Bioinformatics analysis identifies biomarkers associated with T-cell depletion in systemic lupus erythematosus.

Bioinformatics analysis identifies biomarkers associated with T-cell depletion in systemic lupus erythematosus - PMC Skip to main content An official website of the United States government Here's how you know Here's how you know Search Log in Primary site navigation Search Log in Search PMC Full-Text Archive Search in PMC Journal List User Guide As a library, NLM provides access to scientific literature. Inclusion in an NLM database does not imply endorsement of, or agreement with, the contents by NLM or the National Institutes of Health. Learn more: PMC Disclaimer | PMC Copyright Notice Medicine (Baltimore) . 2026 May 29;105(22):e49016. doi: 10.1097/MD.0000000000049016 Bioinformatics analysis identifies biomarkers associated with T-cell depletion in systemic lupus erythematosus Lanqing Yang a , Wei Gao a, * , Lulu Yang b , Xingyu Liu c Author information Article notes Copyright and License information a Department of Rheumatology and Immunology, The First Affiliated Hospital of Jinzhou Medical University, Jinzhou, Liaoning Province, China b Department of Pediatrics, Shiqian County Maternal and Child Health Hospital, Tongren, Guizhou Province, China c Department of Pediatric Orthopedics, Shanghai Children’s Medical Center Guizhou Hospital, Shanghai Jiao Tong University School of Medicine, Guiyang, Guizhou Province, China. * Correspondence: Wei Gao, Department of Rheumatology and Immunology, The First Affiliated Hospital of Jinzhou Medical University, No. 2, Section 5, Renmin Street, Guta District, Jinzhou 121001, Liaoning Province, China (e-mail: gaowei6678@126.com ). Received 2025 Mar 10; Revised 2025 Oct 22; Accepted 2026 May 7; Collection date 2026 May 29. Copyright © 2026 the Author(s). Published by Wolters Kluwer Health, Inc. This is an open-access article distributed under the terms of the Creative Commons Attribution-Non Commercial License 4.0 (CCBY-NC) , where it is permissible to download, share, remix, transform, and buildup the work provided it is properly cited. The work cannot be used commercially without permission from the journal. PMC Copyright notice PMCID: PMC13225601  PMID: 42216358 Abstract Systemic lupus erythematosus (SLE) is a chronic autoimmune disease that affects multiple organ systems. In SLE, T-cell subsets are closely associated with autoimmunity and organ inflammation. Detecting T-cell exhaustion (TEX)-related biomarkers is crucial for early SLE diagnosis and offers new insights into disease mechanisms. Transcriptomic data for SLE were collected from public databases, and TEX-related genes were obtained from the literature. Biomarkers were identified using analyses such as differential expression analysis and machine learning algorithms. The potential mechanisms of action of these biomarkers were further investigated, including nomogram construction, gene set enrichment analysis, immune microenvironment analysis, molecular regulatory network analysis, molecular docking, and reverse transcription-quantitative polymerase chain reaction validation. IFIT3 , IFIH1 , CXCL10 , and STAT2 were identified as biomarkers. The nomogram demonstrated good predictive performance. These biomarkers were mainly involved in biological processes such as ribosome biogenesis and negative regulation of viral genome replication. Four significantly different immune cell types were identified, including monocytes and resting memory CD4 T cells. The biomarkers were found to be regulated by several transcription factors and microRNAs. Furthermore, the 4 biomarkers exhibited high binding affinity for several drugs; in particular, the binding energy of STAT2 with bisphenol A was −7.0 kcal/mol. Finally, reverse transcription-quantitative polymerase chain reaction results showed that all 4 biomarkers were significantly upregulated in SLE patients. In summary, this study identified TEX-related biomarkers in SLE, which may have important clinical implications for the diagnosis and treatment of SLE. Keywords: bioinformatics, enrichment analysis, immune microenvironment 1. Introduction Systemic lupus erythematosus (SLE) is a chronic autoimmune disease that affects multiple organ systems. [ 1 ] Approximately 3.4 million people worldwide have been diagnosed with SLE, which can lead to significant morbidity and mortality. [ 2 ] It is characterized by a breakdown of immune tolerance, leading to the production of a large number of autoantibodies targeting nuclear, cytoplasmic, and cell surface components of the body’s own cells. These autoantibodies form immune complexes by binding to their corresponding antigens, which subsequently deposit in various organs such as the skin, joints, and kidneys, triggering severe inflammatory responses. [ 1 ] The early symptoms of SLE are typically nonspecific, primarily presenting as fever, fatigue, weight loss, hair loss, rashes, and superficial lymphadenopathy. As the disease progresses, multi-organ involvement may occur, affecting the joints, kidneys, cardiovascular system, lungs, and nervous system. [ 3 ] Research indicates that SLE primarily affects adolescents and women of reproductive age. It is influenced by factors such as genetics, sex hormones, environmental factors (such as ultraviolet radiation and viral infections), and age. [ 4 ] The treatment goals of SLE include controlling the disease, reducing relapse rates, minimizing medication side effects, and improving quality of life. Common therapeutic approaches involve pharmacological interventions and lifestyle modifications. [ 5 ] Studies have shown that corticosteroids, antimalarial drugs (such as hydroxychloroquine), immunosuppressive agents (such as cyclophosphamide), and biologics (such as belimumab) have demonstrated certain efficacy in alleviating symptoms in the clinical management of SLE. [ 3 ] However, due to the complex etiology and diverse symptoms of SLE, individualized treatment plans must be developed based on the patient’s specific condition and affected organs, which limits the effectiveness of current therapeutic approaches. Therefore, there is a critical need for further research into the pathological mechanisms of SLE and the identification of its biomarkers, with the goal of developing novel and effective targeted therapeutic strategies. T-cell exhaustion (TEX) is a state characterized by the progressive loss of T-cell function and is widely observed in chronic infections, tumors, and autoimmune diseases. Its features include diminished effector function, reduced cytotoxicity, and decreased cytokine secretion of T cells under persistent antigen stimulation, together with the high expression of inhibitory receptors such as programmed cell death protein 1, T-cell immunoglobulin and mucin-domain containing-3, and lymphocyte activation gene 3. [ 6 , 7 ] These inhibitory receptors limit T-cell activation and proliferation by interfering with T-cell receptor signaling. [ 8 ] Furthermore, the TEX state is accompanied by metabolic reprogramming abnormalities, such as impaired mitochondrial function, reduced glycolysis, and irreversible changes in epigenetic modifications, rendering T cells unable to regain function even after antigen clearance. [ 9 ] This immunosuppressive state plays a double-edged role in maintaining immune homeostasis and preventing excessive immune responses. In recent years, studies have revealed that TEX is not only present in tumors and chronic infections, but is also prominent in SLE. In the peripheral blood of SLE patients and in spontaneous lupus mouse models (such as NZB/W F1), genes associated with TEX pathways (e.g., lymphocyte activation gene 3) are significantly upregulated, suggesting that TEX may be a consequence of chronic immune activation in SLE. Under the TEX state, CD8 + T cells exhibit decreased capacity to eliminate self-antigens, which may in turn lead to abnormal B-cell activation and sustained autoantibody production. [ 10 ] Notably, TEX may also serve as a molecular basis for SLE subtype stratification: some studies have divided SLE patients into different molecular subtypes based on the expression profiles of TEX-related genes (such as the TUTases family), with patients exhibiting a high TEX signature showing relatively lower SLE Disease Activity Index scores, suggesting that TEX may, to some extent, suppress excessive immune activation. [ 9 ] Taken together, TEX plays a central role in the pathogenesis of SLE, highlighting the need to identify exhaustion-related biomarkers and providing new potential targets for therapy in SLE patients. Accordingly, this study employed bioinformatics and machine learning methods to screen for biomarkers related to TEX in SLE, investigated their potential mechanisms, and validated their expression in clinical samples using reverse transcription-quantitative polymerase chain reaction (RT-qPCR). By analyzing key molecular mechanisms of disease onset at the level of gene regulation, we aim to identify potential biomarkers and therapeutic targets. These findings may provide new insights for the early diagnosis of SLE and offer a scientific basis for the development of future personalized treatment strategies. 2. Materials and methods 2.1. Data acquisition and processing GSE72326 ( GPL10558 ) training set containing human whole blood samples from 157 SLE patients and 20 controls ( Table S1 , Supplemental Digital Content) and GSE72509 ( GPL16791 ) validation set containing human whole blood samples from 99 SLE samples and 18 controls ( Table S2 , Supplemental Digital Content) were received from the Gene Expression Omnibus (GEO) database ( https://www.ncbi.nlm.nih.gov/geo/ ) on October 16, 2024. [ 11 , 12 ] The 683 T-cell exhaustion-related genes (TEXRGs) were obtained from the references ( Table S3 , Supplemental Digital Content). [ 13 ] The flowchart of this study is shown in Figure S1 , Supplemental Digital Content. To minimize the potential impact of confounding factors, microarray data and sample information were first downloaded from the GEO database, and the expression matrix was extracted. Regular expressions were then used to filter samples from SLE patients and healthy control groups, and samples with missing values were removed. Subsequently, the chip annotation file corresponding to the respective GPL number was downloaded, and probes that did not match gene symbols or mapped to multiple genes were filtered out. For each gene, if multiple probes corresponded to it, the average expression value was calculated, and the probe with the highest expression level was selected as the representative for that gene. Finally, invalid gene symbols were removed, and gene symbols were used as row names to construct the final gene expression matrix. 2.2. Recognition of candidate genes Differentially expressed genes (DEGs) between SLE and control groups were analyzed in training set via the limma (v 3.58.1; Bioconductor) package (|log 2 fold change [FC]| > 0.5, adjusted P < .05). [ 14 ] Volcano plot was plotted via ggplot2 (v 3.4.4; CRAN/R Project), and the up- and down-adjusted genes for each top 5 were labeled according to the order of |log 2 FC|. [ 15 ] In addition, a heatmap was generated using by the ComplexHeatmap package (v 2.18.0; Bioconductor) to illustrate the expression patterns of up- and down-adjusted top 20 DEGs (sorted by |log 2 FC|). [ 16 ] Candidate genes were obtained by intersecting DEGs and TEXRGs using the ggvenn (v 0.1.10; CRAN/R Project) package. [ 17 ] 2.3. Functional enrichment and protein–protein interaction analyses of candidate genes For the purpose of getting a better grasp of the biological role of these candidate genes. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed via clusterProfiler (v 4.10.0; Bioconductor) package and org.Hs.eg.db (v 3.18.0; Bioconductor; P < .05). [ 18 , 19 ] The GO system had 3 parts, comprising molecular functions, cellular components, and biological processes. After sorting according to P value, the GOplot (v 1.0.2; CRAN/R Project) package was used to display the top 5 results in 3 parts of GO and the top 10 results of KEGG. [ 20 ] To gain insight into the interactions between the proteins encoded by the candidate genes, the obtained candidate genes were aligned to the Search Tool for the Retrieval of Interacting Genes/Proteins database ( https://string-db.org/ ; confidence = 0.4). The protein–protein interaction (PPI) network was presented via Cytoscape (v 3.10.2; The Cytoscape Consortium) software. [ 21 ] The CytoHubba plugin (v 0.1; Institute of Information Science, Academia Sinica) for Cytoscape software was applied to figure out the genes for top 20 of the 4 methods, namely, edge percolated component, stress, radiality, and betweenness centrality, and depicted via Cytoscape (v 3.10.2) software. [ 22 ] Candidate key genes were then obtained by taking the intersection of top 20 genes from 4 algorithms by ggvenn (v 0.1.10) package. 2.4. Identification of biomarkers In the training set, based on the candidate key genes, feature genes were obtained by learning Least Absolute Shrinkage and Selection Operator (LASSO; family = binomial, type.measure = class) and Support Vector Machine-Recursive Feature Elimination (SVM-RFE) via the glmnet package (v 4.1-8; CRAN/R Project) and e1071 (v 1.7-14; CRAN/R Project), respectively. [ 23 , 24 ] LASSO, a regularization regression method, was employed to automatically screen important features and eliminate irrelevant ones. A 10-fold cross-validation was utilized to optimize the regularization parameter λ, aiming at minimizing the mean squared error. Ultimately, the λ value that resulted in the smallest validation error was selected, thereby more reliably evaluating the model’s generalization capability and mitigating the risk of overfitting or underfitting. The SVM model employed 10-fold cross-validation with a random seed set to 2. The RFE algorithm (rfe.method = “svm”) was used to recursively eliminate less important features, as evaluated by the SVM model, and gradually select the optimal variables. Key feature genes were then obtained by intersecting the LASSO and SVM-RFE feature genes via the ggvenn (v 0.1.10) package. Next, the expression differences of key feature genes in SLE and control samples were analyzed by the Wilcoxon test ( P < .05), and genes that were significantly different within both the training and validation sets and had consistent trends in expression were regarded as biomarkers. To assess the predictive utility of biomarkers for SLE, receiver operating characteristic (ROC) analyses of biomarkers were performed in 2 datasets by the pROC (v 1.18.5; CRAN/R Project) package (area under the curve [AUC] value > 0.8). [ 25 ] 2.5. Development and verification of nomogram In training set, founded on biomarkers, a nomogram was constructed by the rms (v 6.7-1; CRAN/R Project) package to predict the probability of SLE patients. [ 26 ] At the same time, the calibration curve was plotted to check the accuracy of the model; the closer the slope was to one, the more accurate the prediction. Then, the pROC (v 1.18.5) and rmda (v 1.6; CRAN/R Project, https://github.com/mdbrown/rmda .) packages were used to plot the ROC curves and decision curve analysis in turn to appraise the predictive capacity of nomogram. The closer the AUC value was to one, the more accurate the nomogram model (AUC > 0.7). 2.6. Gene set enrichment analysis To explore the pathways and latent biological mechanisms for biomarkers in training set, c2.cp.kegg.v7.4.symbols.gmt and c5.all.v7.4.entrez.gmt as the background gene sets were downloaded from Molecular Signature Database (MSigDB; https://www.gsea-msigdb.org/gsea/msigdb ). Moreover, the association between each biomarker and other genes was analyzed via the corrplot (v 0.92; CRAN/R Project) package, and the list of genes was generated by sorting the correlation coefficients (cor) from largest to smallest. [ 27 ] Finally, gene set enrichment analysis (GSEA) analysis was performed with the clusterProfiler (v 4.10.0) package (adjusted P < .05, |normalized enrichment score| > 1), and the noticeably enriched top 5 results were visualized. 2.7. Immune microenvironment analysis In training set, the plentifulness of 22 immune cells in every SLE sample and control sample was calculated using the CIBERSORT algorithm of IOBR (v 0.99.8; IOBR Project/GitHub) package (infiltration level > 0, P < .05). [ 28 , 29 ] The distinctions in immune cell infiltration between SLE and control samples were examined by means of the Wilcoxon test ( P < .05), and visualized by the ggplot2 (v 3.4.4) package. Spearman correlation analysis of differential immune cells and biomarkers was performed via the psych (v 2.4.3; CRAN/R Project) package (|cor| > 0.3, P < .05), visualized by the ggplot2 (v 3.4.4) package. [ 30 ] 2.8. Construction of molecular regulatory networks To investigate the molecular regulatory mechanism of biomarkers, first, the transcription factors (TFs) of biomarkers were predicted using the Joint Annual Survey of Professional Associations-Research ( https://jaspar.elixir.no/ ) database from the miRNet ( https://www.mirnet.ca/ ) website. The microRNAs (miRNAs) of biomarkers were predicted through the MicroRNA Data Integration Portal (MirDIP; https://ophid.utoronto.ca/mirDIP ) website. Based on the obtained miRNAs, TFs, and biomarkers, the Cytoscape (v 3.10.2) software was employed to establish the TF-mRNA, miRNA-mRNA, and TF-miRNA-mRNA networks. 2.9. The drug prediction and molecular docking of biomarkers The comparative toxicogenomics database ( http://ctdbase.org ) was used to explore drugs associated with biomarkers. And the Cytoscape (v 3.10.2) software was used to construct the biomarker-drug network. In order to further clarify the binding ability of biomarkers and drugs, the predicted drugs were then ranked according to their “Interaction Count,” and the top-ranked drug for each biomarker was selected for molecular docking. First, the 3-dimensional structures of the protein of biomarker were retrieved from the PDB ( https://www1.rcsb.org/ ) database and small molecules as well as water molecules that were carried were eliminated, and protein hydrogenation and calculation of charges were done by AutoDock Tools (v 1.5.7; The Scripps Research Institute). [ 31 ] The 2-dimensional molecular structural formula of the drug was then fetched from the PubChem ( https://pubchem.ncbi.nlm.nih.gov/ ) database, and the small molecules were scrutinized for charge equilibrium, rotatable bonds, and the range of docking boxes selected based on the active center using AutoDock Tools. Ultimately, the molecular docking was conducted by AutoDock Vina ( https://vina.scripps.edu/ ), and visualized by PyMol software (v 2.6.0a0; Schrödinger, LLC; a docking score below −5 kcal/mol was indicative of a high binding affinity between the drugs and the biomarkers). [ 32 ] 2.10. Reverse transcription-quantitative polymerase chain reaction The biomarkers were then further validated by RT-qPCR. In this study, a total of 10 pairs of samples (10 control and 10 SLE whole blood samples) of patients were acquired from the First Hospital of Jinzhou Medical University [ 33 ] ( Table S4 , Supplemental Digital Content). SLE patients were diagnosed according to the 2019 American College of Rheumatology/European League Against Rheumatism classification criteria. Exclusion criteria included the presence of concurrent infections or other autoimmune diseases. All samples were collected as whole blood. Healthy controls were defined as individuals with no history of autoimmune diseases and normal liver and renal function. The study was approved by the Medical Research Ethics Committee of the First Affiliated Hospital of Jinzhou Medical University. The patients had signed an informed consent form. Then, RNA from each sample was extracted with 1 mL TRIzol reagent and 300 μL chloroform, and its concentration was quantified with NanoPhotometer N50 (Implen). The cDNA was reverse transcribed using the SweScript First Strand cDNA Synthesis Kit (Servicebio, Wuhan, China). RT-qPCR amplification was accomplished by the CFX96 Real-Time Fluorescence Quantitative PCR Instrument (Bio-Rad, California) for a total of 40 cycles. One cycle consisted of 1 minute of pre-denaturation at 95°C, denaturation at 95°C for 20 seconds, annealing at 55°C for 20 seconds, and extension at 72°C for 30 seconds. Finally, the amplification curve, melting curve, and Cq value were analyzed. The Ct values of biomarkers and the calibrator gene Glyceraldehyde-3-Phosphate Dehydrogenase ( GAPDH ) were employed to calculate the relative expression of every biomarker in each sample by the 2 −△△Ct method. The primer sequences of biomarkers and GAPDH were listed in Table S5 , Supplemental Digital Content. 2.11. Statistical analysis R language (v 4.3.2; R Foundation for Statistical Computing) was leveraged in this study for bioinformatics analyses. [ 34 ] The limma package (v 3.58.1; Bioconductor) was used to compare gene expression differences between SLE and control patients in the dataset. The clusterProfiler package (v 4.10.0) was applied for functional enrichment analysis. Feature genes were identified via machine learning using the glmnet (v 4.1-8) and e1071 (v 1.7-14) packages, and the pROC package (v 1.18.5) was used to assess their diagnostic performance. The rms package (v 6.7-1) was used to construct a nomogram for key genes, and the psych package (v 2.4.3) for Spearman correlation analysis. Wilcoxon rank-sum tests were used for pairwise comparisons, with a threshold of P < .05 for significance. GraphPad Prism software (v 8.0; GraphPad Software) was utilized to analyze RT-qPCR data. [ 35 ] 3. Result 3.1. Identification and functional enrichment of 91 candidate genes In the SLE and control groups, a total of 389 DEGs (|log 2 FC| > 0.5, adjusted P < .05), including 119 downregulated and 270 upregulated in the SLE group (Fig. 1 A and B, Table S6 , Supplemental Digital Content). Subsequently, an intersection of 389 DEGs and 683 TEXRGs identified 91 candidate genes (Fig. 1 C, Table S7 , Supplemental Digital Content). Moreover, GO enrichment analyses yielded 480 terms ( P < .05), with 404 biological processes such as response to virus and defense response to virus, 23 cellular components like RNA polymerase II transcription regulator complex and membrane raft, and 53 molecular functions including double-stranded RNA binding, cytokine receptor binding, and G protein-coupled receptor binding (Fig. 1 D, Table S8 , Supplemental Digital Content). KEGG analysis enriched 44 pathways ( P < .05), such as RIG-I-like receptor signaling pathway, Toll-like receptor signaling pathway, and JAK-STAT signaling pathway (Fig. 1 E, Table S9 , Supplemental Digital Content). These results clearly demonstrated that viral infection and receptor binding had a substantial impact on the pathogenesis of SLE. Figure 1. Open in a new tab A total of 389 differentially expressed genes were intersected with 683 T-cell exhaustion-related genes to identify 91 candidate genes, which were then subjected to functional enrichment analysis. (A) Volcano plot of differentially expressed genes between SLE and control groups. The x-axis represented the log 2 value of the fold change, while the y-axis represented the −log 10 of the adjusted P value. Yellow represented highly expressed genes, blue represented lowly expressed genes, and gray represented genes with no significant difference. (B) Heat map of differentially expressed genes between SLE and control groups. Each small square represented a gene, with yellower colors indicating higher expression levels and bluer colors indicating lower expression levels. The color bar in the first row of the figure represented sample grouping. (C) Venn diagram for identification of candidate genes. A total of 91 candidate genes were obtained from the intersection. (D) Tree diagram for GO enrichment analysis of candidate genes. The top 5 pathways of BP, MF, and CC were displayed from left to right, and the branches in the diagram represented genes enriched in these pathways. (E) KEGG pathway enrichment plot of candidate genes. The top 10 significant pathways were displayed, with points representing genes enriched in these pathways, green indicating downregulated genes, and purple indicating upregulated genes. The color of the innermost squares in the plot represented the z -score, indicating the upregulation or downregulation of a particular pathway. BP = biological process, CC = cellular component, GO = Gene Ontology, KEGG = Kyoto Encyclopedia of Genes and Genomes, MF = molecular function, SLE = systemic lupus erythematosus. 3.2. IFIT3, IFIH1, CXCL10, and STAT2 as biomarkers Furthermore, a PPI network was established based on the 91 candidate genes; after removing the 10 isolated nodes, the network contains 81 nodes and 1136 edges; the STAT2 , IFIH1 , and CXCL10 , IFIT3 showed strong interactions with other proteins (Fig. 2 A). Subsequently, each of the 4 algorithms obtained the top 20 genes respectively, and then the intersection of the obtained genes was taken to acquire 9 candidate key genes (Fig. 2 B and C). Figure 2. Open in a new tab Four biomarkers ( IFIT3 , IFIH1 , CXCL10 , and STAT2 ) were screened out by machine learning, which showed high diagnostic efficacy in both the training and validation sets. (A) PPI network of candidate genes. (B) Key gene networks ranked top 20 by 4 algorithms in CytoHubba. Node colors represented algorithm-specific scores (redder hues indicated higher values). (C) Venn diagram of 4 algorithms to screen out 9 intersecting genes. (D) Five candidate feature genes were selected by the Lasso model. The left panel showed the change trajectory of each independent variable coefficient, with the ordinate representing the coefficient value. The right panel had the abscissa as log(Lambda) and the ordinate as the cross-validation error. (E) Seven candidate feature genes were selected by the support vector machine model. The ordinate represented the model accuracy under different numbers of feature genes, and the abscissa represented the number of feature genes. The red circle indicates that the maximum accuracy of 0.954 was achieved when N = 7. (F) Venn diagram of key characterization genes. (G) Boxplots of key feature gene expression in training set GSE72326 and validation set GSE72509 (Wilcoxon test). (H) ROC curves for 4 key feature genes in training set GSE72326 and validation set GSE72509 . The y-axis represented sensitivity (true positive rate, TPR), and the x-axis represented 1 − specificity (false positive rate, FPR). PPI = protein–protein interaction, ROC = receiver operating characteristic. Then, 5 feature genes were revealed by the LASSO analysis (optimal lambda = −4.579), while 7 were identified by the SVM-RFE approach (the maximum accuracy of 0.954; Fig. 2 D and E). Next, an intersection of the 5 and 7 feature genes was performed to identify 4 key feature genes ( IFIT3 , IFIH1 , CXCL10 , and STAT2 ; P < .05; Fig. 2 F). Finally, 4 genes had a high level of expression in the SLE group (Fig. 2 G). In the training dataset GSE72326 , the AUC values of IFIT3, IFIH1, CXCL10, and STAT2 were 0.882, 0.856, 0.891, and 0.913, respectively. In the validation dataset GSE72509 , their AUC values were 0.837, 0.812, 0.865, and 0.8, all of which were higher than 0.8 (Fig. 2 H). The expression trends of 4 biomarkers were coherent in training set and validation set, implying that 4 genes possessed superior diagnostic potency for SLE and were established as biomarkers. 3.3. Nomogram had better diagnostic value for SLE The results of nomogram based on IFIT3 , IFIH1 , CXCL10 , and STAT2 showed that the higher the total score, the higher incidence of SLE (Fig. 3 A). The predictive performance of the model was better, as the slope of the model was close to 1 in the calibration curve (Fig. 3 B). The AUC value was 0.969, and the clinical utility of the nomogram was superior to that of the individual genes, suggesting a better predictive performance for SLE of nomogram (Fig. 3 C and D). Figure 3. Open in a new tab Nomogram based on 4 biomarker constructions. (A) Prediction of morbidity based on biomarker-constructed nomogram. On the left were the biomarkers, and on the right, each biomarker’s corresponding line was labeled with a scale representing the range of values the biomarker could take. The length of the line segment reflected the contribution of the biomarker to the disease, where “point” indicated the individual score corresponding to different values of each biomarker; “Total score” represented the sum of individual scores from all biomarkers; and “Pr” indicated the probability of having SLE. (B) Calibration curves for nomogram. The x-axis showed the model-predicted probability, and the y-axis showed the actual probability. The “Ideal” line represented perfect consistency between model predictions and actual outcomes in an ideal scenario. (C) The ROC curve of the nomogram. (C) The decision curve of the nomogram. The x-axis indicated threshold probability (Pt). Net benefit (y-axis) was calculated as: Net benefit = true-positive rate − (false-positive rate × Pt/[1 − Pt]). ROC = receiver operating characteristic, SLE = systemic lupus erythematosus. 3.4. GSEA of IFIT3, IFIH1, CXCL10, STAT2 Then, the biological pathways of biomarkers were analyzed. The GSEA results for the biomarkers revealed that CXCL10 was significantly enriched in 576 GO pathways like organellar ribosome, mitochondrial ribosome, and 123 KEGG pathways like oxidative phosphorylation, proteasome, and ribosome (Fig. 4 A, Table S10 , Supplemental Digital Content); IFIH1 was predominantly enriched in 590 GO pathways like defense response to symbiont, cytoplasmic translation, negative regulation of viral genome replication, and 85 KEGG pathways like ribosome, NOD-like receptor signaling pathway (Fig. 4 B, Table S11 , Supplemental Digital Content); IFIT3 was markedly enriched in 919 GO pathways like cytoplasmic translation, negative regulation of viral process, cytosolic ribosome and 112 KEGG pathways such as ribosome, NOD-like receptor signaling pathway (Fig. 4 C, Table S12 , Supplemental Digital Content); STAT2 was substantially enriched in 830 GO pathways like cytoplasmic translation, cytosolic ribosome, cytosolic large ribosomal subunit and 101 KEGG pathways like ribosome, NOD-like receptor signaling pathway (adjusted P < .05; Fig. 4 D, Table S13 , Supplemental Digital Content). These KEGG results showed that all 4 biomarkers were enriched in ribosomes, and IFIH1 , IFIT3 , and STAT2 were co-enriched in the NOD-like receptor signaling pathway. While GO results showed that IFIT3 , IFIH1 , and STAT2 were co-enriched in cytoplasmic translation. The above findings indicated that the mechanisms of biomarkers played a significant part in SLE. Figure 4. Open in a new tab Biomarker-based single-gene GSEA. (A) CXCL10 , (B) IFIH1 . (C) IFIT3 , (D) STAT2 . The first part showed a line chart of the gene ES, with the vertical axis representing the corresponding Running ES. A peak was present in the line chart, which was the ES of the gene set, and the genes before the peak were the core genes under this gene set. The horizontal axis represents each gene in the gene set. The second part used vertical lines to represent genes in the pathway, sorted by expression level from left to right. The third part was a distribution map of all genes’ rank values, based on a list of gene correlations calculated using the Spearman method, corresponding to the vertical axis title. If the ES was positive, the relevant genes were on the left side of the peak; if negative, they were on the right side of the peak. ES = enrichment score, GSEA = gene set enrichment analysis. 3.5. Immune microenvironment in SLE and control groups Five types of immune cells with an infiltration level of 0 were filtered out. The ratios of 17 immune cells in SLE and control groups indicated that T CD8 cells, T CD4 naive cells, and monocytes were abundant ( P < .05; Fig. 5 A). Subsequently, a total of 4 significantly distinct immune cells were identified ( P < .05). In the SLE samples, the infiltration levels of monocytes and dendritic activated cells were significantly higher compared with the control samples, while the infiltration levels of T CD4 memory resting cells and natural killer (NK) resting cells were significantly higher in control samples (Fig. 5 B). IFIT3 was most significant positive correlation with dendritic activated cells (cor = 0.44, P < .001) and a substantial negative linkage with T CD4 memory resting cells (cor = 0.35, P < .001); while CXCL10 exhibited a remarkable positive linkage with monocytes (cor = 0.39, P < .001; Fig. 5 C, Table S14 , Supplemental Digital Content). Figure 5. Open in a new tab Infiltration of monocytes and activated dendritic cells was increased in SLE patients and showed significant correlations with biomarkers. (A) Percentage of different immune cells in samples from SLE and control groups. (B) Box line plot of the difference in the proportion of immune cells (Wilcoxon test). ** P < .01, *** P < .001, **** P < .0001. (C) Heatmap of correlation between biomarkers and differential immune cells. *represents P < .05, ** represents P < .01. NK = natural killer, ns = not significant, SLE = systemic lupus erythematosus. 3.6. Molecular regulatory networks and molecular docking of IFIT3, IFIH1, CXCL10, STAT2 The molecular regulatory mechanisms of biomarkers were investigated through miRNA-TF, miRNA-mRNA, and TF-mRNA-miRNA networks. There were 147 nodes (24 TFs, 119 miRNAs, 4 mRNAs) and 155 edges in the TF-miRNA-mRNA network, where STAT2 was directly regulated by 30 miRNAs (Filtered by Score.Class = Very High). For example, IFIH1 and CXCL10 were possibly co-regulated by NFKB1 and RELA , while the IFIT3 and STAT2 were potentially co-regulated by GATA2 (Fig. 6 A, Table S15 , Supplemental Digital Content). IFIT3 and CXCL10 were co-regulated by hsa-miR-296-5p; IFIH1 was directly adjusted by hsa-miR-223-3p, while STAT2 was directly controlled by hsa-miR-197-5p and hsa-miR-6807-5p (Fig. 6 A, Table S15 , Supplemental Digital Content). Figure 6. Open in a new tab Molecular regulatory networks and molecular docking. (A) miRNA-TF-mRNA regulatory network, miRNA-mRNA regulatory network, and mRNA-TF regulatory network. Pink nodes represented biomarkers; orange nodes represented miRNAs, and yellow nodes represented TFs. (B) Biomarker-targeted drug prediction. Cyan nodes represented biomarkers; red nodes represented predicted drugs. (C) Docking affinity of CXCL10 with Poly I-C was −5.5 kcal/mol. (D) Docking affinity of IFIH1 with bisphenol A was −6.8 kcal/mol. (E) Docking affinity of IFIT3 with tretinoin was −5.3 kcal/mol. (F) Docking affinity of STAT2 with bisphenol A was −7.0 kcal/mol. TF = transcription factor. The Comparative Toxicogenomics Database was utilized to predict potential drugs targeting biomarkers for the treatment of SLE. The analysis identified 82 drugs associated with CXCL10 , 9 drugs linked to IFIT3 , 7 drugs linked to IFIH1 , and 7 drugs linked to STAT2 . Notably, bisphenol A (BPA), benzo(a)pyrene, and lipopolysaccharides emerged as common predicted drugs across all 4 biomarkers (Fig. 6 B, Table S16 , Supplemental Digital Content). The molecular docking results indicated that the binding energy of CXCL10 with Poly I-C was −5.5 kcal/mol (Fig. 6 C), and the binding energy of IFIH1 with BPA was −6.8 kcal/mol (Fig. 6 D). In addition, IFIT3 exhibited a binding energy of −5.3 kcal/mol with tretinoin (Fig. 6 E), and the binding energy of STAT2 with BPA was −7.0 kcal/mol (Fig. 6 F), demonstrating the strongest binding affinity among the interactions studied. 3.7. RT-qPCR validation of IFIT3, IFIH1, CXCL10, and STAT2 The RT-qPCR results showed that IFIT3 , IFIH1 , CXCL10 , and STAT2 were significantly upregulated in SLE patients when compared with controls ( P < .05; Fig. 7 A–D). Furthermore, the outcomes of RT-qPCR corresponded to training and validation sets, suggesting that database predictions were dependable. Figure 7. Open in a new tab RT-qPCR validation of (A) IFIT3 , (B) IFIH1 , (C) CXCL10 , and (D) STAT2 (GADPH as internal reference gene, using t test,* P < .05). RT-qPCR = reverse transcription-quantitative polymerase chain reaction, SLE = systemic lupus erythematosus. 4. Discussion In this study, bioinformatics analysis combined with machine learning algorithms was used to identify 4 key TEX-related biomarkers – IFIT3 , IFIH1 , CXCL10 , and STAT2 . These biomarkers were found to be significantly upregulated in SLE patients, and their diagnostic performance was validated by ROC analysis (all AUCs > 0.8). Functional enrichment analysis revealed that these biomarkers are mainly involved in ribosome biogenesis, negative regulation of viral genome replication, and the NOD-like receptor signaling pathway, suggesting that they may drive SLE progression by modulating immune responses and disrupting protein synthesis homeostasis. These findings highlight the pivotal role of TEX-related biomarkers in the immunopathology of SLE and provide novel theoretical insights for future precision diagnosis and targeted therapy. In this study, we performed a comprehensive differential expression analysis of the GSE72326 dataset and successfully identified 389 DEGs. Through intersection analysis with the TEXRGs dataset, we further filtered out 91 candidate genes (DE-TEXRGs). To explore the potential biological functions and pathways these genes may be involved in, we conducted GO and KEGG functional enrichment analyses. These genes showed significant enrichment in multiple key biological pathways, particularly pathways related to viral response mechanisms, which demonstrated a strong correlation. It is noteworthy that pathways related to “viral response” are crucial for understanding how host cells detect and respond to viral infections. These pathways involve various molecular interactions that facilitate the recognition of viral components, leading to the activation of innate immune responses. These immune responses play a critical role in controlling viral replication and preventing its spread, suggesting the potential involvement of these genes in regulating the immune response process. [ 36 ] Furthermore, these genes also show significant enrichment in the JAK/STAT signaling pathway, which is considered a crucial immune regulatory pathway in the pathogenesis of SLE. [ 37 ] These immune responses are crucial for controlling viral replication and preventing its transmission, indicating the potential role of these genes in regulating the immune response process. Subsequently, we constructed a PPI network based on these 91 candidate genes and identified the top 20 genes using 4 different algorithms. By taking the intersection of these results, we obtained 9 key candidate genes. Using LASSO and SVM machine learning algorithms, along with expression validation and ROC analysis, we identified 4 biomarkers ( IFIT3 , IFIH1 , CXCL10 , and STAT2 ). IFIT3, or interferon (IFN)-induced protein with tetratricopeptide repeats 3, is a protein induced by IFNs. [ 38 ] Previous studies have identified IFIT3 as one of the genes responsible for the overactivity of the cyclic GMP-AMP synthase/stimulator of interferon genes signaling pathway in human SLE monocytes. Therefore, IFIT3 may be a new therapeutic target to block the production of type I IFN and other pro-inflammatory cytokines by the cyclic GMP-AMP synthase/stimulator of interferon genes signaling pathway in SLE patients. [ 39 ] In addition, Li et al [ 40 ] highlighted in their study that IFIT3 , as one of the core shared genes, demonstrates a strong association with multiple inflammation and immune response pathways, indicating its significant role in SLE and other related diseases. In conclusion, this gene can be studied as a potential biomarker to provide new ideas for clinical diagnosis and treatment. IFIH1, also known as melanoma differentiation-associated protein 5 (MDA5), is an IFN-induced helicase C domain-containing protein and a member of the DEAD-box helicase family. [ 41 ] The importance of the IFIH1 gene and its single nucleotide polymorphisms in SLE has gained increasing support from recent studies. Several studies have shown that the T allele at the rs1990760 motif on the IFIH1 gene is strongly associated with the risk of developing SLE. [ 42 – 44 ] For example, a study involving 357 Caucasian SLE patients found that variations in the IFIH1 gene were associated with changes in the levels of several inflammatory mediators, such as interleukin-6 and IFN-gamma-induced protein 10, suggesting that IFIH1 may play a role in the pathogenesis of SLE by regulating inflammatory responses. [ 45 ] In addition, a study in the Chinese population demonstrated that variations at the rs1990760 locus were associated with levels of interleukin-18 and granzyme B in SLE patients, further highlighting the role of IFIH1 in the pathogenesis of SLE. [ 46 ] Although some other studies have failed to establish a direct association between rs1990760 and SLE, they still underscore the potential role of IFIH1 in autoimmune diseases. [ 47 , 48 ] In conclusion, variations in the IFIH1 gene, particularly at the rs1990760 locus, may play a key role in the pathogenesis of SLE by influencing the expression of inflammatory mediators and modulating immune responses. These findings provide new insights for the development of future therapeutic and preventive strategies for SLE. CXCL10, officially known as C-X-C motif chemokine 10, is also referred to as IFN-gamma-induced protein 10. [ 49 ] The importance of CXCL10 in SLE has been widely supported by research. Studies have shown a significant correlation between CXCL10 levels and the SLE Disease Activity Index, making it a potential biomarker for assessing SLE activity. [ 50 ] Specifically, elevated levels of CXCL10 are associated with renal involvement in lupus patients, particularly in those with lupus nephritis (LN). The levels of CXCL10 in urine and serum are significantly higher in LN patients compared with those with SLE without renal involvement, making it a potential noninvasive diagnostic tool for monitoring LN activity. [ 51 – 54 ] In addition, CXCL10 plays a key role in chronic inflammation by promoting the migration of T cells and NK cells to sites of inflammation through its interaction with its receptor, CXCR3. [ 55 , 56 ] Notably, elevated levels of CXCL10 are negatively correlated with other inflammatory markers, such as serum complement levels and anti-double-stranded DNA antibody levels, further supporting its potential application in clinical evaluation. [ 57 , 58 ] In conclusion, CXCL10 plays a critical role in the pathogenesis of SLE. Its potential as a biomarker for monitoring disease activity warrants further investigation, with the aim of providing more precise diagnostic and therapeutic strategies for clinical application. Signal transducer and activator of transcription 2 ( STAT2 ), as a cofactor, participates in the signaling process of type I IFNs, including IFN-α, IFN-β, IFN-τ, and IFN-ω. [ 59 ] It plays a crucial role in the pathogenesis of SLE by modulating immune regulation through the JAK/STAT signaling pathway. [ 60 , 61 ] It is worth noting that targeted interventions on the JAK/STAT signaling pathway have emerged as a new direction in the treatment of SLE. For instance, JAK inhibitors such as baricitinib have demonstrated potential efficacy in clinical trials for SLE patients, likely due to their inhibitory effects on STAT2 and associated signaling pathways, thereby reducing inflammation and immune responses. [ 62 ] In addition, the overexpression of STAT2 is closely associated with clinical features of SLE, such as the occurrence of skin lesions and arthritis. [ 63 ] Previous studies have indicated that aberrant activation of STAT2 may contribute to the exacerbation of autoimmune responses, leading to the production of antibodies and subsequent tissue damage. [ 64 ] Therefore, research on STAT2 and its signaling pathways has not only enhanced our understanding of the pathogenesis of SLE but also provided important insights for the development of novel therapeutic strategies. The results of the GSEA clearly highlight the key roles of the identified biomarkers in several biological pathways, particularly in the ribosome, NOD-like receptor signaling pathways, and cytoplasmic translation. Specifically, the ribosome pathway is significantly enriched in all 4 biomarkers, a pathway that is crucial for protein synthesis and the maintenance of cellular function. Studies have shown that the biosynthesis and dysfunction of the ribosome are closely associated with various diseases, including autoimmune disorders. [ 65 ] This finding further suggests that alterations in ribosomal activity may play a critical role in the pathophysiology of SLE. In addition, the NOD-like receptor signaling pathway is commonly enriched in the 3 biomarkers, IFIH1 , IFIT3 , and STAT2 , highlighting its pivotal role in the innate immune response. This pathway, by recognizing pathogen-associated molecular patterns and initiating inflammatory responses, effectively influences the production of pro-inflammatory cytokines, which are typically elevated in SLE patients. [ 66 ] Therefore, there is a close association between this pathway and the exacerbation and progression of the disease. Finally, the enrichment of the cytoplasmic translation pathway in IFIH1 , IFIT3 , and STAT2 highlights the critical importance of translation control in immune responses. The cytoplasmic translation process is crucial for the rapid synthesis of proteins involved in immune signaling and the response to viral infections. [ 67 ] Given that SLE is characterized by abnormal immune responses, a deeper understanding of the regulatory mechanisms of cytoplasmic translation may provide new insights for therapeutic targets aimed at modulating immune activity in SLE. [ 68 ] Taken together, these findings reveal their potential mechanisms in SLE, further advancing our research into therapeutic targets for this complex disease. Immune infiltration analysis revealed significant differences in the infiltration levels of various immune cell types between SLE and control samples. In particular, monocytes played a key role in the immune environment of SLE, with their infiltration levels markedly higher than those in the control group. Their increased presence in SLE may contribute to the disease’s pathogenesis by promoting inflammation and autoimmunity. [ 69 ] Dendritic cells also showed a significant increase in SLE samples. These antigen-presenting cells play a crucial role in linking innate and adaptive immunity, facilitating the activation and differentiation of T cells. [ 70 ] The positive correlation between dendritic cell activation and IFIT3 suggests that the functional state of these cells may be regulated or influenced by IFIT3 . The increase in dendritic cells may reflect a persistent state of immune activation, potentially exacerbating the disease by promoting autoreactive T-cell responses. In contrast, the number of T CD4+ memory resting cells was higher in control samples, suggesting a potential deficiency of this cell type in SLE patients. Memory T cells are crucial for maintaining long-term immunity, and their reduced infiltration may impair the ability to mount an effective immune response. [ 71 ] In conclusion, these findings elucidate the complex immune microenvironment in SLE and underscore the need for further research into the functional roles of these immune cell populations in disease progression, as well as potential therapeutic interventions. The construction of the molecular regulatory network provided important insights into the potential regulatory mechanisms of the 4 biomarkers identified in this study. Notably, we found that IFIH1 and CXCL10 may be co-regulated by NFKB1 and RELA , while IFIT3 and STAT2 may be regulated by GATA2 . NFKB1 and RELA are the 2 main TF subunits in the canonical NF-κB signaling pathway. Together, they form the p50/p65 complex, which, upon stimulation by pathogens or pro-inflammatory factors, translocates into the nucleus and binds to promoter regions of target genes, thereby initiating the transcription of downstream genes. [ 72 ] IFIH1 (also known as MDA5) serves as a key receptor for sensing viral RNA within cells. It not only activates type I IFN responses but can also be induced by NF-κB signaling, further amplifying antiviral and inflammatory responses. [ 73 ] For example, in mice ( Mus musculus ), activation of NF-κB has been shown to induce the expression of Ifih1 , thereby enhancing antiviral responses. [ 74 ] Similarly, CXCL10 is an IFN-inducible chemokine that attracts T cells and NK cells to sites of inflammation. Its promoter contains NF-κB binding sites and can be directly activated by RELA/p65. [ 75 , 76 ] Studies have shown that in models of viral infection or autoimmune diseases, the NFKB1-RELA/p65 complex can upregulate IFIH1 expression, [ 73 ] while the expression of CXCL10 can be co-induced by NF-κB, STAT1, and IRF1. [ 77 ] In addition, similar mechanisms may be present in mouse models. [ 78 ] GATA2 is a key zinc-finger TF that plays an essential role in the differentiation, proliferation, and functional maintenance of cells in the hematopoietic and immune systems. [ 79 ] GATA2 may regulate the transcriptional activity of downstream target genes such as IFIT3 and STAT2 , thereby influencing IFN signaling, inflammatory responses, and immune cell functions. [ 80 , 81 ] These inferences are based on database prediction results and require further experimental validation to confirm their biological relevance. It is also noteworthy that previous studies have demonstrated that CEACAM1 can interact with T-cell immunoglobulin and mucin-domain containing-3 to regulate TEX and immune tolerance, suggesting that TEX in SLE patients may be influenced by a synergistic network of immune regulatory molecules. [ 82 ] The existence of such mechanisms indicates that the TEX state is not the result of a single pathway but is driven by a complex regulatory network, providing new perspectives for understanding the immunopathology of SLE. The identification of potential therapeutic drugs targeting biomarkers is a crucial step in developing effective treatment strategies. In this study, BPA emerged as a key candidate, demonstrating strong binding affinity with IFIH1 and STAT2 . BPA, a widely used synthetic compound, has garnered increasing attention for its regulatory effects on the immune system. [ 83 , 84 ] Research has shown that BPA can decrease the number of regulatory T cells (Tregs) and upregulate the production of pro-inflammatory and anti-inflammatory cytokines, indicating its potential role in immune dysregulation and autoimmune diseases. [ 85 ] In addition, the impact of BPA and its analogs, such as bisphenol S, on the gut microbiome has also attracted attention from researchers. BPA intake may further modulate immune responses by influencing the gut microbiome, thereby affecting host health. [ 86 , 87 ] These studies provide new insights into the potential therapeutic applications of BPA in immune-related diseases, particularly in the context of autoimmune disorders. In addition, lipopolysaccharide is one of the predictive drugs in our study, known for its potent immune activation capabilities. [ 88 ] It triggers an inflammatory response through the TLR4 signaling pathway and may also regulate the gut microbiome and immune responses, playing a crucial role in autoimmune disease. [ 89 , 90 ] In conclusion, the molecular docking results highlight promising interactions between these drugs and biomarkers, suggesting potential directions for future research aimed at developing targeted therapies for SLE. Despite the preliminary progress made in the identification and validation of TEX-related biomarkers in this study, several limitations should be acknowledged. First, our data were mainly derived from public databases such as GEO. Due to the inherent limitations of public datasets, we were unable to fully control for all potential confounding factors, such as ethnicity and disease stage. To more rigorously address these confounders, future studies could consider more refined sample stratification and matching during the data collection phase or employ more advanced statistical methods, such as multivariate regression analysis and propensity score matching, during the data analysis phase to further reduce the impact of confounding factors. Second, the retrospective nature of this study makes it difficult to directly elucidate the dynamic changes and causal relationships of gene expression in SLE. Future research should employ prospective study designs and incorporate experimental models to further investigate the causal relationship between gene expression and the pathogenesis of SLE. Third, although we performed initial validation of the differential expression of the identified biomarkers in SLE patients using RT-qPCR, the relatively small sample size restricts the statistical robustness and generalizability of the results. We plan to expand the validation cohort in subsequent studies and integrate multicenter clinical samples to enhance the reliability and broad applicability of our findings. Fourth, the construction of the molecular regulatory network and drug docking analyses in this study were primarily based on computational predictions, lacking functional experimental support; thus, the actual biological relevance of these mechanisms remains to be confirmed. Although some compounds, such as BPA and lipopolysaccharide, showed good binding affinities with key biomarkers, further validation in vitro, in animal models, or with clinical samples is required to evaluate their pharmacodynamics and safety. Moreover, due to experimental limitations, we were unable to perform protein-level validation such as a Western blot in the current study, although changes at the protein level are critical for confirming gene function. In future work, we will supplement these findings with Western blotting, immunohistochemistry, and other methods to comprehensively verify the expression and function of candidate biomarkers, thereby improving the biological interpretability of our results. In summary, although this study established a preliminary framework for identifying TEX-related biomarkers in SLE, further research involving larger sample sizes, functional validation, and multi-omics integration is needed to enhance the depth and clinical translational potential of these findings. 5. Conclusions This study utilized bioinformatics approaches and machine learning techniques, including LASSO and SVM-RFE, to identify 4 biomarkers ( IFIT3 , IFIH1 , CXCL10 , and STAT2 ) associated with TEX. Further analysis of the immune microenvironment and molecular regulatory networks enriched our understanding of the potential biological mechanisms underlying SLE, thereby providing support for the development of targeted therapeutic strategies. Acknowledgments We would like to express our sincere gratitude to all individuals and organizations who supported and assisted us throughout this research. Special thanks to the following authors: Wei Gao, Lulu Yang, Xingyu Liu. 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: Lanqing Yang. Formal analysis: Lanqing Yang. Investigation: Lanqing Yang, Wei Gao, Lulu Yang, Xingyu Liu. Methodology: Lanqing Yang. Project administration: Lanqing Yang, Wei Gao. Software: Lanqing Yang, Lulu Yang, Xingyu Liu. Writing – original draft: Lanqing Yang. Writing – review & editing: Lanqing Yang, Wei Gao, Lulu Yang, Xingyu Liu. medi-105-e49016-s001.xlsx (228.1KB, xlsx) medi-105-e49016-s002.xlsx (37.7KB, xlsx) medi-105-e49016-s003.xlsx (17.3KB, xlsx) Open in a new tab medi-105-e49016-s005.xlsx (9.6KB, xlsx) medi-105-e49016-s006.xlsx (9.5KB, xlsx) medi-105-e49016-s007.xlsx (54.4KB, xlsx) medi-105-e49016-s008.xlsx (10.2KB, xlsx) medi-105-e49016-s009.xlsx (60KB, xlsx) medi-105-e49016-s010.xlsx (14.4KB, xlsx) medi-105-e49016-s011.xlsx (180.9KB, xlsx) medi-105-e49016-s012.xlsx (164.7KB, xlsx) medi-105-e49016-s013.xlsx (257.6KB, xlsx) medi-105-e49016-s014.xlsx (224.8KB, xlsx) medi-105-e49016-s015.xlsx (10.3KB, xlsx) medi-105-e49016-s016.xlsx (12.4KB, xlsx) medi-105-e49016-s017.xlsx (11.7KB, xlsx) Abbreviations: AUC area under the curve BPA bisphenol A DEGs differentially expressed genes FC fold change GEO Gene Expression Omnibus GO gene ontology GSEA gene set enrichment analysis IFN interferon KEGG Kyoto Encyclopedia of Genes and Genomes LASSO Least Absolute Shrinkage and Selection Operator LN lupus nephritis miRNAs microRNAs NK natural killer PPI protein–protein interaction ROC receiver operating characteristic RT-qPCR reverse transcription-quantitative polymerase chain reaction SLE systemic lupus erythematosus SVM-RFE support vector machine-recursive feature elimination TEX T-cell exhaustion TEXRGs T-cell exhaustion-related genes TFs transcription factors The authors are solely responsible for the content of this article. The authors have no funding and conflicts of interest to disclose. This study was approved by the Medical Research Ethics Committee of the First Affiliated Hospital of Jinzhou Medical University (Approval Date: December 29, 2024; Approval Number: KYLL2024493). Written informed consent was obtained from all participants, and all procedures were conducted in accordance with the ethical standards of the Declaration of Helsinki. All data generated or analyzed during this study are included in this published article (and its supplementary information files). Supplemental Digital Content is available in the online version of this article ( http://dx.doi.org/10.1097/MD.0000000000049016 ). How to cite this article: Yang L, Gao W, Yang L, Liu X. Bioinformatics analysis identifies biomarkers associated with T-cell depletion in systemic lupus erythematosus. Medicine 2026;105:22(e49016). Contributor Information Lanqing Yang, Email: 1346672460@qq.com. Lulu Yang, Email: 1346672460@qq.com. 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