from both algorithms (via ggvenn v0.1.10) and filtered by three machine learning algorithms in GSE112087: randomForest (v4.7.1.2) [82] for RF (ntree=350, top 10 genes by MeanDecreaseGini), e1071 (v1.7.16) [83] for SVM-RFE, and glmnet (v4.1.8) [84] for LASSO (5-fold cross-validation, lambda at minimum classification error). Key genes were the intersection of RF, SVM-RFE, and LASSO characteristic genes (ggvenn v0.1.10). Expression patterns of key genes in GSE112087/GSE72509 were compared between SLE and control groups (Wilcoxon test, p 0.7 in both datasets as definitive biomarkers. 5.4 Development and evaluation of nomogram In GSE112087, a nomogram to evaluate the predictive probability of biomarkers for SLE occurrence was developed using rms (v 6.8.1) [86] . Individual scores were allocated to each biomarker, and the cumulative total was associated with elevated SLE risk. The nomogram's calibration was subsequently evaluated via a calibration curve generated by the regplot package (v 1.1). A slope of the calibration curve approaching 1 indicated higher accuracy of the nomogram predictions. The reliability of the calibration curve was also evaluated using the Hosmer-Lemeshow (HL) test (p > 0.05). Additionally, to evaluate the nomogram, decision curve analysis (DCA) was applied via the ggDCA package (v 1.1; https://CRAN.R-project.org/package=ggDCA) to determine its clinical utility. Concurrently, the pROC package (v 1.18.5) [87] was used to plot the ROC curve for assessing predictive accuracy. The AUC was subsequently computed to appraise the prognostic efficacy of the nomogram (AUC > 0.7). 5.5 Gene set enrichment analysis (GSEA) and GeneMANIA analyses of biomarkers Biomarker functional characterization was performed via GSEA and GeneMANIA. Using MSigDB’s "c2.cp.all.v2022.1.Hs.symbols.gmt" gene set, the psych package (v2.4.6.26) [88] computed genome-wide Spearman correlation coefficients for the biomarker panel in GSE112087 (SLE vs. control) via corr.test. Genes were ranked by descending correlation coefficients, and GSEA was conducted with clusterProfiler (v4.15.0.003) (p.adjust 1). Top 5 pathways (ranked by ascending p.adjust) were visualized using GseaVis (v0.1.0) [89] . Biomarkers were input into GeneMANIA (http://www.genemania.org) (species: Homo sapiens) to construct an interaction network with similar genes, aiding in biomarker function identification and characterization. 5.6 Immune microenvironment analysis Comprehensive profiling of 22 immune cell types [90] in the GSE112087 dataset was performed with CIBERSORT (v 0.1.0) [91] (p < 0.05) and visualized using ggplot2 (v 3.5.1) to characterize their role in SLE. Wilcoxon test-defined differentially infiltrated immune cells (p < 0.05) were identified and visualized (ggplot2 v 3.5.1). Interrelationships among these cells and with biomarkers were examined by Spearman correlation (psych v 2.4.6.26; p 0.3) and plotted with corrplot (v 0.95) [92] and ggplot2 (v 3.5.1). 5.7 Construction of regulatory networks and drug prediction An investigation into the regulatory microRNAs (miRNAs) targeting the biomarkers was conducted using the StarBase database (https://starbase.sysu.edu.cn/). The resulting miRNAs were filtered based on a pancancerNum threshold of >6 to identify key regulatory miRNAs. To study the regulation of biomarkers by transcription factors (TFs), TFs for biomarkers were postulated utilizing ChIPBase (http://rna.sysu.edu.cn/chipbase/). Key TFs were screened on the basis of the criterion that the aggregate of "Upstream sample count" and "Downstream sample count" exceeded 12. Data organization and screening were conducted utilizing tidyverse (v 2.0.0) [93] . Potential therapeutic drugs for SLE were explored by mining the Drug-Gene Interaction Database (DGIdb, https://dgidb.org/) for interactions with the biomarkers. All resultant networks, including mRNA-miRNA, mRNA-TF, and drug-biomarker interactions, were visualized using Cytoscape (v 3.8.2). 5.8 Statistical analysis Statistical analyses were carried out in R (v 4.3.3). Group comparisons were conducted with the Wilcoxon test, applying a significance threshold of p < 0.05. The following asterisk notation was used to indicate significance levels: **** p < 0.0001, *** p < 0.001, ** p < 0.01, * p 0.05. Abbreviations Abbreviation Full Term SLE Systemic Lupus Erythematosus AD Ammonia Death ADRGs Ammonia Death-Related Genes DEGs Differentially Expressed Genes PPI Protein-Protein Interaction ROC Receiver Operating Characteristic IFN-I Type I Interferons CAR-T Therapy Chimeric Antigen Receptor T-cell Therapy GEO Gene Expression Omnibus MSigDB Molecular Signatures Database GO Gene Ontology KEGG Kyoto Encyclopedia of Genes and Genomes BP Biological Process CC Cellular Component MF Molecular Function RF RandomForest SVM-RFE Support Vector Machine-Recursive Feature Elimination LASSO Least Absolute Shrinkage and Selection Operator AUC Area Under the Curve HL Hosmer-Lemeshow DCA Decision Curve Analysis GSEA Gene Set Enrichment Analysis NES Normalized Enrichment Score TFs Transcription Factors miRNAs microRNAs DGIdb Drug-Gene Interaction Database JAK2 Janus Kinase 2 CXCL10 Small Inducible Cytokine Subfamily B (Cys-X-Cys) Member 10 STAT Signal Transducer and Activator of Transcription TYK2 Tyrosine Kinase 2 AhR Aryl Hydrocarbon Receptor Tfh Follicular Helper T CXCR3 C-X-C Chemokine Receptor Type 3 hUC-MSCs Human Umbilical Cord Mesenchymal Stem Cells NLRs Nucleotide-binding and Oligomerization Domains-Like Receptors TLR Toll-Like Receptor RLR Retinoic Acid-Inducible Gene I Protein-Like Receptor cGAS-STING Cyclic GMP-AMP Synthase-Stimulator of Interferon Genes MAVS Mitochondrial Antiviral Signaling Protein NLRX1 NLR Family Member X1 NLRC5 NLR Family CARD Domain-Containing 5 NLRP11 NLR Family Pyrin Domain-Containing 11 PPARα Peroxisome Proliferator-Activated Receptor Alpha SREBPs Sterol Regulatory Element-Binding Proteins CMV Cytomegalovirus IFNAR Interferon Alpha/Beta Receptor ROS Reactive Oxygen Species PAD4 Peptidylarginine Deiminase 4 NET Neutrophil Extracellular Trap DAMP Damage-Associated Molecular Pattern dsDNA Double-Stranded DNA pDCs Plasmacytoid Dendritic Cells IFN-α Interferon-Alpha LDGs Low-Density Granulocytes SLEDAI Systemic Lupus Erythematosus Disease Activity Index TNF-α Tumor Necrosis Factor-Alpha mtROS Mitochondrial Reactive Oxygen Species Declarations Acknowledgements We would like to express our sincere gratitude to all individuals and organizations who supported and assisted us throughout this research.In conclusion, we extend our thanks to everyone who has supported and assisted us along the way. Without your support, this research would not have been possible. Author contributions Conceptualization, Data curation, Validation, Visualization, Writing–original draft, Writing–review & editing. M. F.: Data curation, Validation, Visualization, Writing–review & editing. M.F.andY.L.: Validation, Writing–review & editing. C.L.andD.Y.: Visualization, Writing–review & editing. J.Z.andY.L.: Conceptualization, Supervision, Writing–review & editing.J.Z.andP.C.: Conceptualization, Project administration, Supervision, Writing–review & editing. This work currently described has not been published, is not being considered for publication elsewhere, and its publication was approved by all authors. Declaration of competing interests The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Funding sources This research received no external funding. Ethics approval and consent to participate Not applicable. Data Availability Statement The datasets analysed during the current study are available in the Gene Expression Omnibus (GEO) repository at https://www.ncbi.nlm.nih.gov/geo/, reference numbers GSE112087 and GSE72509. References Siegel, C. H. & Sammaritano, L. R. Systemic Lupus Erythematosus: A Review. Jama 331 , 1480–1491 (2024). Hoi, A., Igel, T., Mok, C. C. & Arnaud, L. Systemic lupus erythematosus. Lancet (London England) . 403 , 2326–2338 (2024). Tian, J., Zhang, D., Yao, X., Huang, Y. & Lu, Q. Global epidemiology of systemic lupus erythematosus: a comprehensive systematic analysis and modelling study. Ann. Rheum. Dis. 82 , 351–356 (2023). Zucchi, D. et al. Systemic lupus erythematosus: one year in review 2023. Clin. Exp. Rheumatol. 41 , 997–1008 (2023). Lazar, S. & Kahlenberg, J. M. Systemic Lupus Erythematosus: New Diagnostic and Therapeutic Approaches. Annu. Rev. Med. 74 , 339–352 (2023). Schilirò, D. et al. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9439399","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":634696621,"identity":"18ccd65f-af85-4e6c-bad8-b303c707a86c","order_by":0,"name":"Manli Feng","email":"","orcid":"","institution":"Kunming Hospital of Traditional Chinese Medicine The Third Affiliated Hospital of Yunnan University of Traditional Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Manli","middleName":"","lastName":"Feng","suffix":""},{"id":634696622,"identity":"bbb06b38-8c0c-4ce9-ba06-f7c341c99cbd","order_by":1,"name":"Yu Lu","email":"","orcid":"","institution":"Kunming Hospital of Traditional Chinese Medicine The Third Affiliated Hospital of Yunnan University of Traditional Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Yu","middleName":"","lastName":"Lu","suffix":""},{"id":634696623,"identity":"88d45732-6967-4737-a3d3-92dc05b95ec9","order_by":2,"name":"Jin Zou","email":"","orcid":"","institution":"Jinniu District Maternal and Child Health Hospital","correspondingAuthor":false,"prefix":"","firstName":"Jin","middleName":"","lastName":"Zou","suffix":""},{"id":634696624,"identity":"33a643c3-9e92-43ac-9aed-d8870cc169b8","order_by":3,"name":"Pengfei Cun","email":"","orcid":"","institution":"Baoshan College of Traditional Chinese Medicine Baoshan City","correspondingAuthor":false,"prefix":"","firstName":"Pengfei","middleName":"","lastName":"Cun","suffix":""},{"id":634696625,"identity":"e555ca1e-ee0b-4640-913e-9e5833833f73","order_by":4,"name":"Yuan Liu","email":"","orcid":"","institution":"Kunming Hospital of Traditional Chinese Medicine The Third Affiliated Hospital of Yunnan University of Traditional Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Yuan","middleName":"","lastName":"Liu","suffix":""},{"id":634696626,"identity":"00a16018-cec6-4c0d-9787-48332715c284","order_by":5,"name":"Chenxi Liao","email":"","orcid":"","institution":"Kunming Hospital of Traditional Chinese Medicine The Third Affiliated Hospital of Yunnan University of Traditional Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Chenxi","middleName":"","lastName":"Liao","suffix":""},{"id":634696627,"identity":"9101ea70-1a63-435e-b1ba-01b0864be209","order_by":6,"name":"Jingwen Zhang","email":"","orcid":"","institution":"Kunming Hospital of Traditional Chinese Medicine The Third Affiliated Hospital of Yunnan University of Traditional Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Jingwen","middleName":"","lastName":"Zhang","suffix":""},{"id":634696628,"identity":"5716278a-5641-44e3-a5db-29c3ee73f81e","order_by":7,"name":"DengKe Yang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5ElEQVRIie3PsWqDUBTG8QOC0yGuRyLxFQ4EDAEhr+Llwp1ScHTIICTEIeZdOnZUhDuZPUMGg5C5XUoGCW3mFrVbh/ubzx++A2AY/5DtV2Xb8QNXWSqaKNkMJxOyJWNceIxFy02th5MZYUD4XoRM4uZed9aIYdO9ZOILLkCqRKQ2ONkh6k+8qmyYb7hMtTqLNw+oPr32J6AkR2whlPl3UtvA9DKUrAMqnknlfMZib41IaB24KVfIGhSMS1DLObBCNwdJUa1x8Bc/25YtdOHK8RvxcU82Myc79ic/4N/ODcMwjF99Ad6pSmlRn0zBAAAAAElFTkSuQmCC","orcid":"","institution":"Kunming Hospital of Traditional Chinese Medicine The Third Affiliated Hospital of Yunnan University of Traditional Chinese Medicine","correspondingAuthor":true,"prefix":"","firstName":"DengKe","middleName":"","lastName":"Yang","suffix":""}],"badges":[],"createdAt":"2026-04-16 14:23:53","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9439399/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9439399/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108944257,"identity":"78f3b433-14c1-4a49-9288-21532f37ac8e","added_by":"auto","created_at":"2026-05-11 05:58:04","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1399815,"visible":true,"origin":"","legend":"\u003cp\u003eIdentification and functional analysis of candidate genes associated with systemic lupus erythematosus (SLE) and ammonia death. (a) Volcano plot of differentially expressed genes (DEGs) in the GSE112067 dataset (|logFC| \u0026gt; 0.5). Red indicates upregulated genes, blue indicates downregulated genes, and gray indicates non-significant genes. (b) Heatmap of DEGs. Red represents high expression, and blue represents low expression. (c) Venn diagram of candidate genes. (d) Bubble plot of significantly enriched Gene Ontology (GO) terms for candidate genes. Redder color indicates higher significance, and larger bubble size represents a greater number of enriched genes. (e) Bubble plot of significantly enriched Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways for candidate genes. Redder color indicates higher significance, and larger bubble size represents a greater number of enriched genes.\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-9439399/v1/bf0d511ca1439f5ed174749a.png"},{"id":108944226,"identity":"e7156cc1-3956-4118-b2f3-962b2dd4b0e0","added_by":"auto","created_at":"2026-05-11 05:57:45","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1668759,"visible":true,"origin":"","legend":"\u003cp\u003eIdentification of key genes. (a) Core genes identified by the maximum neighborhood component (MNC) algorithm. (b) Core genes identified by the Degree algorithm. (c) Venn diagram of hub genes overlapping between the two algorithms. (d-e) Feature gene screening via the random forest (RF) algorithm. (f) Feature gene screening using the support vector machine-recursive feature elimination (SVM-RFE) algorithm. (g) Feature gene screening using least absolute shrinkage and selection operator (LASSO) logistic regression. The left panel shows the coefficient path, and the right panel shows the cross-validation curve. (h) Venn diagram of key genes overlapping across the three machine learning algorithms.\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-9439399/v1/95e5ed3ff8a44ee90b0fc8eb.png"},{"id":108944265,"identity":"43d202e9-889d-4014-ac98-3d65a21eafb9","added_by":"auto","created_at":"2026-05-11 05:58:05","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":411117,"visible":true,"origin":"","legend":"\u003cp\u003eValidation of biomarkers and construction of the diagnostic nomogram. (a-b) Validation of potential candidate biomarker expression levels based on the GSE112087 (a) and GSE72509 (b) datasets. Red represents the systemic lupus erythematosus (SLE) group, blue represents the control group (*p \u0026lt; 0.05, **p \u0026lt; 0.01, ***p \u0026lt; 0.0001). (c-d) Receiver operating characteristic (ROC) curves for candidate biomarkers in the GSE112087 (c) and GSE72509 (d) datasets. (e) The diagnostic nomogram. “Points” refers to the score assigned to each variable. The “Total Points” are calculated by summing the scores of the two biomarkers and then mapped to the linear predictor and the probability of the outcome. (f) Calibration plot of the nomogram. (g) Decision curve analysis (DCA) plot for the clinical application of the nomogram. (h) ROC curve of the nomogram.\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-9439399/v1/76f188612e84ad4077e2a957.png"},{"id":108944263,"identity":"5b71604d-db38-433f-8745-966db849da9e","added_by":"auto","created_at":"2026-05-11 05:58:04","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1906163,"visible":true,"origin":"","legend":"\u003cp\u003eFunctional enrichment and immune microenvironment analysis of biomarkers. (a-b) Gene set enrichment analysis (GSEA) of enriched pathways for biomarkers JAK2 (a) and CXCL10 (b). Lines of different colors represent distinct pathways. (c) Genes co-expression network. The inner circle represents the biomarkers, the outer circle represents correlated genes, and different colors indicate diverse functions or interaction types. (d) Stacked bar chart of immune cell proportions in systemic lupus erythematosus (SLE) patients versus control samples from the GSE112087 dataset. Different colors represent distinct immune cell types. (e) Box plots showing the fraction distribution of differential immune cells between the control group (teal) and the SLE group (red) (*p \u0026lt; 0.05, **p \u0026lt; 0.01, **\u003cem\u003ep \u0026lt; 0.0001). \u003c/em\u003e(f)\u003cem\u003e Heatmap of correlations among differential immune cells between the SLE and control groups. Red indicates a positive correlation, cyan indicates a negative correlation, and darker colors represent stronger correlations (\u003c/em\u003ep \u0026lt; 0.05, **p \u0026lt; 0.01, ***p \u0026lt; 0.001, ***\u003cem\u003ep \u0026lt; 0.0001). \u003c/em\u003e(g)\u003cem\u003e Heatmap of correlations between differential immune cells and biomarkers. Red indicates a positive correlation, blue indicates a negative correlation, and darker colors represent stronger correlations (\u003c/em\u003ep \u0026lt; 0.05, **p \u0026lt; 0.01, ***p \u0026lt; 0.001).\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-9439399/v1/f239c718c8e19767b67d4c33.png"},{"id":108944290,"identity":"3746395c-be1e-4bab-aca8-b6fe205e19ec","added_by":"auto","created_at":"2026-05-11 05:58:14","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1654998,"visible":true,"origin":"","legend":"\u003cp\u003eRegulatory network and drug prediction for biomarkers. (a) microRNA (miRNA)-biomarker regulatory network. Blue nodes represent biomarkers, pink nodes represent miRNAs. (b) Transcription factor (TF)-biomarker regulatory network. Pink nodes represent biomarkers, purple nodes represent TFs. (c) Drug-biomarker interaction network. Blue nodes represent biomarkers, yellow nodes represent drugs.\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-9439399/v1/2d47796e034131c871a1f4ab.png"},{"id":108979821,"identity":"d4b00492-3455-4b6b-bdb3-9674b5db73a7","added_by":"auto","created_at":"2026-05-11 12:01:41","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":7666185,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9439399/v1/16635f64-3f21-4c22-a7c5-0a6f8ca933f8.pdf"},{"id":108978064,"identity":"39934ce4-88bf-4845-94c4-6c7296cef4aa","added_by":"auto","created_at":"2026-05-11 11:33:55","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":15616,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Table S1. List of candidate genes.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"TableS1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-9439399/v1/40ee2f6c5e109da9f60c3141.xlsx"},{"id":108944264,"identity":"b30d0237-f3f9-438d-8145-dcc2b1abfeb2","added_by":"auto","created_at":"2026-05-11 05:58:05","extension":"csv","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":454,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Table S2. List of gene ontology (GO) terms for candidate genes.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"TableS2.csv","url":"https://assets-eu.researchsquare.com/files/rs-9439399/v1/63a3fc7279fee5d1adce6db4.csv"},{"id":108944236,"identity":"345ef92a-9f12-4432-9ff4-4379072b0046","added_by":"auto","created_at":"2026-05-11 05:57:49","extension":"txt","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":48329,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Table S3. List of kyoto encyclopedia of genes and genomes (KEGG) pathways for candidate genes.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"TableS3.txt","url":"https://assets-eu.researchsquare.com/files/rs-9439399/v1/e0f76c5d2958025eab1c256a.txt"},{"id":108944260,"identity":"8270c7fe-d5c2-44b2-bcd6-7f2302a096b0","added_by":"auto","created_at":"2026-05-11 05:58:04","extension":"txt","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":2618,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Table S4. List of gene set enrichment analysis (GSEA) pathways for 2 biomarkers.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"TableS4.txt","url":"https://assets-eu.researchsquare.com/files/rs-9439399/v1/5ea96948dd8dd5824b9e397a.txt"},{"id":108944262,"identity":"4df891b2-33f7-4bd7-801f-f8b5f8291f03","added_by":"auto","created_at":"2026-05-11 05:58:04","extension":"xlsx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":27270,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Table S5. List of ammonia death-related genes.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"TableS5.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-9439399/v1/7fb94822ad037d624c1cc1c1.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Identification of JAK2 and CXCL10 as ammonia death related biomarkers in systemic lupus erythematosus based on transcriptomic analysis","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eSystemic Lupus Erythematosus (SLE) is an autoimmune disease that affects multiple organs and systems \u003csup\u003e[1]\u003c/sup\u003e. It causes inflammation and damage as the immune system abnormally attacks the body\u0026apos;s own tissues and organs \u003csup\u003e[2]\u003c/sup\u003e. The global incidence of SLE is approximately 20-70 cases per 100,000 people, with variations across different regions and populations \u003csup\u003e[3]\u003c/sup\u003e. Notably, the incidence rate in women is significantly higher than that in men \u003csup\u003e[4]\u003c/sup\u003e. Currently, the main treatment approaches for SLE include glucocorticoids, immunosuppressants, and biological agents, which aim to control inflammation and inhibit abnormal immune responses. However, existing therapies have limitations: there are significant individual differences in treatment responses; long-term use leads to severe side effects (such as increased risk of infection and organ damage); some patients show poor response to medications. Moreover, these therapies can hardly halt disease progression or achieve a radical cure \u003csup\u003e[5-8]\u003c/sup\u003e. Chimeric Antigen Receptor T-cell Therapy (CAR-T Therapy), as a highly promising emerging treatment, has demonstrated potential for application, but its clinical translation still requires in-depth research and verification \u003csup\u003e[9,10]\u003c/sup\u003e. Recent studies have revealed that the molecular mechanisms of SLE are associated with multiple factors, involving two key mediator families: type I interferons (IFN-I) and autoantibodies targeting nucleic acids and nucleic acid-binding proteins \u003csup\u003e[11]\u003c/sup\u003e. Therefore, the identification of novel SLE biomarkers is crucial for elucidating disease pathogenesis and paving the way for more effective therapies.\u003c/p\u003e\n\u003cp\u003eUnder physiological conditions, ammonia is the nitrogen-containing end product of amino acid metabolism. It is mainly excreted from the body through urea synthesis in the liver, and simultaneously acts as a neurotransmitter to participate in signal transmission in the central nervous system. Studies indicate that excessive ammonia accumulation alkalinizes lysosomes, thereby disrupting their capacity for ammonia sequestration. The subsequent backflow of ammonia into mitochondria triggers cell death by inducing mitochondrial damage, a process characterized by lysosomal alkalinization, mitochondrial swelling, and impaired autophagic flux \u003csup\u003e[12,13]\u003c/sup\u003e. Excessively high ammonia concentration in the blood triggers hyperammonemia, which is closely associated with disrupted potassium homeostasis, mitochondrial dysfunction, oxidative stress, inflammation, hypoxemia, and dysregulated neurotransmission \u003csup\u003e[14-16]\u003c/sup\u003e. It has been reported that patients with SLE may complicate with hyperammonemia \u003csup\u003e[17]\u003c/sup\u003e. In SLE patients, chronic inflammation and autoimmune attack may cause hepatocellular injury and metabolic abnormalities, reducing the body\u0026rsquo;s ability to clear ammonia and thereby increasing the risk of hyperammonemia \u003csup\u003e[18]\u003c/sup\u003e. However, there is currently no research on the association between dysregulated ammonia concentration and SLE. To gain an in-depth understanding of this association, studies on AD-related genes (ADRGs) are particularly important. These genes hold promise as novel diagnostic or therapeutic targets for SLE, paving the way for developing more effective treatment strategies in the future.\u003c/p\u003e\n\u003cp\u003eIn this study, using SLE data and ADRGs retrieved from online databases and relevant literature, we identified biomarkers associated with AD in SLE through bioinformatics approaches including protein-protein interaction (PPI) networks, machine learning, and expression level validation. The study further explored the molecular mechanisms of these biomarkers in the disease, thereby providing new references for the precise diagnosis and personalized treatment regimens of SLE patients.\u003c/p\u003e"},{"header":"2. Results","content":"\u003cp\u003e\u003cstrong\u003e2.1 Discovery of 55 candidate genes and investigation into their biological roles\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAfter performing differential analysis on the GSE112087 dataset to distinguish DEGs between SLE and control groups, a total of 1,460 DEGs were obtained. Specifically, 383 genes showed down-regulation, and 1,077 genes exhibited up-regulation. As an illustration, IFI27 and USP18 exhibited significant up-regulation in the SLE group, whereas SLC4A10 and GRIN1 showed notable down-regulation. Results were presented using a volcano plot and a heatmap \u003cstrong\u003e(Figure 1a-b)\u003c/strong\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTaking the intersection of 1,460 DEGs and 467 ADRGs yielded 55 candidate genes, such as ZBP1, ADAR, and MYD88 \u003cstrong\u003e(Fig\u003c/strong\u003e\u003cstrong\u003eure 1c, Supplementary Table S1)\u003c/strong\u003e. Moreover, candidate genes significantly accumulated within 321 GO terms (p.adjust \u0026lt; 0.05), comprising 266 BPs, 22 CCs, and 33 MFs \u003cstrong\u003e(\u003c/strong\u003e\u003cstrong\u003eSupplementary Table S2)\u003c/strong\u003e. The top 10 of these BPs include \u0026quot;macroautophagy\u0026quot; and \u0026quot;response to virus\u0026quot;. The top 10 enriched CCs include \u0026quot;endocytic vesicle\u0026quot; and \u0026quot;autophagosome\u0026quot;. The top 10 of these MFs include \u0026quot;GTPase activity\u0026quot; and \u0026quot;quaternary ammonium group binding\u0026quot; \u003cstrong\u003e(Fig\u003c/strong\u003e\u003cstrong\u003eure 1d)\u003c/strong\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe 55 candidate genes were mapped to 14 KEGG pathways \u003cstrong\u003e(\u003c/strong\u003e\u003cstrong\u003eSupplementary Table S3)\u003c/strong\u003e, such as the \u0026quot;NOD-like receptor signaling pathway\u0026quot; and \u0026quot;Herpes simplex virus 1 infection\u0026quot; \u003cstrong\u003e(Figure 1e)\u003c/strong\u003e. Through GO and KEGG analyses, these candidate genes were shown to correlate with multiple critical signaling pathways, like autophagy and viral response.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2 Acquisition of 2 biomarkers: JAK2 and CXCL10\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe MNC algorithm identified genes such as CXCL10 and STAT1 as highly important \u003cstrong\u003e(Fig\u003c/strong\u003e\u003cstrong\u003eure 2a)\u003c/strong\u003e, whereas the Degree algorithm highlighted genes such as GBP5 and TAP1 as core genes \u003cstrong\u003e(Fig\u003c/strong\u003e\u003cstrong\u003eure 2b)\u003c/strong\u003e. By intersecting the results, 18 hub genes were obtained, including JAK2, STAT1, and CXCL11\u003cstrong\u003e\u0026nbsp;(Fig\u003c/strong\u003e\u003cstrong\u003eure 2c)\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003eFurthermore, the top 10 genes ranked by MeanDecreaseGini in the RF algorithm included JAK2, CXCR6, and IRGM \u003cstrong\u003e(Fig\u003c/strong\u003e\u003cstrong\u003eure 2d-e)\u003c/strong\u003e. Moreover, SVM-RFE screened 8 characteristic genes when the correct rate was maximum, such as CXCR6, GBP5, and JAK2\u003cstrong\u003e\u0026nbsp;(Fig\u003c/strong\u003e\u003cstrong\u003eure 2f)\u003c/strong\u003e. In the LASSO algorithm, when the log(lambda.min) was 0.0271, the model was optimal and 6 characteristic genes were obtained, such as JAK2, CXCR6, and LAP3\u003cstrong\u003e\u0026nbsp;(Fig\u003c/strong\u003e\u003cstrong\u003eure 2g)\u003c/strong\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFollowing the application of the 3 algorithms, their resulting gene sets were intersected, yielding 3 key genes: CXCR6, JAK2, and CXCL10 \u003cstrong\u003e(Fig\u003c/strong\u003e\u003cstrong\u003eure 2h)\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003eThe gene expression profiles of these key genes were next inspected in two atasets. The results revealed that all 3 key genes (CXCR6, JAK2, and CXCL10) exhibited conserved expression profiles in both datasets and showed marked intergroup differences (p \u0026lt; 0.05), which could serve as candidate biomarkers \u003cstrong\u003e(Fig\u003c/strong\u003e\u003cstrong\u003eure 3a-b)\u003c/strong\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAmong the 3 candidate biomarkers, CXCR6 demonstrated a significantly higher expression level in the control group. In contrast, JAK2 and CXCL10 exhibited notably higher levels of expression in the SLE group. The ROC curves demonstrated that only JAK2 (GSE112087: AUC = 0.84, GSE72509: AUC = 0.804) and CXCL10 (GSE112087: AUC = 0.74, GSE72509: AUC = 0.817) exhibited commendable predictive capabilities\u003cstrong\u003e\u0026nbsp;(Figure 3c-d)\u003c/strong\u003e. Thus, JAK2 and CXCL10 were identified as biomarkers for SLE and included in subsequent analyses.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.3 The nomogram performed favourably in diagnosing SLE\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo further assess the diagnostic utility of JAK2 and CXCL10, a predictive nomogram was developed. The nomogram assigned points to 2 biomarkers and calculated total points to predict an outcome. According to the nomogram, a total score of 159 points (from JAK2 and CXCL10) mapped to an SLE probability of 91.6%, revealing a direct association between higher composite scores and increased disease risk\u003cstrong\u003e\u0026nbsp;(Figure 3e)\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003eIn addition, the nomogram model demonstrated a low diagnostic error rate (HL test: p = 0.279), as substantiated by the calibration curve depicted in \u003cstrong\u003eFigure 3f\u003c/strong\u003e. The DCA curves confirmed that the nomogram had great clinical utility\u003cstrong\u003e\u0026nbsp;(Figure 3g)\u003c/strong\u003e. And ROC curve revealed that nomogram showed high diagnostic value (AUC = 0.863) \u003cstrong\u003e(Figure 3h)\u003c/strong\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo sum up, a well-constructed nomogram was developed, and calibration, DCA, and ROC curves demonstrated its favorable predictive performance.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.4 The biological functions of JAK2 and CXCL10 were further explored\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGSEA evidenced that JAK2 was enriched in 42 pathways, and CXCL10 was present in 24 pathways (p.adjust \u0026lt; 0.05, |NES| \u0026gt; 1) \u003cstrong\u003e(\u003c/strong\u003e\u003cstrong\u003eSupplementary Table S4)\u003c/strong\u003e. Notably, JAK2 was primarily enriched in pathways associated with innate immune signaling, such as Nod like, Toll Like, and Rig I Like receptor signaling pathways \u003cstrong\u003e(Figure 4a)\u003c/strong\u003e. In contrast, CXCL10 was primarily enriched in pathways associated with physiological and pathological processes such as autoimmune disorders (systemic lupus erythematosus) and energy metabolism (oxidative phosphorylation) \u003cstrong\u003e(Figure 4b)\u003c/strong\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe GeneMANIA prediction results showed that JAK2 and CXCL10 were primarily associated with 20 genes, such as CXCL9 and PPBP, and they interacted with each other mainly through physical interactions, shared protein domains, etc. Additionally, CXCL10 was potentially related to genes including CXCL9, CXCL11, and CCL13 in functions such as \u0026quot;cytokine activity\u0026quot; and \u0026quot;G protein-coupled receptor binding\u0026quot;, while JAK2 was potentially associated with genes like CXCL6, PIK3R1, and XCL1 in the function of \u0026quot;cytokine receptor binding\u0026quot; \u003cstrong\u003e(Figure 4c)\u003c/strong\u003e. The above results facilitated in-depth understanding of the biological functions of JAK2 and CXCL10.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.5 Significant relationships between immune infiltrating cells and biomarkers\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eImmune cell infiltration is closely associated with SLE, influencing disease severity and immune responses. The immune infiltration landscape of 22 cell types in GSE112087 (SLE vs. controls) is depicted in \u003cstrong\u003eFigure 4d\u003c/strong\u003e, with five cell types showing significant differential abundance (p \u0026lt; 0.05), such as resting NK cells (p \u0026lt; 0.0001) and Neutrophils (p \u0026lt; 0.01) \u003cstrong\u003e(Figure 4e)\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003eA robust positive link was identified between resting memory CD4 T cells and CD8 T cells (cor = 0.711, p = 2.61e-10); conversely, a marked inverse correlation was found between CD8 T cells and neutrophils (cor = -0.752, p = 1.78e-10)\u003cstrong\u003e\u0026nbsp;(Figure 4f)\u003c/strong\u003e. In addition, JAK2 demonstrated opposing correlations with neutrophils (positive; cor = 0.329, p = 0.0108) and resting NK cells (negative; cor = -0.447, p = 0.00039). CXCL10, however, was not significantly associated with any differentially infiltrated immune cells \u003cstrong\u003e(Figure 4g)\u003c/strong\u003e. Collectively, these results indicated that immune cell infiltration might play a significant regulatory role in SLE.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.6 miRNA hsa-miR-582-5p and drug peginterferon alfa-2b were identified to simultaneously target JAK2 and CXCL10\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMolecular regulatory networks offered additional insights into the regulatory factors affecting JAK2 and CXCL10. Specifically, 7 key miRNAs targeting JAK2 and 5 key miRNAs targeting CXCL10 were predicted, among which hsa-miR-582-5p was involved in the regulation of both JAK2 and CXCL10 \u003cstrong\u003e(Figure 5a)\u003c/strong\u003e. Meanwhile, as shown in \u003cstrong\u003eFigure 5b\u003c/strong\u003e, 5 key TFs targeting JAK2 and 2 key TFs targeting CXCL10 were predicted. Subsequently, potential drugs for SLE were explored. A total of 98 drugs targeting JAK2 and 16 drugs targeting CXCL10 were predicted, among which a common drug for both JAK2 and CXCL10, peginterferon alfa-2b, was identified \u003cstrong\u003e(Figure 5c)\u003c/strong\u003e. The above results facilitated the revelation of regulatory mechanisms of JAK2 and CXCL10 at the molecular level, and provided new scientific evidence for the treatment of SLE.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.7 Clinical trial validation of biomarkers\u003c/strong\u003e\u003c/p\u003e"},{"header":"3. Discussion","content":"\u003cp\u003eAccording to existing research reports, in patients with SLE, chronic inflammation and autoimmune attack may cause hepatocellular injury and metabolic abnormalities, which reduce the body\u0026rsquo;s ability to clear ammonia, thereby increasing the risk of hyperammonemia \u003csup\u003e[17]\u003c/sup\u003e. Hyperammonemia is implicated in various pathological dysfunctions, including mitochondrial impairment and inflammatory responses, and is strongly associated with a spectrum of liver diseases \u003csup\u003e[14-16,19]\u003c/sup\u003e. Additionally, dysregulated ammonia concentration can trigger AD, impairing lysosomal and mitochondrial functions\u003csup\u003e\u0026nbsp;\u003c/sup\u003e\u003csup\u003e[12]\u003c/sup\u003e. Consequently, elucidating the potential association between SLE and AD could broaden our understanding of SLE pathogenesis and provide a novel therapeutic rationale for its management.\u003c/p\u003e\n\u003cp\u003eIn this study, we identified 55 candidate genes associated with AD in SLE. Functional enrichment analysis revealed these genes are predominantly linked to immune and antiviral response pathways. Eventually, Janus kinase 2 (JAK2) and small inducible cytokine subfamily B (Cys-X-Cys), member 10 (CXCL10) were identified as SLE biomarkers. A systematic investigation of these biomarkers was conducted to elucidate their regulatory functions in SLE pathogenesis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eJAK2 (Janus Kinase 2)\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e, a non-receptor tyrosine kinase, primarily mediates cytokine (e.g., erythropoietin, growth hormone) signal transduction \u003csup\u003e[20]\u003c/sup\u003e. By phosphorylating/activating STAT proteins, it regulates cell proliferation, differentiation, apoptosis, and immune response, playing a key role in hematopoietic development and homeostasis \u003csup\u003e[21-23]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eAs a critical node in immune-related pathways, JAK2 is involved in diseases like myeloproliferative neoplasms, osteoarthritis, and leukemia \u003csup\u003e[24-30]\u003c/sup\u003e. It has been reported that germline mutations and polymorphisms of JAK family members are associated with specific diseases like SLE \u003csup\u003e[31,32]\u003c/sup\u003e. Tyrosine kinase 2 (TYK2), another member of the JAK family, triggers a unique set of immune events targeting JAK1, JAK2, and JAK3 by participating in downstream signaling pathways of type I interferons, and is linked to susceptibility to SLE \u003csup\u003e[33,34]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eJAK2 inhibitor Baricitinib, approved for rheumatoid arthritis and atopic dermatitis, is effective in SLE and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) \u003csup\u003e[35-39]\u003c/sup\u003e. Additionally, tapinarof, an agonist of the aryl hydrocarbon receptor (AhR), can inhibit the differentiation of follicular helper T (Tfh) cells by regulating the JAK2-STAT3 signaling pathway, thereby alleviating lupus-related autoimmunity \u003csup\u003e[40]\u003c/sup\u003e. Further research is needed on JAK2\u0026rsquo;s regulatory mechanisms in SLE progression, and this study provides a new theoretical basis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCXCL10 (Small Inducible Cytokine Subfamily B (Cys-X-Cys) Member 10)\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eprimarily exerts its physiological functions in immune cell recruitment and inflammation regulation \u003csup\u003e[41]\u003c/sup\u003e. These functions include chemotactic migration of immune cells to inflammatory sites, participation in antiviral and antitumor immune responses, involvement in angiogenesis and tissue repair, and maintenance of immune regulation and inflammatory balance \u003csup\u003e[42-47]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eIt has been reported that CXCL10 antagonists can limit inflammatory immune responses and promote neuronal regeneration and functional recovery, thereby participating in the regulation of spinal cord injury \u003csup\u003e[48]\u003c/sup\u003e. The IFN-\u0026gamma;-CXCL9/CXCL10-CXCR3 axis plays an important regulatory role in vitiligo, and CXCL10 is also one of the biomarkers for various diseases such as vitiligo, kidney transplantation, leprosy, and inflammatory rheumatic diseases \u003csup\u003e[49-53]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eFurthermore, elevated levels of CXCL10 are associated with cytokine storms, which in turn correlate with the severe course and disease progression of SARS-CoV-2 infection \u003csup\u003e[54-56]\u003c/sup\u003e; meanwhile, CXCL10 can also serve as a detection biomarker for patients with pulmonary tuberculosis \u003csup\u003e[57,58]\u003c/sup\u003e.\u003csup\u003e.\u003c/sup\u003e Studies have shown that patients with early-stage SLE produce significantly higher levels of CXCL10, which may decrease after one year \u003csup\u003e[59,60]\u003c/sup\u003e. The chemokine family induces autoimmune responses and amplifies the induced inflammatory responses in SLE and related complications (especially lupus nephritis), thus playing a vital role in the pathogenesis \u003csup\u003e[61,62]\u003c/sup\u003e. For example, in lupus nephritis, CXCL10 can recruit human umbilical cord mesenchymal stem cells (hUC-MSCs) to migrate to the kidneys; blocking the CXCL10-CXCR3 axis impairs the migration of hUC-MSCs to the kidneys and exerts a negative impact on treatment \u003csup\u003e[63]\u003c/sup\u003e. The more specific regulatory mechanisms of CXCL10 in SLE progression still need in-depth exploration, and this study offers a new perspective for this field.\u003c/p\u003e\n\u003cp\u003eEnrichment analysis showed JAK2 is enriched in antiviral innate immune pathways, including Nucleotide-binding and Oligomerization Domains (Nod), Toll-like receptors (TLR), and Retinoic Acid-Inducible Gene I Protein (RigI). NLRs maintain immune homeostasis by regulating TLR, RLR, and cyclic GMP-AMP synthase-stimulator of interferon genes (cGAS-STING) pathways: upon recognizing pathogen-associated molecules, these pathways induce proinflammatory cytokines/IFN-I. Most RIG-I-like receptors (RLRs) negatively regulate them (e.g., NLRX1 degrades MAVS) to prevent excessive activation, while a subset positively activates them for enhanced antiviral capacity \u003csup\u003e[64]\u003c/sup\u003e. The core pathology of SLE is excessive activation of innate immunity and hyperactivity of IFN-I.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn the TLR pathway, SLE immune complexes activate TLR7/9; impaired NLRP11/NLRX1 function causes excessive pathway activation, increasing IFN-I and inflammatory cytokines. In the RLR pathway, SLE self-RNA activates the RIG-I/MDA5-MAVS axis; NLRC5/NLRX1 abnormalities induce sustained activation and IFN storms. In the cGAS-STING pathway, SLE abnormal cytoplasmic DNA triggers activation; NLRC3/NLRX1 loss-of-function leads to uncontrolled activation, exacerbating immune disorders and disease progression.\u003c/p\u003e\n\u003cp\u003eThe hepatocyte glucose/lipid metabolism-TLR3/RLR antiviral immunity cross-talk supports hepatocyte innate immunity and cellular homeostasis via metabolic reprogramming (e.g., glycolytic lactate production, PPAR\u0026alpha;/SREBPs regulation in lipid metabolism). It regulates disease via \u0026quot;metabolism-immunity balance\u0026quot;: physiological metabolites inhibit excessive immune activation, while abnormal metabolism or viral hijacking causes immune evasion and liver injury \u003csup\u003e[65]\u003c/sup\u003e. This pathway may exacerbate SLE immune dysregulation/organ damage via glucose/lipid metabolic imbalance, indirectly affecting disease progression. CMV evades host innate immunity through PRRs, IFNAR-JAK-STAT, and intrinsic cellular defense (apoptosis/autophagy/endoplasmic reticulum stress) pathways, which maintain antiviral immunity by regulating IFN-I production, proinflammatory cytokine release, and cellular homeostasis. Normally, these pathways activate to resist infection; abnormally, CMV targets key molecules (e.g., US7 degrades TLR3, pM27 degrades STAT2) for immune evasion, leading to persistent infection \u003csup\u003e[66]\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eExtensive evidence has established that NLRs are critically involved in the pathogenesis of various autoimmune diseases, including inflammatory bowel disease, rheumatoid arthritis, SLE, psoriasis, and multiple sclerosis \u003csup\u003e[67-69]\u003c/sup\u003e. Furthermore, in SLE, the breakdown of B-cell tolerance to self-antigens is intrinsically regulated by TLRs \u003csup\u003e[70-72]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eOur immune infiltration analysis revealed significant differences in immune cell infiltration (neutrophils, resting NK cells) between SLE patients and controls, which correlated with biomarker JAK2\u0026mdash;suggesting these infiltration changes may play a key role in SLE progression. As the predominant innate immune leukocytes, neutrophils exert cytotoxic functions (pathogen phagocytosis, ROS/granular protease release, NET formation) and modulate innate/adaptive immunity via cytokine/chemokine secretion \u003csup\u003e[73]\u003c/sup\u003e. In SLE, neutrophils drive disease via multidimensional abnormal activation: SLE neutrophils show increased ROS production, which directly causes oxidative tissue damage (e.g., vascular endothelium, kidneys; oxidized DNA formsdamage-associated molecular pattern (DAMP) 8-hydroxyguanosine) and activates PAD4 to promote NET formation \u003csup\u003e[73]\u003c/sup\u003e. NET-exposed self-antigens (double-stranded DNA (dsDNA), citrullinated histones) activate plasmacytoid dendritic cells (pDCs) to produce interferon-alpha (IFN-\u0026alpha;) and induce B cells to generate autoantibodies (anti-dsDNA, anti-histone), which form immune complexes depositing in organs like glomeruli, exacerbating injuries such as lupus nephritis \u003csup\u003e[74]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eSecond, low-density granulocytes (LDGs)\u0026mdash;a notably enriched proinflammatory subset in SLE patients\u0026rsquo; peripheral blood\u0026mdash;positively correlate with disease activity (assessed by SLEDAI scores). Compared with normal-density neutrophils, LDGs are more prone to spontaneous neutrophil extracellular trap (NET) formation and secrete cytokines like interferon-alpha (IFN-\u0026alpha;) and tumor necrosis factor-alpha (TNF-\u0026alpha;). They inhibit endothelial progenitor cell differentiation, disrupt vascular endothelial barrier function, amplify inflammation via enhanced mitochondrial reactive oxygen species (mtROS) release, and induce abnormal biomechanical properties by altering cytoskeletal protein expression. LDGs are more likely trapped in microvessels, forming microthrombi that contribute to SLE vascular lesions. Thus, subsequent studies can deeply explore specific regulatory mechanisms based on the correlation between JAK2 and neutrophils.\u003c/p\u003e"},{"header":"4. Conclusion","content":"\u003cp\u003eIn this study, two SLE biomarkers (JAK2 and CXCL10) associated with AD were identified through bioinformatics combined with experimental validation, and a nomogram was constructed. Subsequently, to elucidate the underlying mechanisms, a multi-method approach integrating enrichment and immune infiltration analyses was applied, thereby establishing a theoretical foundation for improving SLE diagnosis and treatment. However, this study still has certain limitations: the sample sizes of the database and collected clinical samples are limited, detailed sample information is lacking, and there is significant room for improvement in the accuracy of disease assessment and prediction. In addition, the identified potential interacting genes and the biological pathways involved in regulation need further validation to provide reliable evidence for clinically targeted treatment. In future studies, we will expand the sample scope and conduct in vivo and in vitro experimental validation by constructing animal models, aiming to gain deeper insights.\u003c/p\u003e "},{"header":"5. Methods","content":"\u003cp\u003e\u003cstrong\u003e5.1 Data acquisition\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTwo datasets, GSE112087 and GSE72509 (both on the GPL16791 platform), were sourced from the Gene Expression Omnibus (GEO) database (https://www.ncbi.nlm.nih.gov/geo/). From GSE112087, a total of 31 SLE and 29 control blood samples were randomly selected for this study. GSE72509 included 99 blood samples from SLE patients (SLE group) and 18 control samples (control group). Next, 2 ADRGs, GLS1 and RHCG, were retrieved from relevant literature \u003csup\u003e[12]\u003c/sup\u003e. Meanwhile, based on the summarization of signaling pathways involved in literatures research, a search was conducted in molecular signatures database (MSigDB) (https://www.gsea-msigdb.org/gsea/msigdb) utilizing keywords \u0026quot;CD8+ T cell\u0026quot;, \u0026quot;lysosomal pH regulation\u0026quot;, \u0026quot;ammonia\u0026quot;, \u0026quot;ammonium\u0026quot;, and \u0026quot;autophagy\u0026quot;. With C5 as the filtering criterion, the ggvenn package (v 0.1.10) \u003csup\u003e[75]\u003c/sup\u003e was used to calculate the intersection of all retrieved results. Finally, a total of 467 ADRGs were obtained\u003cstrong\u003e\u0026nbsp;(\u003c/strong\u003e\u003cstrong\u003eSupplementary Table S5)\u003c/strong\u003e. All data were downloaded on March 26th, 2025.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e5.2 Discernment and related functional analyses of candidate genes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA comparative transcriptome analysis was performed on the GSE112087 dataset (SLE vs. control) using DESeq2 (v 1.40.2) \u003csup\u003e[76]\u003c/sup\u003e to uncover AD-relevant genes in SLE. Differentially expressed genes (DEGs) were defined by applying a threshold of |log2FC| \u0026gt; 0.5 and p.adjust \u0026lt; 0.05, with the threshold of |log2fold change (FC)| \u0026gt; 0.5 and p.adjust \u0026lt; 0.05. Furthermore, a volcano plot for DEGs was generated utilizing the ggplot2 (v 3.5.1) \u003csup\u003e[77]\u003c/sup\u003e and ggpubr package (v 0.6.0) \u003csup\u003e[78]\u003c/sup\u003e, and labeled with top 10 up/down-regulated genes by log2FC. A heatmap of top 10 DEGs was plotted via ComplexHeatmap (v2.16.0) \u003csup\u003e[79]\u003c/sup\u003e. DEGs and ADRGs were intersected using ggvenn (v0.1.10) to identify candidate genes. ClusterProfiler (v4.15.0.003) was used for GO and KEGG analyses (p.adjust \u0026lt; 0.05) to elucidate candidate gene functions\u003csup\u003e[80]\u003c/sup\u003e. Top 10 entries from each GO category \u0026mdash; biological process (BP), cellular component (CC), molecular function (MF) and KEGG pathway were selected for display.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e5.3 Discernment of biomarkers via protein-protein interaction (PPI) network, machine learning, expression validation, and receiver operating characteristic (ROC) curve analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo further screen AD-associated biomarkers in SLE, PPI was first analyzed via STRING (https://www.string-db.org, interaction score \u0026gt; 0.4). MNC and Degree algorithms in Cytoscape\u0026rsquo;s CytoHubba plugin (v3.8.2) \u003csup\u003e[81]\u003c/sup\u003e computed node connectivity to construct the PPI network. Hub genes were the intersection of top 20 candidates from both algorithms (via ggvenn v0.1.10) and filtered by three machine learning algorithms in GSE112087: randomForest (v4.7.1.2) \u003csup\u003e[82]\u003c/sup\u003e for RF (ntree=350, top 10 genes by MeanDecreaseGini), e1071 (v1.7.16) \u003csup\u003e[83]\u003c/sup\u003e for SVM-RFE, and glmnet (v4.1.8) \u003csup\u003e[84]\u003c/sup\u003e for LASSO (5-fold cross-validation, lambda at minimum classification error). Key genes were the intersection of RF, SVM-RFE, and LASSO characteristic genes (ggvenn v0.1.10). Expression patterns of key genes in GSE112087/GSE72509 were compared between SLE and control groups (Wilcoxon test, p \u0026lt; 0.05), with candidate biomarkers identified by consistent significant dysregulation. Validation via pROC (v1.18.5) \u003csup\u003e[85]\u003c/sup\u003e retained genes with AUC \u0026gt; 0.7 in both datasets as definitive biomarkers.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e5.4 Development and evaluation of nomogram\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn GSE112087, a nomogram to evaluate the predictive probability of biomarkers for SLE occurrence was developed using rms (v 6.8.1) \u003csup\u003e[86]\u003c/sup\u003e. Individual scores were allocated to each biomarker, and the cumulative total was associated with elevated SLE risk. The nomogram\u0026apos;s calibration was subsequently evaluated via a calibration curve generated by the regplot package (v 1.1). A slope of the calibration curve approaching 1 indicated higher accuracy of the nomogram predictions. The reliability of the calibration curve was also evaluated using the Hosmer-Lemeshow (HL) test (p \u0026gt; 0.05). Additionally, to evaluate the nomogram, decision curve analysis (DCA) was applied via the ggDCA package (v 1.1; https://CRAN.R-project.org/package=ggDCA) to determine its clinical utility. Concurrently, the pROC package (v 1.18.5) \u003csup\u003e[87]\u003c/sup\u003e was used to plot the ROC curve for assessing predictive accuracy. The AUC was subsequently computed to appraise the prognostic efficacy of the nomogram (AUC \u0026gt; 0.7).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e5.5 Gene set enrichment analysis (GSEA) and GeneMANIA analyses of biomarkers\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBiomarker functional characterization was performed via GSEA and GeneMANIA. Using MSigDB\u0026rsquo;s \u0026quot;c2.cp.all.v2022.1.Hs.symbols.gmt\u0026quot; gene set, the psych package (v2.4.6.26) \u003csup\u003e[88]\u003c/sup\u003e\u003csup\u003e\u0026nbsp;\u003c/sup\u003ecomputed genome-wide Spearman correlation coefficients for the biomarker panel in GSE112087 (SLE vs. control) via corr.test. Genes were ranked by descending correlation coefficients, and GSEA was conducted with clusterProfiler (v4.15.0.003) (p.adjust \u0026lt; 0.05, |normalized enrichment score (NES)| \u0026gt; 1). Top 5 pathways (ranked by ascending p.adjust) were visualized using GseaVis (v0.1.0) \u003csup\u003e[89]\u003c/sup\u003e. Biomarkers were input into GeneMANIA (http://www.genemania.org) (species: Homo sapiens) to construct an interaction network with similar genes, aiding in biomarker function identification and characterization.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e5.6 Immune microenvironment analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eComprehensive profiling of 22 immune cell types \u003csup\u003e[90]\u003c/sup\u003e\u003csup\u003e\u0026nbsp;\u003c/sup\u003ein the GSE112087 dataset was performed with CIBERSORT (v 0.1.0) \u003csup\u003e[91]\u003c/sup\u003e\u003csup\u003e\u0026nbsp;\u003c/sup\u003e(p \u0026lt; 0.05) and visualized using ggplot2 (v 3.5.1) to characterize their role in SLE. Wilcoxon test-defined differentially infiltrated immune cells (p \u0026lt; 0.05) were identified and visualized (ggplot2 v 3.5.1). Interrelationships among these cells and with biomarkers were examined by Spearman correlation (psych v 2.4.6.26; p \u0026lt; 0.05, |cor| \u0026gt; 0.3) and plotted with corrplot (v 0.95)\u0026nbsp;\u003csup\u003e[92]\u003c/sup\u003e\u003csup\u003e\u0026nbsp;\u003c/sup\u003eand ggplot2 (v 3.5.1).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e5.7 Construction of regulatory networks and drug prediction\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAn investigation into the regulatory microRNAs (miRNAs) targeting the biomarkers was conducted using the StarBase database (https://starbase.sysu.edu.cn/). The resulting miRNAs were filtered based on a pancancerNum threshold of \u0026gt;6 to identify key regulatory miRNAs. To study the regulation of biomarkers by transcription factors (TFs), TFs for biomarkers were postulated utilizing ChIPBase (http://rna.sysu.edu.cn/chipbase/). Key TFs were screened on the basis of the criterion that the aggregate of \u0026quot;Upstream sample count\u0026quot; and \u0026quot;Downstream sample count\u0026quot; exceeded 12. Data organization and screening were conducted utilizing tidyverse (v 2.0.0) \u003csup\u003e[93]\u003c/sup\u003e\u003csup\u003e.\u003c/sup\u003e Potential therapeutic drugs for SLE were explored by mining the Drug-Gene Interaction Database (DGIdb, https://dgidb.org/) for interactions with the biomarkers. All resultant networks, including mRNA-miRNA, mRNA-TF, and drug-biomarker interactions, were visualized using Cytoscape (v 3.8.2).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e5.8 Statistical analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eStatistical analyses were carried out in R (v 4.3.3). Group comparisons were conducted with the Wilcoxon test, applying a significance threshold of p \u0026lt; 0.05. The following asterisk notation was used to indicate significance levels: **** p \u0026lt; 0.0001, *** p \u0026lt; 0.001, ** p \u0026lt; 0.01, * p \u0026lt; 0.05, and ns (not significant) for p \u0026gt; 0.05.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"555\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAbbreviation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 421px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFull Term\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003eSLE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 421px;\"\u003e\n \u003cp\u003eSystemic Lupus Erythematosus\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003eAD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 421px;\"\u003e\n \u003cp\u003eAmmonia Death\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003eADRGs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 421px;\"\u003e\n \u003cp\u003eAmmonia Death-Related Genes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003eDEGs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 421px;\"\u003e\n \u003cp\u003eDifferentially Expressed Genes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003ePPI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 421px;\"\u003e\n \u003cp\u003eProtein-Protein Interaction\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003eROC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 421px;\"\u003e\n \u003cp\u003eReceiver Operating Characteristic\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003eIFN-I\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 421px;\"\u003e\n \u003cp\u003eType I Interferons\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003eCAR-T Therapy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 421px;\"\u003e\n \u003cp\u003eChimeric Antigen Receptor T-cell Therapy\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003eGEO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 421px;\"\u003e\n \u003cp\u003eGene Expression Omnibus\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003eMSigDB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 421px;\"\u003e\n \u003cp\u003eMolecular Signatures Database\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003eGO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 421px;\"\u003e\n \u003cp\u003eGene Ontology\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003eKEGG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 421px;\"\u003e\n \u003cp\u003eKyoto Encyclopedia of Genes and Genomes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003eBP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 421px;\"\u003e\n \u003cp\u003eBiological Process\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003eCC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 421px;\"\u003e\n \u003cp\u003eCellular Component\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003eMF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 421px;\"\u003e\n \u003cp\u003eMolecular Function\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003eRF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 421px;\"\u003e\n \u003cp\u003eRandomForest\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003eSVM-RFE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 421px;\"\u003e\n \u003cp\u003eSupport Vector Machine-Recursive Feature Elimination\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003eLASSO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 421px;\"\u003e\n \u003cp\u003eLeast Absolute Shrinkage and Selection Operator\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003eAUC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 421px;\"\u003e\n \u003cp\u003eArea Under the Curve\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003eHL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 421px;\"\u003e\n \u003cp\u003eHosmer-Lemeshow\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003eDCA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 421px;\"\u003e\n \u003cp\u003eDecision Curve Analysis\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003eGSEA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 421px;\"\u003e\n \u003cp\u003eGene Set Enrichment Analysis\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003eNES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 421px;\"\u003e\n \u003cp\u003eNormalized Enrichment Score\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003eTFs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 421px;\"\u003e\n \u003cp\u003eTranscription Factors\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003emiRNAs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 421px;\"\u003e\n \u003cp\u003emicroRNAs\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003eDGIdb\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 421px;\"\u003e\n \u003cp\u003eDrug-Gene Interaction Database\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003eJAK2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 421px;\"\u003e\n \u003cp\u003eJanus Kinase 2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003eCXCL10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 421px;\"\u003e\n \u003cp\u003eSmall Inducible Cytokine Subfamily B (Cys-X-Cys) Member 10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003eSTAT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 421px;\"\u003e\n \u003cp\u003eSignal Transducer and Activator of Transcription\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003eTYK2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 421px;\"\u003e\n \u003cp\u003eTyrosine Kinase 2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003eAhR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 421px;\"\u003e\n \u003cp\u003eAryl Hydrocarbon Receptor\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003eTfh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 421px;\"\u003e\n \u003cp\u003eFollicular Helper T\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003eCXCR3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 421px;\"\u003e\n \u003cp\u003eC-X-C Chemokine Receptor Type 3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003ehUC-MSCs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 421px;\"\u003e\n \u003cp\u003eHuman Umbilical Cord Mesenchymal Stem Cells\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003eNLRs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 421px;\"\u003e\n \u003cp\u003eNucleotide-binding and Oligomerization Domains-Like Receptors\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003eTLR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 421px;\"\u003e\n \u003cp\u003eToll-Like Receptor\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003eRLR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 421px;\"\u003e\n \u003cp\u003eRetinoic Acid-Inducible Gene I Protein-Like Receptor\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003ecGAS-STING\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 421px;\"\u003e\n \u003cp\u003eCyclic GMP-AMP Synthase-Stimulator of Interferon Genes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003eMAVS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 421px;\"\u003e\n \u003cp\u003eMitochondrial Antiviral Signaling Protein\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003eNLRX1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 421px;\"\u003e\n \u003cp\u003eNLR Family Member X1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003eNLRC5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 421px;\"\u003e\n \u003cp\u003eNLR Family CARD Domain-Containing 5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003eNLRP11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 421px;\"\u003e\n \u003cp\u003eNLR Family Pyrin Domain-Containing 11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003ePPAR\u0026alpha;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 421px;\"\u003e\n \u003cp\u003ePeroxisome Proliferator-Activated Receptor Alpha\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003eSREBPs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 421px;\"\u003e\n \u003cp\u003eSterol Regulatory Element-Binding Proteins\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003eCMV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 421px;\"\u003e\n \u003cp\u003eCytomegalovirus\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003eIFNAR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 421px;\"\u003e\n \u003cp\u003eInterferon Alpha/Beta Receptor\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003eROS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 421px;\"\u003e\n \u003cp\u003eReactive Oxygen Species\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003ePAD4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 421px;\"\u003e\n \u003cp\u003ePeptidylarginine Deiminase 4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003eNET\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 421px;\"\u003e\n \u003cp\u003eNeutrophil Extracellular Trap\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003eDAMP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 421px;\"\u003e\n \u003cp\u003eDamage-Associated Molecular Pattern\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003edsDNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 421px;\"\u003e\n \u003cp\u003eDouble-Stranded DNA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003epDCs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 421px;\"\u003e\n \u003cp\u003ePlasmacytoid Dendritic Cells\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003eIFN-\u0026alpha;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 421px;\"\u003e\n \u003cp\u003eInterferon-Alpha\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003eLDGs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 421px;\"\u003e\n \u003cp\u003eLow-Density Granulocytes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003eSLEDAI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 421px;\"\u003e\n \u003cp\u003eSystemic Lupus Erythematosus Disease Activity Index\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003eTNF-\u0026alpha;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 421px;\"\u003e\n \u003cp\u003eTumor Necrosis Factor-Alpha\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 135px;\"\u003e\n \u003cp\u003emtROS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 421px;\"\u003e\n \u003cp\u003eMitochondrial Reactive Oxygen Species\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to express our sincere gratitude to all individuals and organizations who supported and assisted us throughout this research.In conclusion, we extend our thanks to everyone who has supported and assisted us along the way. Without your support, this research would not have been possible.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization, Data curation, Validation, Visualization, Writing\u0026ndash;original draft, Writing\u0026ndash;review \u0026amp; editing. M. F.: Data curation, Validation, Visualization, Writing\u0026ndash;review \u0026amp; editing. M.F.andY.L.: Validation, Writing\u0026ndash;review \u0026amp; editing. C.L.andD.Y.: Visualization, Writing\u0026ndash;review \u0026amp; editing. J.Z.andY.L.: Conceptualization, Supervision, Writing\u0026ndash;review \u0026amp; editing.J.Z.andP.C.: Conceptualization, Project administration, Supervision, Writing\u0026ndash;review \u0026amp; editing. This work currently described has not been published, is not being considered for publication elsewhere, and its publication was approved by all authors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of competing interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding sources\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research received no external funding.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets analysed during the current study are available in the Gene Expression Omnibus (GEO) repository at https://www.ncbi.nlm.nih.gov/geo/, reference numbers \u0026nbsp;GSE112087 and GSE72509.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSiegel, C. 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Candidate genes were acquired by intersecting ADRGs with differentially expressed genes (DEGs) between SLE and control groups. Biomarkers were screened via protein-protein interaction (PPI) network analysis, machine learning and receiver operating characteristic (ROC) curve analysis. A nomogram was constructed, followed by enrichment analysis, immune microenvironment evaluation, regulatory factor and drug prediction.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eFifty-five candidate genes were identified, and Janus kinase 2 (JAK2) and C-X-C motif chemokine ligand 10 (CXCL10) were selected as key biomarkers with reliable diagnostic value. The nomogram showed favorable predictive performance. Gene set enrichment analysis (GSEA) revealed JAK2 participated in innate immune pathways, while CXCL10 was linked to immune and metabolic processes. JAK2 correlated significantly with neutrophils and resting natural killer (NK) cells. Hsa-miR-582-5p and peginterferon alfa-2b were identified as shared regulatory factors.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThe study identified JAK2 and CXCL10 as biomarkers for SLE, offering potential therapeutic targets for patients with the condition.\u003c/p\u003e","manuscriptTitle":"Identification of JAK2 and CXCL10 as ammonia death related biomarkers in systemic lupus erythematosus based on transcriptomic analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-11 05:54:46","doi":"10.21203/rs.3.rs-9439399/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewersInvited","content":"","date":"2026-04-28T12:37:13+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-28T12:22:45+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-04-27T11:18:24+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-25T14:02:31+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2026-04-25T13:55:59+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"0687c4f9-5192-4427-8047-9c4347dfdf20","owner":[],"postedDate":"May 11th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":67544214,"name":"Health sciences/Biomarkers"},{"id":67544215,"name":"Biological sciences/Computational biology and bioinformatics"},{"id":67544216,"name":"Health sciences/Diseases"},{"id":67544217,"name":"Biological sciences/Immunology"},{"id":67544218,"name":"Health sciences/Rheumatology"}],"tags":[],"updatedAt":"2026-05-11T05:54:47+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-11 05:54:46","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9439399","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9439399","identity":"rs-9439399","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}