A biomarker for diagnosing systemic lupus erythematosus complicated with cardiovascular disease and its application

Abstract

The present invention discloses a biomarker for diagnosing cardiovascular disease complicated by systemic lupus erythematosus and its application, belonging to the field of biomedical technology. The biomarker includes CD163, IL1B, IL1RN, MMP9 and / or NFKBIA. The present invention uses bioinformatics analysis to determine that the core shared genes involved in the common mechanism of SLE and CVD are CD163, IL1B, IL1RN, MMP9 and NFKBIA. These five genes are used as biomarkers for diagnosing cardiovascular disease complicated by systemic lupus erythematosus, and have good diagnostic efficacy, and can achieve the purpose of early diagnosis of cardiovascular disease complicated by systemic lupus erythematosus. The present invention determines for the first time that these five genes are potential biomarkers for the diagnosis and treatment targets of SLE complicated with CVD, providing an important basis for the improvement of SLE patients, and providing new targets for the clinical diagnosis and treatment of SLE complicated with CVD.

Description

Technical Field

[0001] The present invention relates to the field of biomedical technology, and in particular to a biomarker for diagnosing systemic lupus erythematosus complicated with cardiovascular disease and application thereof. Background Art

[0002] Systemic lupus erythematosus (SLE) is a chronic inflammatory autoimmune disease that affects multiple organ systems and is characterized by the presence of numerous autoantibodies. The disease primarily affects women of childbearing age, with a male to female ratio of 1:9. The global prevalence of SLE in adults ranges from 30 to 150 cases per 100,000 population, and the annual incidence ranges from 2.2 to 23.1 cases per 100,000 population. Although the exact cause of SLE remains unclear, it is thought that immune dysfunction may result from a combination of genetic, environmental, and hormonal factors.

[0003] Despite significant advances in the diagnosis and treatment of SLE, life expectancy among patients remains lower than that of the general population. This highlights the continued unmet needs of lupus patients, which have evolved over time. Notably, the incidence of cardiovascular disease (CVD) is increased among SLE patients, which has a significant impact on the already reduced quality of life and survival of these individuals. Therefore, effective management and prevention of CVD in SLE patients is critical to improving overall health outcomes.

[0004] Several studies have shown that the risk of subclinical atherosclerosis in SLE is comparable to other high-risk CVD diseases such as diabetes and rheumatoid arthritis. Atherosclerosis is the main pathological mechanism of SLE-associated CVD, and it is estimated that SLE patients have a 2- to 10-fold higher risk of CVD than the general population. A recent meta-analysis conducted between 2013 and 2020 reported that the CVD risk of SLE ranged from 1.95 to 2.84, supporting these findings. Importantly, women with SLE under the age of 55 years have a 5- to 8-fold increased risk of coronary heart disease compared with the general population. Manzi et al. even observed that women aged 35-44 years had a 50-fold increased risk of myocardial infarction compared with age-matched subjects. The elevated risk of CVD in SLE cannot be attributed solely to traditional cardiovascular risk factors, suggesting that other factors are involved in the pathogenesis. Disease-related factors such as chronic inflammation, immune dysfunction, and SLE treatment medications have been implicated as potential contributors to the increased risk. In addition, genetics may play a role in the shared genetic risk factors for SLE and CVD. However, the common genetic risk factors for SLE and CVD are unclear, as few studies have investigated this relationship at the genetic level. Therefore, the common genetic risk factors for CVD in the context of SLE remain unclear.

[0005] Further research using gene microarray technology can better understand the underlying genetic mechanisms and develop personalized prevention and treatment strategies. Systemic lupus erythematosus (SLE) CVD has a high incidence, insidious onset, and poor prognosis. Early screening, diagnosis, and effective treatment are crucial. Summary of the invention

[0006] The purpose of the present invention is to provide a biomarker for diagnosing systemic lupus erythematosus complicated with cardiovascular disease and its application to solve the problems existing in the above-mentioned prior art. The present invention first determines that CD163, IL1B, IL1RN, MMP9 and NFKBIA are potential biomarkers for the diagnosis and treatment of SLE complicated with CVD, which provides an important basis for the improvement of SLE patients and provides a new target for the clinical diagnosis and treatment of SLE complicated with CVD.

[0007] To achieve the above object, the present invention provides the following solutions:

[0008] The present invention provides a biomarker for diagnosing systemic lupus erythematosus complicated with cardiovascular disease, wherein the biomarker comprises CD163, IL1B, IL1RN, MMP9 and / or NFKBIA.

[0009] The present invention also provides the use of a reagent for detecting the expression level of the above biomarker in the preparation of a diagnostic product for systemic lupus erythematosus complicated with cardiovascular disease.

[0010] Furthermore, the product is a kit or a reagent.

[0011] The present invention also provides a product for diagnosing systemic lupus erythematosus, cardiovascular disease or systemic lupus erythematosus complicated with cardiovascular disease, including a reagent for detecting the expression level of the above-mentioned biomarker.

[0012] Furthermore, the product is a kit or a reagent.

[0013] The present invention also provides a use of the above biomarker in screening therapeutic drugs for systemic lupus erythematosus complicated with cardiovascular diseases.

[0014] The present invention discloses the following technical effects:

[0015] The present invention uses bioinformatics analysis to determine that the core shared genes involved in the common mechanism of SLE and CVD are CD163, IL1B, IL1RN, MMP9 and NFKBIA. These five genes are used as biomarkers for diagnosing systemic lupus erythematosus complicated with cardiovascular disease, have good diagnostic efficacy, and can achieve the purpose of early diagnosis of systemic lupus erythematosus complicated with cardiovascular disease.

[0016] The present invention determines for the first time that CD163, IL1B, IL1RN, MMP9 and NFKBIA are potential biomarkers for the diagnosis and treatment of SLE complicated with CVD, providing an important basis for the improvement of SLE patients and a new target for the clinical diagnosis and treatment of SLE complicated with CVD. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0018] Figure 1 The common genes of SLE and CVD; A: differential genes between SLE and normal control group; B: differential genes between CVD and normal control group; C: common genes of SLE and CVD;

[0019] Figure 2 Functional analysis of common genes in SLE and CVD; A: GO-BP analysis results; B: GO-CC analysis results; C: GO-MF analysis results; D: KEGG pathway analysis results;

[0020] Figure 3 PPI network of genes shared by SLE and CVD; A: PPI network; B: Module 1; C: Module 2;

[0021] Figure 4 Screening results of core shared genes between SLE and CVD; A, B: Screening results of GSE50772 dataset; C, D: Screening results of GSE66360 dataset; E: Screening results of core shared genes between SLE and CVD;

[0022] Figure 5 is the diagnostic efficacy of the five core shared genes in the GSE50772 dataset; AEs are CD163, IL1B, IL1RN, MMP9, and NFKBIA;

[0023] Figure 6 is the diagnostic efficacy of the five core shared genes in the GSE66360 dataset; AEs are CD163, IL1B, IL1RN, MMP9, and NFKBIA;

[0024] Figure 7 The external validation diagnostic efficacy of the five core shared genes in the GSE50772 dataset; AEs are CD163, IL1B, IL1RN, MMP9, and NFKBIA;

[0025] Figure 8 The external validation diagnostic efficacy of the five core shared genes in the GSE66360 dataset; AEs are CD163, IL1B, IL1RN, MMP9, and NFKBIA;

[0026] Figure 9-13 These are the results of high expression of CD163, IL1B, IL1RN, MMP9, and NFKBIA in PBMCs of the SLE group;

[0027] Figure 14-18 The results of enrichment analysis of five core shared gene sets of CD163, IL1B, IL1RN, MMP9 and NFKBIA in SLE samples are shown in order;

[0028] Figure 19-Figure 23 The results of enrichment analysis of five core shared gene sets of CD163, IL1B, IL1RN, MMP9 and NFKBIA in CVD samples are shown in order;

[0029] Fig.24 The relationship between the five core shared genes and immune cells; A: SLE samples; B: CVD samples;

[0030] Fig.25 Key transcription factors for core shared genes predicted by the ChEA3 platform;

[0031] Fig.26 are the 10 enriched transcription factors;

[0032] Fig. 27 To construct the miRNA-mRNA network;

[0033] Fig.28 To predict target drugs based on core shared genes using the PubChem database. DETAILED DESCRIPTION

[0034] Various exemplary embodiments of the present invention will now be described in detail. This detailed description should not be considered as limiting the present invention, but should be understood as a more detailed description of certain aspects, features, and embodiments of the present invention.

[0035] It should be understood that the terms described in the present invention are only for describing a particular embodiment and are not intended to limit the present invention. In addition, for the numerical range in the present invention, it should be understood that each intermediate value between the upper and lower limits of the scope is also specifically disclosed. The intermediate value in any stated value or stated range, and each smaller range between any other stated value or intermediate value in the described range is also included in the present invention. The upper and lower limits of these smaller ranges can be independently included or excluded in the scope.

[0036] Unless otherwise indicated, all technical and scientific terms used herein have the same meanings as those generally understood by those skilled in the art. Although the present invention describes only preferred methods and materials, any methods and materials similar or equivalent to those described herein may also be used in the implementation or testing of the present invention. All documents mentioned in this specification are incorporated by reference to disclose and describe the methods and / or materials associated with the documents. In the event of a conflict with any incorporated document, the content of this specification shall prevail.

[0037] It will be apparent to those skilled in the art that various modifications and variations may be made to the specific embodiments of the present invention description without departing from the scope or spirit of the present invention. Other embodiments derived from the present invention description will be apparent to those skilled in the art. The present invention description and examples are exemplary only.

[0038] The words “include,” “including,” “have,” “contain,” etc. used in this document are open-ended terms, meaning including but not limited to.

[0039] Example 1

[0040] 1 Screening for common genes between SLE and CVD

[0041] The Gene Expression Omnibus (GEO) is a publicly accessible database containing a large number of microarray and high-throughput sequencing datasets from research institutions around the world. In this study, we used the GEOquery R package to download four microarray datasets related to SLE or CVD (GSE50772, GSE61635, GSE66360, and GSE48060). The two datasets, GSE50772 and GSE66360, were used as training sets for preliminary analysis. GSE50772 contained 61 SLE and 20 normal peripheral blood mononuclear cell (PBMC) samples, while GSE66360 contained 31 acute myocardial infarction (AMI) and 21 normal PBMC samples. GSE50772 was selected as the validation set for SLE, and GSE66360 was selected as the validation set for CVD. When multiple probe IDs were associated with a single gene symbol, the expression value of the gene was determined by averaging the expression of the corresponding probe IDs. We used the R package limma to perform background correction, normalization, and log2 transformation on all data for subsequent analysis. Specifically:

[0042] The DEGs (differentially expressed genes) of the GSE50772 and GSE66360 datasets were identified using the limma package in R, with a screening threshold of p value < 0.05 and differential expression fold (FC) > 2. The shared DEGs in the two datasets were identified and visualized using FunRich software (Version 3.1.3) (http: / / www.funrich.org / ).

[0043] The results showed that a total of 558 DEGs were screened between SLE patients and normal controls, including 286 up-regulated genes and 272 down-regulated genes ( Figure 1 A total of 358 DEGs were screened in the CVD group, with 295 up-regulated genes and 63 down-regulated genes ( Figure 1 Middle B). 85 identical DEGs were shared genes, including DUSP1, FOS, CXCL8, CXCL2, NFKBIA, TNFAIP6, ZFP36, CXCL1, FFAR2, DYSF, A2M-AS1, FPR1, S100P, FOSB, BCL6, IL1B, SGK1, CLEC4E, KCNJ2, BABAM2-AS1, CHI3L1, LRG1, SLPI, PPP1R15A, MMP9, GPR84, FCGR3B, NFIL3, IL1RN, IL1R2, CDA, ANXA3, TRIB1, MCEMP1, AQP9, G0S2, RNASE2, ADM, IER3, MME, GLT1D1, and JDP2. , FCGR1B, PTX3, NAMPT, ADGRG3, PTGDR, PTGS2, CYP4F3, MXD1, PRKY, JUN, IRS2, CREB5, KDM5D, TXLNGY, LINC00260, SLC25A37, RPS4Y1, CLEC4D, RBP7, BCL2A1, CXCL3, CMTM2, XIST, CD163, MGAM, USP9Y, DDX3Y, LOXL1-AS1, TNF, VNN3, NOG, CCDC65, HCAR3, PRSS35, IL23R, TTTY15, EIF1AY, ELOVL4, B3GALT2, RASGEF1B, MGP, FN1, and FOLR3. These 85 shared genes may be involved in the regulatory process in both SLE and CVD ( Figure 3 Middle C).

[0044] 2 Functional annotation and pathway enrichment of shared genes

[0045] The shared genes were analyzed by GO (gene function annotation) and KEGG (pathway enrichment analysis) methods using clusterProfiler package in R to reveal the functions of shared genes. GO terms include three parts: BP (biological process), CC (cellular composition), and MF (molecular function). KEGG analysis was used to explore potential pathways. The ggplot R program plotted the top 10 entries of GO and KEGG.

[0046] GO-BP analysis showed that shared genes were enriched in various responses, including responses to steroid hormones, bacteria-derived molecules, mechanical stimulation, etc. ( Figure 2 In GO-CC, it is mainly enriched in the granules and lumens of various immune cells ( Figure 2 In terms of GO-MF, signal receptor activation activity is emphasized ( Figure 2 Middle C). KEGG pathway analysis showed that shared genes were involved in infection-related and inflammation-related diseases, such as kala-azar ( Figure 2 In middle D), the shared genes were also enriched in inflammation- and immunity-related signaling pathways, such as TNF signaling pathway, NOD-like receptor signaling pathway, NF-kappaB signaling pathway, and IL-17 signaling pathway.

[0047] 3PPI network construction and identification of core shared genes

[0048] The MCODE plug-in and LASSO machine learning algorithm of Cytoscape were used to explore the core shared genes related to the common pathogenesis of SLE and CVD. First, MCODE was used to screen out important modules from the previous PPI network, and then LASSO machine learning was performed through Rackageglmnet to screen out hub genes from key functional modules, and finally the common hub genes were selected as core shared genes. The details are as follows:

[0049] Constructing a network for shared genes revealed their close relationships ( Figure 3 From the PPI network, two modules were identified using the Cytoscape plugin MCODE ( Figure 3 Module 1 includes 15 nodes and 90 edges, and the cluster score (density multiplied by the number of members) is 12.857 ( Figure 3 Module 2 has 3 nodes and 3 edges, with a score of 3 ( Figure 3Then the LASSO machine learning algorithm was used to further screen the core shared genes using the gene expression levels of the candidate hub genes in the two modules as features. In GSE50772, six genes (NFKBIA, IL1RN, CXCL2, IL1B, MMP9, and CD163) were identified as hub genes ( Figure 4 In GSE66360, ten genes (NFKBIA, IL1RN, FN1, FOS, IL1B, PTGS2, JUN, MMP9, ZFP36, and CD163) were identified as hub genes ( Figure 4 Finally, overlapping genes (CD163, IL1B, IL1RN, MMP9, and NFKBIA) were selected as core shared genes that may be closely related to the pathogenesis of SLE and CVD ( Figure 4 The differential expression of the five core shared genes in SLE and CVD is shown in Table 1.

[0050] Table 1 Differential expression of 5 core shared genes in SLE and CVD

[0051]

[0052] The diagnostic efficacy of 4 core shared genes for diseases

[0053] In the GSE50772 dataset, these five core shared genes have outstanding value as diagnostic markers: CD163 (AUC = 0.848), IL1B (AUC = 0.958), IL1RN (AUC = 0.936), MMP9 (AUC = 0.935) and NFKBIA (AUC = 0.964) ( Figure 5 The same ROC analysis was performed on these genes in the GSE66360 dataset, and each biomarker showed robust predictive performance: CD163 (AUC = 0.847), IL1B (AUC = 0.869), IL1RN (AUC = 0.861), MMP9 (AUC = 0.859), and NFKBIA (AUC = 0.848) ( Figure 6 Then, we performed external validation of the diagnostic efficacy of the core shared genes in two datasets related to SLE (GSE50772) and CVD (GSE66360). The results showed that the AUC values ​​of all five genes were greater than 0.6, demonstrating that the detection of SLE ( Figure 7 AE) and CVD( Figure 8 The core shared genes have good diagnostic accuracy. Finally, it was found that the core shared genes can be used as reliable biomarkers for diagnosing SLE, CVD and SLE complicated with CVD, and have certain clinical value.

[0054] 5 Experimental samples to verify diagnostic efficacy

[0055] 5.1 Animal experiments

[0056] MRL / lpr mouse model: It is a spontaneous SLE mouse model. Due to the mutation of the lymphoproliferation gene (lpr), Fas antigen expression defects are induced, resulting in massive proliferation of immune cells including B cells, T cells and macrophages, thereby inducing SLE. Serological SLE manifestations can appear in MRL / lpr mice as early as 8 weeks of age, and phenotypes such as proteinuria and systemic lymphadenopathy can be observed at 12 weeks of age. This model not only conforms to the manifestations of SLE in serology and pathology, but also can better simulate the characteristics of human SLE in terms of skin lesions and lymphadenitis. Therefore, this study used MRL / lpr mice (n=3) as the source of SLE blood samples, and the control group (n=3) used normal healthy mice as the source of control group blood samples. Both mice were 8 weeks old.

[0057] 5.2 Peripheral blood mononuclear cell (PBMC) isolation and RT-qPCR

[0058] Blood samples were added to tubes containing ethylenediaminetetraacetic acid (EDTA). According to the instructions, PBMCs were separated by density gradient centrifugation at 18-20°C using Ficoll-Paque (Sigma-Aldrich, USA) reagent. After centrifugation, the corresponding liquid layer of PBMCs was aspirated. 900 μL Trizol (Takara, Japan) was added to disrupt the cells, and after standing on ice for 5 minutes, 200 μL chloroform (SINOPHARM, China), an equal volume of isopropanol (SINOPHARM, China), and anhydrous ethanol (SINOPHARM, China) were added in sequence. After each reagent was added, it was thoroughly mixed, stood on ice, and centrifuged at low temperature. The RNA precipitate obtained by discarding the organic solvent was dissolved in an appropriate amount of DEPC-treated water, and the concentration was detected using the Nanodrop 2000 spectrophotometer (Thermo Fisher Scientific, USA). The PrimeScript RT kit (TaKaRa, Japan) was used to remove genomic DNA and reverse transcribe and synthesize cDNA. Based on the SYBR GreenER Supermix (TaKaRa, Japan) kit, a 20 μL reaction system was constructed in eight tubes and real-time fluorescence quantitative PCR was performed on a 7500 Real-Time PCR System (Thermo Fisher Scientific, USA). –ΔΔCtMethods The expression of CD163, IL1B, IL1RN, MMP9 and / or NFKBIA was analyzed based on the normalized relative expression of β-actin.

[0059] In this study, peripheral blood samples were obtained from three MRL / lpr mice and three normal mice, and PBMCs were isolated. Then, RT-qPCR was used to analyze the expression differences of CD163, IL1B, IL1RN, MMP9 and / or NFKBIA in MRL / lpr mice and normal mice. The results showed that ( Figure 9-13 ), CD163, IL1B, IL1RN, MMP9 and / or NFKBIA were all highly expressed in PBMCs of the SLE group (p<0.01). Among them, the expression difference of CD163 between the SLE group and the control group was the greatest.

[0060] The specificity and sensitivity of the five indicators were tested, and the results showed that the specificity and sensitivity of CD163, IL1B, IL1RN, MMP9 and NFKBIA for diagnosing SLE were both greater than 70%, proving that these five indicators have the potential to serve as diagnostic markers for SLE.

[0061] Example 2

[0062] 1GSEA and immune infiltration of core shared genes

[0063] We have studied SLE ( Figure 14-18 ) and CVD( Figure 19-Figure 23 ) samples were subjected to gene set enrichment analysis (GSEA) and found that inflammation and immune response were involved in common pathogenic processes. SLE ( Fig.24 A) and CVD( Fig.24 (B) The relationship between core shared genes and immune cells, noting that the core genes are closely related to most immune cells.

[0064] 2TF-mRNA, miRNA-mRNA and drug target networks

[0065] The ChEA3 platform was used to predict key transcription factors of core shared genes ( Fig.25 ). The results enriched 611 cross-transcription factors, and the top 10 transcription factors were ranked according to the average score, including TFEC, CSRNP1, FOSB, SPIC, PPARD, ATF3, ZNF267, NFE2L2, MTF1, and BHLHE40 ( Fig.26 ). Similarly, we obtained miRNAs from the online database miRcode based on core shared genes and constructed a miRNA-mRNA network ( Fig. 27). In addition, the PubChem database was used to identify possible target drugs based on core shared genes. A total of 59 target drugs were predicted, including 37 targeting IL1B, 3 targeting IL1RN, 12 targeting MMP9, and 9 targeting NFKBIA ( Fig.28 ).

[0066] In summary, the presence of chronic inflammation, autoimmunity, and autoantibodies can lead to an increased risk of cardiovascular disease in patients with systemic lupus erythematosus. In addition, other risk factors for cardiovascular disease, such as hypertension, diabetes, and hyperlipidemia, are also more common in SLE patients. However, the exact mechanism of the link between systemic lupus erythematosus and cardiovascular disease is not yet fully understood. In this study, we focused on the associated genetic characteristics, potential regulatory targets and pathways, and therapeutic molecules that may help control this disease. It was found that 5 core shared genes can be used as reliable biomarkers for the diagnosis of systemic lupus erythematosus, cardiovascular disease, and systemic lupus erythematosus complicated by cardiovascular disease, and have certain clinical value. Core shared genes are also used as new predictors to promote precision medicine.

[0067] The embodiments described above are only descriptions of the preferred modes of the present invention, and are not intended to limit the scope of the present invention. Without departing from the design spirit of the present invention, various modifications and improvements made to the technical solutions of the present invention by ordinary technicians in this field should all fall within the protection scope determined by the claims of the present invention.

Claims

1. A biomarker for diagnosing systemic lupus erythematosus complicated with cardiovascular disease, characterized in that: The biomarkers include CD163, IL1B, IL1RN, MMP9 and NFKBIA.

2. Use of a reagent for detecting the expression level of the biomarker according to claim 1 in the preparation of a diagnostic product for systemic lupus erythematosus complicated with cardiovascular disease.

3. The use according to claim 2, characterized in that: The product is a kit or a reagent.

4. A product for diagnosing systemic lupus erythematosus, cardiovascular disease or systemic lupus erythematosus complicated with cardiovascular disease, characterized in that: Comprising a reagent for detecting the expression level of the biomarker according to claim 1.

5. The product according to claim 4, characterized in that The product is a kit or a reagent.

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