Screening method and application of systemic lupus erythematosus biomarker

Abstract

The invention discloses a screening method and application of a systemic lupus erythematosus biomarker, and the screening method comprises the following steps: 1) data acquisition and pretreatment, 2) differential expression gene (DEG) analysis, 3) function enrichment analysis, 4) protein-protein interaction (PP I) network construction, 5) subject working characteristic (ROC) curve analysis, 6) result verification, and 7) SEL biomarker determination. Wherein the biomarker is a hub gene FCER1A or RGS1, and the RGS1 gene is used for preparing a kit for detecting the RGS1 gene.

Description

Technical Field

[0001] The present invention relates to the fields of bioanalysis methodology and diagnostic technology, and in particular to a method for screening a biomarker for systemic lupus erythematosus and its application. Background Art

[0002] Systemic lupus erythematosus (SLE) is a chronic autoimmune disease characterized by multisystem involvement, which can affect multiple organs, including the skin, kidneys, joints, and central nervous system. SLE affects approximately 3.4 million people worldwide, primarily women of childbearing age, with a male-to-female prevalence ratio of approximately 10:13. In recent decades, the incidence of SLE has continued to rise. The global prevalence of SLE varies by region, ranging from approximately 20 to 150 cases per 100,000 people.

[0003] The pathogenesis of SLE is complex and involves the interaction of multiple factors such as genetics, epigenetics, environment and hormones (7,8). Its pathogenesis is related to changes in the entire immune system. Dysregulated T cell response is the key to SLE immunopathology. + T helper (Th) cells, particularly Th1, Th2, Th17, and regulatory T cells (Tregs), play a crucial role in disease progression. Imbalances in the Th1 / Th2 cytokine profile and increases in proinflammatory cytokines, such as interleukin-6 (IL-6), interleukin-17 (IL-17), and interferon-α (IFN-α), are closely associated with disease activity and organ damage. Furthermore, impaired Treg function exacerbates immune dysregulation and promotes the production of pathogenic autoantibodies. B cell hyperactivity and the subsequent overproduction of autoantibodies, such as anti-dsDNA and anti-Smith (Smith) antibodies, are hallmarks of SLE. These autoantibodies form immune complexes that deposit in various tissues, triggering complement activation, inflammation, and tissue damage. Furthermore, neutrophil extracellular traps (NETs) have been implicated in the pathogenesis of SLE, exacerbating exposure to autoantigens and persistently expressing type I interferons.

[0004] Despite significant advances in our understanding of the disease mechanisms of SLE, its early diagnosis and treatment remain challenging due to its diverse clinical presentation and unpredictable progression. Current treatment approaches primarily target immunosuppression and symptom relief; however, these strategies are often associated with significant side effects and inconsistent therapeutic responses. The diagnosis of SLE is primarily based on clinical manifestations and laboratory tests. The American College of Rheumatology (ACR) and the Systemic Lupus Erythematosus International Collaborating Clinics (SLICC) have developed classification criteria that incorporate clinical manifestations (eg, rash, joint involvement, renal dysfunction) and serological markers, including antinuclear antibodies (ANA), anti-double-stranded DNA antibodies, Sm antibodies, and decreased complement levels. Other diagnostic methods include urinalysis to assess for renal involvement and skin biopsy in cases of cutaneous SLE.

[0005] However, these diagnostic methods are not without limitations. First, the clinical manifestations of SLE are highly heterogeneous and may overlap with those of other autoimmune diseases, making early diagnosis challenging. For example, symptoms such as fatigue, arthralgia, and rash are not unique to SLE and may also occur in a variety of other diseases, leading to delayed recognition and intervention. Second, the presence of autoantibodies, such as antinuclear antibodies (ANA), lacks specificity for systemic lupus erythematosus (SLE), as these antibodies may also be present in other autoimmune diseases and even in healthy individuals. In addition, some SLE patients may test negative for certain autoantibodies, complicating the diagnostic process. Finally, these diagnostic methods often fail to adequately capture disease activity or predict organ damage, limiting their effectiveness in monitoring disease progression and guiding treatment strategies. Traditional diagnostic methods are time-consuming and cumbersome, hindering their application in timely diagnosis, treatment, and prognosis of patients.

[0006] Genetic testing is increasingly becoming a key tool in the diagnosis and treatment of systemic lupus erythematosus (SLE), significantly improving the accuracy and efficiency of diagnostic and treatment strategies. By identifying genetic biomarkers associated with SLE, these tests facilitate earlier and more accurate diagnosis, enabling timely intervention and optimized patient management. Consequently, genetic screening has become an essential component of clinical practice, not only improving diagnostic accuracy but also paving the way for more personalized treatment approaches. Identifying disease-associated genes through genetic testing and validating corresponding biomarkers using techniques such as flow cytometry allows for faster and more effective diagnosis and treatment of SLE, leading to the development of more targeted treatments. This, in turn, improves short-term clinical outcomes and long-term disease control. Summary of the Invention

[0007] The present invention aims to provide a screening method and application for biomarkers of systemic lupus erythematosus. The screening method combines comprehensive bioinformatics analysis to explore genes, biomarkers, and molecular processes related to SLE, and verifies them through clinical data to identify markers for better diagnosis of SLE.

[0008] To achieve the purpose of the present invention, the following embodiments are provided.

[0009] In some embodiments, a method for screening biomarkers for systemic lupus erythematosus of the present invention comprises the following steps:

[0010] 1) Data acquisition and preprocessing: Two datasets, GSE13887 and GSE10325, were downloaded from the GEO database. These datasets contain gene expression data of healthy controls (HCs) and SLE patients.

[0011] 2) Differentially expressed gene (DEG) analysis: Bioinformatics tools were used to identify DEGs in the two datasets in the previous step, and DEGs with significant common differential expression in the two datasets were screened;

[0012] 3) Functional enrichment analysis: Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were performed on the screened DEGs to reveal the biological processes and pathways in which they are involved;

[0013] 4) Protein-protein interaction (PPI) network construction: Use the information obtained in the previous step to construct a PPI network and identify key hub genes;

[0014] 5) Receiver operating characteristic (ROC) curve analysis: to evaluate the diagnostic efficacy of key hub genes;

[0015] 6) Validation: Validate the expression of key hub genes in peripheral blood samples from SLE patients using quantitative real-time PCR (RT-PCR) and flow cytometry;

[0016] 7) Determination of biomarkers: Determine biomarkers by analyzing the expression of hub genes in peripheral blood samples of SEL patients, wherein the biomarkers are hub genes FCER1A or RGS1.

[0017] In the above screening method, in step 2), the bioinformatics tool is R software.

[0018] The above screening method, DEG analysis in step 2), further includes DEGs co-identified in the GSE13887 dataset and DEGs detected in the GSE10325 dataset, performing hierarchical cluster analysis on these DEGs to determine the shared DEGs.

[0019] In the above screening method, in step 3), the functional enrichment analysis includes pathway enrichment analysis using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) to reveal the functions and effects of common differentially expressed genes of shared DEGs and determine significantly enriched KEGG pathway information.

[0020] In the above screening method, in step 4), seven key genes were identified through the PPI network: GZMB, LAG3, APOL1, CXCL13, RGS1, FCER1A and DPEP2. The expression levels of these genes in the GSE13887 and GSE10325 datasets were collected, and volcano plot evaluation was performed to confirm that the FCER1A gene and the RGS1 gene showed consistent expression trends in the two datasets, among which FCER1A was downregulated and RGS1 was upregulated.

[0021] In the above screening method, step 5), the ROC curve analysis includes collecting the expression levels of the FCER1A gene and the RGS1 gene in the two data sets GSE13887 and GSE10325, generating a receiver operating characteristic (ROC) curve, and evaluating the diagnostic efficacy of FCER1A and RGS1 alone and in combination, showing a strong signature discrimination ability.

[0022] The above screening method, step 6) verification, includes collecting peripheral blood samples from SEL patients, isolating CD3 + T cells, RNA was extracted and quantitative real-time PCR (RT-PCR) analysis was performed. Compared with HC, FCER1A was significantly downregulated, while RGS1 was significantly upregulated in SLE patient samples.

[0023] Preferably, in the above screening method, the marker is the hub gene RGS1.

[0024] Another object of the present invention is to provide a use of the hub gene RGS1 in preparing a kit for diagnosing systemic lupus erythematosus, wherein the kit can detect the expression level of RGS1.

[0025] Another object of the present invention is to provide a detection kit for diagnosing systemic lupus erythematosus, wherein the reagent can identify the expression of the new gene RGS1.

[0026] Technical effect:

[0027] Through comprehensive bioinformatics analysis and experimental validation, the present invention successfully identified FCER1A and RGS1 as potential diagnostic markers for SLE. Compared with existing technologies, the present invention has the following advantages:

[0028] 1. High sensitivity and specificity: FCER1A and RGS1 showed significant differential expression in SLE patients, and ROC curve analysis showed high diagnostic efficacy.

[0029] 2. Multi-dataset validation: This invention is validated based on two independent GEO datasets, which improves the reliability and universality of the results.

[0030] 3. Functional validation: The expression changes of FCER1A and RGS1 in SLE patients were verified by RT-PCR and flow cytometry experiments, further confirming their potential as biomarkers.

[0031] 4. Clinical application prospects: FCER1A and RGS1 can not only serve as diagnostic markers for SLE, but may also become new targets for future therapeutic interventions, and have broad clinical application prospects. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1 Volcano plots and heat maps are generated for differentially expressed genes in the GSE13887 and GSE10325 datasets, where A is the volcano plot of the GSE13887 dataset, the x-axis represents log2 (fold change), and the y-axis represents -log 10 (P value), red dots represent upregulated genes, and blue dots represent downregulated genes; B is the volcano plot of the GSE10325 dataset; C is the heat map of the top 20 upregulated and downregulated EP DEGs in the GSE13887 dataset, each row represents a gene, each column represents a sample, red indicates high expression level, and blue indicates low expression level; D is the heat map of the GSE10325 dataset; E is the box plot of the GSE13887 dataset, and there is no significant difference in the median and upper and lower quartiles; F is the box plot of the GSE10325 dataset; G is the Venn diagram of common DEGs in the GSE13887 and GSE10325 datasets.

[0033] Figure 2 Bubble plots of GO and KEGG pathway enrichment analysis results. The size of the ball represents the number of genes, and the color change of the ball corresponds to different p-values. Among them, A is a bubble plot showing the top five processes enriched in EP DEGs in BP, CC and MF, and B is a bubble plot showing the top five significantly enriched KEGG pathways in DEGs (co-DEGs).

[0034] Figure 3 Functional enrichment analysis of DEGs, where A is a bar graph of the DEGs GO enrichment analysis results, the x-axis represents the GO term, and the y-axis represents the -log of each GO term. 10(p value); B is a bar graph of the KEGG pathway enrichment analysis results of DEGs; CE is a GO chord diagram, red indicates upregulation, blue indicates downregulation, and the depth of the color indicates the magnitude of the change. The chord diagram shows that EP-DEGs are enriched in the top five GO categories of BPs, CCs, and MFs, respectively.

[0035] Figure 4 The constructed hub gene-protein interaction (PPI) network diagram.

[0036] Figure 5 Figure 2: Identification of hub genes in the PPI network and diagrams of FCER1A and RGS1. A is the functional enrichment analysis diagram of hub gene identification. Four hub genes were discovered using CytoHubba in Cytoscape. B is the top ten hub genes in the FCER1A PPI network explored by CytoHubba in Cytoscape. C is the top ten hub genes in the RGS1 PPI network explored by CytoHubba in Cytoscape.

[0037] Figure 6 These are the gene validation plots in the GSE10325 and GSE13887 datasets, where AG is the volcano plot of hub genes expressed in the GSE10325 and GSE13887 datasets, and H is the volcano plot of FCER1A and RGS1, showing genes with consistent expression levels in GSE10325 and GSE13887.

[0038] Figure 7 Figure 3 is the ROC curve of the Hub gene, where A is the ROC curve analysis of the FCER1A and RGS1 hub genes in the GSE13887 dataset; B is the ROC curve analysis of the FCER1A and RGS1 hub genes in the GSE10325 dataset; and C is the combined ROC curve analysis of the FCER1A and RGS1 genes in the GSE13887 and GSE10325 datasets.

[0039] Figure 8 Schematic diagram of the construction of the FCER1A and RGS1 protein interaction (PPI) network.

[0040] Figure 9 Figure 2 PCA plots of two genes, FCER1A and RGS1. PC1 and PC2 are the first and second principal components, respectively, explaining the different expressions of potential variants. These plots represent different samples, with the SLE group in red and the control group in blue.

[0041] Figure 10To validate the FCER1A and RGS1 genes in other datasets, (A) and (B) show receiver operating characteristic (ROC) curve analyses of FCER1A and RGS1 in the GSE61635 dataset. (C) and (D) show comparisons of FCER1A and RGS1 expression in patients with systemic lupus erythematosus and hepatocellular carcinoma. The two groups were compared using the Mann-Whitney test, with p < 0.05 (*), p < 0.01 (**), p > 0.001 (***), and p ≤ 0.0001 (****), ns (not significant).

[0042] Figure 11 CD3 + Verification of FCER1A and RGS1 in T cells and detection results of inflammatory factors in clinical specimens and plasma, where A is the CD3 + Figure 2 is the RT-qPCR results of FCER1A and RGS1 in T cells. B is the flow cytometry gate diagram. C is the CD3 + Figure 4 shows a comparison of FCER1A and RGS1 protein levels in T cells. Figure 5 (D) shows a comparison of IL-6, TNF-α, and IL-10 levels in the plasma of SLE patients and healthy controls. Figure 5 (E) shows a correlation analysis between FCER1A and RGS1 protein levels and SLEDI scores in SLE patients. The two groups were compared using the Mann-Whitney test, with p < 0.05 (*), p < 0.01 (**), p > 0.001 (***), and p ≤ 0.0001 (***). DETAILED DESCRIPTION

[0043] The following examples are provided to describe the present invention in more detail. However, the following examples are provided only to help further understand the present invention and are not intended to limit the present invention. Those skilled in the art will appreciate that any equivalent substitutions made to the present invention, or corresponding improvements, still fall within the scope of protection of the present invention.

[0044] Example 1 Screening of SEL markers

[0045] 1. Screening Method

[0046] 1.1 Research subjects and sample collection

[0047] From January to December 2024, 32 SLE patients and 24 healthy controls (HC) were collected from Nanfang Hospital of Southern Medical University. All SLE patients met the 2012 ACR SLE disease classification criteria. All SLE patients with concurrent infection, acute intercurrent illness, and use of probiotics or antibiotics within 1 month before admission were excluded. The collected HC also needed to have no known history of autoimmune disease and be of comparable age to the SLE patients. 2 mL EDTA-K2 blood samples were collected from all participants.

[0048] The ethics committee of Nanfang Hospital of Southern Medical University approved the study with the ethics number: NFEC-2025-026. All methods used were in accordance with the approved guidelines, and written informed consent was required from all patients and healthy volunteers in the study.

[0049] SLE patients and HCs were comparable in age and sex (Table 1). The mean age of the SLE and HC groups was 37.81 ± 13.40 years and 38.75 ± 10.29 years, respectively (p = 0.734). Table 1 summarizes the patient's basic information, including WBC, white blood cell count; LYM, lymphocyte count; NEU, neutrophil count; RBC, red blood cell count; HGB, hemoglobin; HCT, hematocrit; PLT, platelet count; and SLE disease activity index.

[0050] Table 1. Characteristics of SLE patients and healthy controls

[0051]

[0052]

[0053] 1 P<0.05. 2 P<0.01. 3 P<0.001.

[0054] 1.2 Selecting a Dataset

[0055] Two original datasets, GSE13887 and GSE10325, were downloaded from the Gene Expression Omnibus (GEO) database (https: / / www.ncbi.nlm.nih.gov / geo / ). GSE13887 was an analysis of CD3-positive T cells isolated from 10 SLE patients and 9 HCs. GSE10325 was an analysis of freshly isolated lymphocytes (CD4 T cells) from the blood of 28 HCs and 39 SLE patients. + T cells and CD19 + B cells) and CD33 + Analysis of myeloid subsets.

[0056] 1.3 Identification of differentially expressed genes (DEGs)

[0057] The results of differential analysis were visualized using the ggplot2 (version 3.4.4) package in R software (version 4.2.1), and p values ​​were analyzed to correct for false positive results. The thresholds for plotting volcano plots were defined as p ≤ 0.05 and |log2FC| ≥ 1. Heat maps were visualized using the ComplexHeatmap (version 2.13.1) package in R software.

[0058] The two datasets were normalized, and the cross-comparability assessment was visualized by boxplots using the ggplot2 package in R. The unique and common components of each group were analyzed, and the results were visualized using the ggplot2 package and the VennDiagram package (version 1.7.3) to draw a Venn diagram.

[0059] 1.4 Gene Ontology (GO) term and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis

[0060] The R language package clusterProfiler (version 4.4.4) and ggplot2 were used to perform GO term and KEGG pathway enrichment analysis on common DEGs (co-DEGs) and visualize the results. The threshold was p value < 0.05, and bar charts, bubble charts, and chord charts were drawn.

[0061] 1.5 Protein-protein interaction (PPI) network construction and hub gene identification

[0062] The STRING online database (https: / / www.string-db.org / ) was used to construct a PPI network of DEGs that may play an important role in the progression of SLE. Cytoscape (version 3.10.2) (www.cytoscape.org / ) was used for PPI network construction to better visualize interaction information. Hub genes with a connectivity ≥ 10 were screened using CytoHubba (version 0.1).

[0063] 1.6 Hub Gene Verification

[0064] Receiver operating characteristic (ROC) analysis was performed on the data using the R package pROC (version 1.18.0). The results were visualized using ggplot2. ROC curves for hub genes were plotted, and the area under the ROC curve (AUC) for hub genes was used to assess their ability to distinguish between SLE and HC. Principal component analysis (PCA) was performed using the expression profiles of hub genes in the two data sets as variables to determine PC1 and PC2, and the results were visualized using ggplot2.

[0065] 1.7 Isolation of CD3 from SLE and HC Peripheral Blood Samples + T cells

[0066] Human primary PBMCs were isolated from peripheral blood using Ficoll density gradient centrifugation with EDTA-K2. CD3 + T cell isolation kit (130-097-043, Miltenyi Biotec, Bergisch Gladbach, Germany) was used to enrich 1 × 10 6 CD3 + T cells. The purity of T cells was analyzed by flow cytometry (Cantoll, BD Bioscience) using anti-CD3-PE antibody (562310, clone: ​​HIT3A, BD Bioscience).

[0067] 1.8 Real-time quantitative polymerase chain reaction (RT-qPCR)

[0068] The sorted CD3 + T cells. RNA was isolated by chloroform substitution (Cat.#G3014, Servicebio, Wuhan, China), and then precipitated with isopropanol at 4°C, 15,000 g, for 15 minutes. The RNA was then rinsed twice with 75% ethanol. cDNA was synthesized using the SweScript RT I First Strand cDNA Synthesis Kit (Cat.#G331-1, Servicebio, Wuhan, China) with a Random Hexamer Primer (Cat.#G331-4, Servicebio, Wuhan, China). 1 μg of RNA template was added.

[0069] qPCR was performed using Servicebio 2×SYBR Green qPCR Master Mix (Cat.#G3326-1, Servicebio, Wuhan, China). The reaction volume was 20 μL per well. The cDNA template was diluted 10-fold with DEPC water, and 2 μL was added to each well. 0.4 μL of each forward and reverse primer (see Table 2) was added. qPCR was performed using a Roche cobas 4800 PCR instrument. This experiment used a two-step protocol: initial denaturation at 95°C for 2-5 minutes; cyclic amplification (35-45 cycles): denaturation at 95°C for 5-15 seconds; and annealing / extension at 60-68°C for 20-60 seconds (simultaneous fluorescence acquisition, SYBR Green endpoint acquisition, and TaqMan real-time acquisition). A melting curve (SYBR Green) was generated by gradually increasing the temperature from 65°C to 95°C, and fluorescence was monitored. Relative gene expression was calculated using the 2-ΔΔCt method, and the results were normalized to GAPDH.

[0070] Table 2. Primer gene sequences

[0071] Primer name Sequence (5'-3') GAPDH-FOR CTCATGACCACAGTCCATGC GAPDH-REV TTCAGCTCTGGGATGACCTT FCER1A-FOR GTTCTTCGCTCCAGATGGC FCER1A-REV TTGTGGAACCATTTGGTGGAA RGS1-FOR GGCGCAGTCTTTTGACAAGC RGS1-REV GCCTTCTCGTCTACCACATGC

[0072] 1.9 Flow cytometry

[0073] Flow cytometry was used to detect the expression of CD3 in plasma of HCs and SLE patients. + T cell FCER1A and RGS1 protein expression levels and plasma inflammatory cytokine levels were measured. Samples were analyzed using a BD FACS Fortassa flow cytometer (BD Biosciences, USA). Data were acquired using BD FACSDiva software, with at least 10,000 events recorded for each sample. A polyclonal rabbit anti-human RGS1 antibody (Cat#LS-C162570-400; LSBio, Seattle, Washington, USA) and a FITC-conjugated goat anti-rabbit secondary antibody were used for RGS1 analysis. A polyclonal rabbit anti-human RGS1 antibody (Cat#LS-C162570-400; LSBio, Seattle, Washington, USA) and a FITC-conjugated goat anti-rabbit secondary antibody were used for RGS1 analysis. A polyclonal rabbit anti-FCER1 alpha monoclonal antibody (Cat#16-5899-82, functional grade, eBioscience) was used for FCER1A analysis.

[0074] 1.10 Statistics

[0075] All data processing and analyses were performed in Graphpad Prism 9.0. To compare variables between two groups, the Mann-Whitney test was applied. Statistical significance between two variable datasets was analyzed using Fisher's exact test. A p value < 0.05 was considered statistically significant. Statistical significance is reported as follows: p < 0.05 (*), p < 0.01 (**), p < 0.001 (***), p < 0.0001 (****), and ns (not significant). Data conformed to normality. Pearson correlation was used to analyze the correlation between FCER1A and RGS1 proteins and SLEDI scores. The correlation coefficient was used to assess the magnitude of the association. 0.8 ≤ |r| < 1 (strong correlation), 0.5 ≤ |r| < 0.8 (moderate correlation), 0.3 ≤ |r| < 0.5 (weak correlation), and 0 ≤ |r| < 0.3 (no correlation).

[0076] 2. Results

[0077] 2.1 Identification of DEGs

[0078] Bioinformatics analysis was performed using R software (version 4.2.1). A total of 1,912 differentially expressed genes (DEGs) were identified in the GSE13887 dataset, while 52 DEGs were detected in the GSE10325 dataset. Hierarchical clustering analysis was then performed on these DEGs to prioritize the top 20 up-regulated and down-regulated genes in GSE13887 (see Table 3) and GSE10325 (see Table 4), respectively. Volcano plots and heat maps were generated to visualize the differences in GSE13887 ( Figure 1 A, C) and GSE10325 ( Figure 1 Clustering results of B and D). The heat map clearly shows that the sample clusters are highly consistent.

[0079] Table 3. 20 up-regulated and down-regulated genes in the GSE13887 dataset

[0080]

[0081]

[0082] Table 4. Top 20 up-regulated and down-regulated genes in the GSE10325 dataset

[0083]

[0084]

[0085] After data normalization and comparative evaluation of the datasets, boxplots were drawn to confirm that GSE13887 ( Figure 1 E) and GSE10325( Figure 1The results showed that the data distribution of the two datasets met the established quality standards, confirming the robustness and cross-comparability of the microarray data. A Venn diagram ( Figure 1 G) To illustrate the overlap of DEGs between the two datasets, and eight shared DEGs were identified.

[0086] 2.2 DEG enrichment analysis

[0087] Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis was performed to elucidate the functional roles of eight common differentially expressed genes (co-DEGs). GO terms were divided into biological process (BP), cellular component (CC), and molecular function (MF), and the top five most significantly enriched terms in each category were selected based on the lowest p-value (see Table 5). These results are shown in the bubble plots of the GO and KEGG pathway enrichment analysis results (see Figure 2 ) and bar charts ( Figure 3 A) Perform visualization. Figure 2 The bubble plot of A shows the top 5 processes enriched with EP DEGs in BP, CC, and MF. Figure 2 The bubble plot in Figure B shows the top five significantly enriched KEGG pathways in DEGs (co-DEGs). The size of the ball represents the number of genes, and the color change of the ball corresponds to different p-values. The co-DEGs are mainly related to processes such as cell killing, leukocyte-mediated cytotoxicity, natural killer cell-mediated immunity, and natural killer cell-mediated cytotoxicity. Subsequently, the five most significantly enriched KEGG pathways were identified (as shown in Table 6) and displayed by bar graphs ( Figure 3 B) and bubble charts ( Figure 2 B) The co-DEGs were mainly involved in pathways related to transcriptional dysregulation in cancer, apoptosis, thyroid cancer, African trypanosomiasis, and asthma.

[0088] Table 5. Top 5 GO terms in each category of DEGs between controls and SLE

[0089]

[0090] Table 6. Top 5 KEGG pathways of DEGs between controls and SLE

[0091]

[0092]

[0093] 2.3PPI network and hub genes

[0094] Chord plots were generated to illustrate the enrichment of genes within the top five GO categories in BP, CC, and MF, and seven key genes were identified: GZMB, LAG3, APOL1, CXCL13, RGS1, FCER1A, and DPEP2. Notably, RGS1, CXCL13, and GZMB were enriched in at least two GO categories ( Figure 3 DEGs were uploaded to the STRING database to construct protein-protein interaction (PPI) networks (see Figure 4 Using the MCODE plugin, we identified a tightly interconnected protein cluster consisting of CXCL13, GZMB, LAG3, and FCER1A ( Figure 5 A), MCODE analysis identified a highly interconnected protein cluster consisting of ten genes ( Figure 5 B, C).

[0095] Overall, based on the PPI network and chord diagram analysis, we selected seven hub genes - GZMB, LAG3, APOL1, CXCL13, RGS1, FCER1A, and DPEP2. Volcano plot evaluation of their expression levels in GSE13887 and GSE10325 datasets showed that only FCER1A and RGS1 showed consistent expression trends in the two datasets, with FCER1A downregulated and RGS1 upregulated (see Figure 6 ), represents the gene validation in GSE10325 and GSE13887 datasets, where (AG) is the volcano plot of the hub genes expressed in GSE10325 and GSE13887 datasets, and (H) is the volcano plot of FCER1A and RGS1, which are genes with consistent expression levels in GSE10325 and GSE13887. These two genes were further analyzed using the STRING database to construct another PPI network (see Figure 8 A and B) and visualized in Cytoscape, where A is the PPI network diagram of FCER1A and B is the PPI network diagram of RGS1.

[0096] 2.4 Prognostic value of hub genes

[0097] Using the expression levels in GSE13887 and GSE10325 datasets, receiver operating characteristic (ROC) curves were generated to evaluate the diagnostic efficacy of FCER1A and RGS1 alone and in combination ( Figure 7 Both genes showed strong diagnostic potential, with area under the curve (AUC) values ​​exceeding 0.7. Combined analysis of these two genes showed superior diagnostic performance.

[0098] Principal component analysis (PCA) was performed to further validate their discriminative ability. PCA plots were obtained from the dimensionality reduction of gene expression data from healthy controls (HC) and SLE patients. Based on the expression profiles of FCER1A and RGS1, there was a clear separation between the two groups ( Figure 9 The first two principal components (PC1 and PC2) explained 100% of the explained variance. SLE samples were mainly clustered on the negative axis of PC1, while HC were located on the positive axis, indicating that there were significant differences in gene expression patterns between the groups and highlighting the strong discriminatory power of these two genes.

[0099] 2.5 Validation in other datasets and clinical samples

[0100] To further confirm these findings, the expression of FCER1A and RGS1 was validated using the GSE61635 dataset, confirming their diagnostic efficacy. The expression trends observed in GSE61635 were consistent with those in GSE13887, GSE10325, and clinical samples, with FCER1A downregulated and RGS1 upregulated in SLE patients compared with HCs ( Figure 10 ).

[0101] Peripheral blood samples were collected and CD3 + T cells. Quantitative real-time PCR (RT-PCR) after RNA extraction showed that FCER1A was significantly downregulated (p<0.0001) and RGS1 was significantly upregulated (p<0.0001) in SLE patient samples compared with HC ( Figure 11 A). These findings were confirmed by flow cytometry, which showed that FCER1A protein levels were significantly decreased (p<0.01) and RGS1 protein expression was significantly increased (p<0.001) in the plasma of SLE patients ( Figure 11 B, C).

[0102] In addition, flow cytometry analysis of inflammatory cytokines showed that compared with HC, the levels of IL-6 and TNF-α in the plasma of SLE patients were increased (p<0.01), while IL-10 was significantly decreased (p<0.001) ( Figure 11D). These results further support the potential of FCER1A and RGS1 as powerful biomarkers for diagnosing and understanding the pathophysiology of SLE. At the same time, we further studied the specificity of FCER1A and RGS1 in diagnosing SLE disease. By analyzing the correlation between FCER1A protein and RGS1 protein and SLEDI score in SLE patients, we found that there was a negative correlation between FCER1A protein expression and SLEDI score in SLE patients, and the correlation coefficient r = -0.8434, indicating that the negative correlation between the two was strong; there was a positive correlation between RGS1 protein expression and SLEDI score in SLE patients, and the correlation coefficient r = 0.8394, indicating that the positive correlation between the two was strong ( Figure 11 E).

[0103] Discussion of experimental results

[0104] Systemic lupus erythematosus (SLE) is a multifactorial autoimmune disease characterized by widespread inflammation and immune-mediated damage across multiple organ systems, with a significantly higher prevalence in women. The pathogenic mechanisms of SLE are complex and interdependent, including T cell dysregulation, B cell hyperactivity, and elevated proinflammatory cytokines. The clinical manifestations of SLE are diverse, and its progression trajectory is unpredictable, complicating early diagnosis and therapeutic intervention. Therefore, identifying biomarkers, genes, or signaling pathways associated with SLE is crucial for improving diagnostic accuracy, therapeutic strategies, and prognostic assessment.

[0105] The screening of the present invention used bioinformatics tools to analyze the GSE13887 and GSE10325 datasets related to SLE and identified eight common DEGs. Functional enrichment analysis was then performed. Among them, seven hub genes - FCER1A, RGS1, CXCL13, DPEP2, LAG3, APOL1 and GZMB - were extracted by constructing a PPI network, which was subsequently validated in peripheral blood samples of SLE patients by RT-PCR and flow cytometry. After evaluating the expression levels of genes in the two datasets, we identified two genes, FCER1A and RGS1, whose expression trends were consistent in both the datasets and clinical samples. FCER1A is a high-affinity IgE receptor that is significantly downregulated in SLE patients, indicating that antigen presentation and immune regulation may be impaired. In contrast, RGS1 is a G protein signaling regulator that is significantly upregulated, and its expression is associated with T cell migration and immune response regulation. Functional enrichment analysis showed that DEGs were significantly associated with biological processes such as “leukocyte-mediated cytotoxicity,” “natural killer cell-mediated immunity,” and “apoptosis.” KEGG pathway analysis highlighted the enrichment of pathways related to “cancer transcriptional dysregulation,” “apoptosis,” and “thyroid cancer pathway,” emphasizing the role of immune dysregulation and apoptosis in the pathogenesis of SLE. Existing studies have confirmed that inflammation, overactivation of immune pathways, and impaired apoptosis mechanisms are central to the pathogenesis of SLE. Our findings are consistent with existing reports, highlighting the key role of hub genes identified in these pathological processes. Among the identified hub genes, FCER1A plays a key role in immune regulation and is involved in IgE-mediated signaling, while RGS1 regulates G protein-coupled receptor signaling pathways, affecting immune cell migration and function. Similarly, CXCL13 is known to regulate B cell trafficking and follicular helper T cell responses, both of which contribute to the pathogenesis of SLE.

[0106] In summary, our bioinformatics analysis identified key hub genes, including FCER1A and RGS1, as potential biomarkers and therapeutic targets for SLE. This research continues to reveal the complexity of SLE. FCER1A and RGS1 genes can be used as SLE markers, enabling the development of diagnostic kits to detect FCER1A and / or RGS1 gene expression for diagnosis and prognosis of SLE. RGS1, in particular, is particularly well-suited as a biomarker for the diagnosis of SLE, paving the way for future improvements in diagnostic tools, personalized treatments, and better outcomes for SLE patients.

Claims

1. A method for screening biomarkers of systemic lupus erythematosus, comprising the following steps: 1) Data acquisition and preprocessing: Two datasets, GSE13887 and GSE10325, were downloaded from the GEO database. These datasets contain gene expression data of healthy controls (HCs) and SLE patients. 2) Differentially expressed gene (DEG) analysis: Bioinformatics tools were used to identify the DEGs in the two datasets in the previous step, and the DEGs with significant common differential expression in the two datasets were screened; 3) Functional enrichment analysis: Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were performed on the screened DEGs to reveal the biological processes and pathways in which they are involved; 4) Protein-protein interaction (PPI) network construction: Use the information obtained in the previous step to construct a PPI network and identify key hub genes; 5) Receiver operating characteristic (ROC) curve analysis: to evaluate the diagnostic efficacy of key hub genes; 6) Validation: Validate the expression of key hub genes in peripheral blood samples from SLE patients using quantitative real-time PCR (RT-PCR) and flow cytometry; 7) Determination of biomarkers: The markers are determined by expression in peripheral blood samples, and the markers are the hub genes FCER1A or RGS1.

2. The screening method according to claim 1, wherein in step 2), the bioinformatics tool is R software.

3. The screening method according to claim 1, in step 2), the DEG analysis further comprises identifying 1912 DEGs in the GSE13887 dataset and detecting 52 DEGs in the GSE10325 dataset, performing hierarchical cluster analysis on these DEGs, and identifying eight shared DEGs.

4. The screening method according to claim 1, wherein in step 3), the functional enrichment analysis comprises performing pathway enrichment analysis using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) to reveal the functions and effects of the eight common differentially expressed genes and identify five significantly enriched KEGG pathway information.

5. The screening method according to claim 1, in step 4), seven key genes were identified through the PPI network: GZMB, LAG3, APOL1, CXCL13, RGS1, FCER1A, and DPEP2. The expression levels of these genes in the GSE13887 and GSE10325 datasets were collected, and volcano plot evaluation was performed to confirm that the FCER1A gene and the RGS1 gene showed consistent expression trends in the two datasets. FCER1A was downregulated, whereas RGS1 was upregulated.

6. The screening method according to claim 1, in step 5), the ROC curve analysis comprises collecting the expression levels of the FCER1A gene and the RGS1 gene in two datasets, GSE13887 and GSE10325, generating a receiver operating characteristic (ROC) curve, and evaluating the diagnostic efficacy of FCER1A and RGS1 alone and in combination, showing a strong signature discrimination ability.

7. The screening method according to claim 1, wherein in step 6), the verification comprises collecting peripheral blood samples from SEL patients, isolating CD3 + T cells, RNA was extracted and quantitative real-time PCR (RT-PCR) analysis was performed. Compared with HC, FCER1A was significantly downregulated, while RGS1 was significantly upregulated in SLE patient samples.

8. The screening method according to claim 1, wherein in step 7), the marker is the hub gene RGS1.

9. Use of a hub gene RGS1 in preparing a kit for diagnosing systemic lupus erythematosus, wherein the kit can detect the expression level of RGS1.

10. A detection kit for diagnosing systemic lupus erythematosus, wherein the reagent can identify the expression of the new gene RGS1.

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