CN117721197A - Biomarker for diagnosing systemic lupus erythematosus complicated with cardiovascular diseases and application thereof - Google Patents

Abstract

The invention discloses a biomarker for diagnosing systemic lupus erythematosus complicated with cardiovascular diseases and application thereof, belonging to the technical field of biological medicines, wherein the biomarker comprises CD163, IL1B, IL1RN, MMP9 and/or NFKBIA. The present invention utilizes bioinformatics analysis to determine that the core shared genes involved in the common mechanisms of SLE and CVD are CD163, IL1B, IL RN, MMP9 and NFKBIA. The five genes are used as biomarkers for diagnosing systemic lupus erythematosus complicated with cardiovascular diseases, have good diagnosis efficiency, and can realize the purpose of early diagnosis of systemic lupus erythematosus complicated with cardiovascular diseases. The invention determines that the five genes are potential biomarkers of SLE concurrent CVD disease diagnosis and treatment targets for the first time, provides important basis for improving SLE patients, and provides a new target for clinical diagnosis and treatment of SLE concurrent CVD.

Description

Biomarker for diagnosing systemic lupus erythematosus complicated with cardiovascular diseases and application thereof

Technical Field

The invention relates to the technical field of biological medicines, in particular to a biomarker for diagnosing systemic lupus erythematosus complicated with cardiovascular diseases and application thereof.

Background

Systemic lupus erythematosus (systemic lupus erythematosus, SLE) is a chronic inflammatory autoimmune disease affecting multiple organ systems characterized by the presence of large numbers of autoantibodies. The disease mainly affects women of childbearing age, and the ratio of men and women is 1:9. The global adult SLE prevalence varies from 30 to 150 per 100,000 people and the annual incidence varies from 2.2 to 23.1 per 100,000 people. Although the exact cause of SLE is still unclear, it is thought that immune dysfunction may be the result of a combination of genetic, environmental and hormonal factors.

Despite significant advances in diagnosis and treatment of SLE, the life expectancy of patients is still lower than that of the general population. This highlights the continuing unmet need for lupus patients, which changes over time. Notably, SLE patients have an increased incidence of cardiovascular disease (CVD), which has a significant impact on the quality of life and survival that these people have reduced. Thus, effective management and prevention of CVD in SLE patients is critical to improving overall health outcomes.

Multiple 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 primary pathological mechanism of SLE-related CVD, and SLE patients are estimated to be at 2-10 times more at risk for CVD than the general population. A meta-analysis report recently conducted during 2013 to 2020 reports that SLE has a CVD risk range of 1.95 to 2.84 supporting these findings. Importantly, women under 55 years of age with SLE are 5-8 times more at risk for coronary heart disease than the general population. Manzi et al even observed a 50-fold increase in risk of myocardial infarction in women aged 35-44 years compared to age-matched subjects. The increased risk of CVD in SLE patients cannot be attributed solely to traditional cardiovascular risk factors, suggesting that other factors are also involved in pathogenesis. Disease-related factors such as chronic inflammation, immune dysfunction, and SLE therapeutic drugs have been considered as potential factors leading to increased risk. Furthermore, genetics may play a role in the common genetic risk factors for SLE and CVD. However, the common genetic risk factors for systemic lupus erythematosus and cardiovascular disease are not clear, as few studies investigate this relationship at the genetic level. Thus, the common genetic risk factors for CVD in the context of SLE remain unclear.

Further research is carried out by utilizing the gene microarray technology, so that potential genetic mechanisms can be better known and personalized prevention and treatment strategies can be formulated. Systemic Lupus Erythematosus (SLE) CVD is of high incidence, hidden onset, poor prognosis, early screening, diagnosis and effective treatment are of paramount importance.

Disclosure of Invention

The invention aims to provide a biomarker for diagnosing systemic lupus erythematosus complicated with cardiovascular diseases and application thereof, so as to solve the problems in the prior art, and the biomarker is used for determining that CD163, IL1B, IL1RN, MMP9 and NFKBIA are potential biomarkers for SLE complicated CVD disease diagnosis and treatment targets for the first time, providing important basis for improving SLE patients and providing a new target for clinical diagnosis and treatment of SLE complicated CVD.

In order to achieve the above object, the present invention provides the following solutions:

the invention provides a biomarker for diagnosing systemic lupus erythematosus complicated with cardiovascular diseases, which comprises CD163, IL1B, IL RN, MMP9 and/or NFKBIA.

The invention also provides application of the reagent for detecting the expression level of the biomarker in preparing a diagnostic product for cardiovascular diseases complicated with systemic lupus erythematosus.

Further, the product is a kit or a reagent.

The invention also provides a product for diagnosing systemic lupus erythematosus, cardiovascular diseases or cardiovascular diseases complicated with systemic lupus erythematosus, which comprises a reagent for detecting the expression level of the biomarker.

Further, the product is a kit or a reagent.

The invention also provides application of the biomarker in screening medicaments for treating cardiovascular diseases complicated with systemic lupus erythematosus.

The invention discloses the following technical effects:

the present invention utilizes bioinformatics analysis to determine that the core shared genes involved in the common mechanisms of SLE and CVD are CD163, IL1B, IL RN, MMP9 and NFKBIA. The five genes are used as biomarkers for diagnosing systemic lupus erythematosus complicated with cardiovascular diseases, have good diagnosis efficiency, and can realize the purpose of early diagnosis of systemic lupus erythematosus complicated with cardiovascular diseases.

The invention determines that CD163, IL1B, IL RN, MMP9 and NFKBIA are potential biomarkers of SLE concurrent CVD disease diagnosis and treatment targets for the first time, provides important basis for improving SLE patients, and provides a new target for clinical diagnosis and treatment of SLE concurrent CVD.

Drawings

In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are needed in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.

FIG. 1 is a common gene for SLE and CVD; a: differential genes of SLE and normal control group; b: differential genes of CVD and normal control; c: common genes for SLE and CVD;

FIG. 2 is a functional analysis of common genes for SLE and CVD; a: GO-BP analysis results; b: GO-CC analysis results; c: GO-MF analysis results; d: KEGG pathway analysis results;

FIG. 3 is a PPI network of SLE and CVD shared genes; a: a PPI network; b: a module 1; c: a module 2;

FIG. 4 shows SLE and CVD core shared gene screening results; A. b: GSE50772 dataset screening results; C. d: GSE66360 dataset screening results; e: SLE and CVD core sharing gene screening results;

FIG. 5 is a graph showing diagnostic efficacy of 5 core sharing genes in GSE50772 dataset; A-E are CD163, IL1B, IL RN, MMP9 and NFKBIA in sequence;

FIG. 6 is a diagnostic efficacy of 5 core sharing genes in a GSE66360 dataset; A-E are CD163, IL1B, IL RN, MMP9 and NFKBIA in sequence;

FIG. 7 is a graph showing the diagnostic efficacy of 5 core sharing genes on the external validation of GSE50772 dataset; A-E are CD163, IL1B, IL RN, MMP9 and NFKBIA in sequence;

FIG. 8 is a graph showing the diagnostic efficacy of 5 core sharing genes on the external validation of GSE66360 dataset; A-E are CD163, IL1B, IL RN, MMP9 and NFKBIA in sequence;

FIGS. 9-13 show the results of high expression of CD163, IL1B, IL1RN, MMP9, NFKBIA in PBMC of SLE group;

FIGS. 14-18 show the results of a CD163, IL1B, IL1RN, MMP9 and NFKBIA 5 core-shared gene set enrichment analysis of SLE samples in sequence;

FIGS. 19-23 show the results of a CD163, IL1B, IL1RN, MMP9 and NFKBIA 5 core shared gene set enrichment analysis of CVD samples in sequence;

FIG. 24 is a graph showing the relationship between 5 core sharing genes and immune cells; a: SLE samples; b: a CVD sample;

FIG. 25 is a diagram showing key transcription factors of core shared genes predicted by the ChEA3 platform;

FIG. 26 is an enriched of 10 transcription factors;

FIG. 27 is a constructed miRNA-mRNA network;

FIG. 28 is a graph showing the predicted target drugs based on core shared genes using the PubCHem database.

Detailed Description

Various exemplary embodiments of the invention will now be described in detail, which should not be considered as limiting the invention, but rather as more detailed descriptions of certain aspects, features and embodiments of the invention.

It is to be understood that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. In addition, for numerical ranges in this disclosure, it is understood that each intermediate value between the upper and lower limits of the ranges is also specifically disclosed. Every smaller range between any stated value or stated range, and any other stated value or intermediate value within the stated range, is also encompassed within the invention. The upper and lower limits of these smaller ranges may independently be included or excluded in the range.

Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. Although only preferred methods and materials are described herein, any methods and materials similar or equivalent to those described herein can be used in the practice or testing of the present invention. All documents mentioned in this specification are incorporated by reference for the purpose of disclosing and describing the methods and/or materials associated with the documents. In case of conflict with any incorporated document, the present specification will control.

It will be apparent to those skilled in the art that various modifications and variations can be made in the specific embodiments of the invention described herein without departing from the scope or spirit of the invention. Other embodiments will be apparent to those skilled in the art from consideration of the specification of the present invention. The specification and examples of the present invention are exemplary only.

As used herein, the terms "comprising," "including," "having," "containing," and the like are intended to be inclusive and mean an inclusion, but not limited to.

Example 1

1 screening for consensus genes for SLE and CVD

Gene expression profiling (GEO) is a publicly accessible database containing a large array of microarrays and high throughput sequencing datasets from research institutions around the world. In this study, we downloaded four microarray datasets (GSE 50772, GSE61635, GSE66360, and GSE 48060) associated with SLE or CVD using a GEOquery R package. Two data sets GSE50772 and GSE66360 are 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 a plurality of probe IDs are associated with a single gene symbol, the expression value of the gene is determined by averaging the expression of the corresponding probe IDs. We used R-package limma for background calibration, normalization and log2 conversion of all data for subsequent analysis. The method comprises the following steps:

DEGs (differentially expressed genes) of GSE50772 and GSE66360 datasets were identified using limmapackage in R with p-values <0.05 and fold differential expression (FC) >2 as screening thresholds. Shared DEGs in both data sets were identified and visualized using FunRich software (version 3.1.3) (http:// www.funrich.org /).

The results showed that the SLE group screened 558 DEGs in total between SLE patient and normal control, with 286 up-regulated genes and 272 down-regulated genes (a in fig. 1). The CVD group screened 358 DEGs in total, up-regulated and down-regulated genes 295 and 63, respectively (B in FIG. 1). 85 identical DEGs AS a shared gene, comprises DUSP1, FOS, CXCL8, CXCL2, NFKBIA, TNFAIP, ZFP36, CXCL1, FFAR2, DYSF, A2M-AS1, FPR1, S100P, FOSB, BCL6, IL1B, SGK1, CLEC4E, KCNJ2, BABAM2-AS1, CHI3L1, LRG1, SLPI, PPP1R15A, MMP, GPR84, FCGR3B, NFIL3, IL1RN, IL1R2, CDA, ANXA3, TRIB1, MCEMP1, AQP9, G0S2, RNASE2, ADM, IER3, MME, JDT 1D1, P2 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, LOXL-AS 1, 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 regulation process in both SLE and CVD (C in fig. 3).

2 functional annotation and pathway enrichment of shared genes

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

GO-BP analysis showed that shared genes were enriched in a variety of responses, including responses to steroid hormones, bacterial derived molecules, mechanical stimuli, etc. (a in fig. 2). Particles and lumen (B in fig. 2) enriched in various immune cells are mainly concentrated in GO-CC. In the context of GO-MF, signal receptor activation activity and the like are emphasized (C in FIG. 2). KEGG pathway analysis showed that shared genes are involved in infection-related and inflammation-related diseases such as kala-azar and the like (D in fig. 2), and that shared genes are also enriched in inflammatory and immune-related signaling pathways such as TNF signaling pathway, NOD-like receptor signaling pathway, NF-kappaB signaling pathway, and IL-17 signaling pathway.

3PPI network construction and core sharing gene identification

The MCODE plugin and LASSO machine learning algorithm using Cytoscape explored core shared genes associated with SLE and CVD co-pathogenesis. Firstly, screening an important module from a previous PPI network by using MCODE, then performing LASSO machine learning through a package glmnet to screen out a hub gene from a key functional module, and finally selecting a common hub (hub) gene as a core sharing gene. The method comprises the following steps:

a network was constructed for the shared genes, revealing their affinity (A in FIG. 3). From the PPI network, two modules (B and C in fig. 3) were determined using the Cytoscape plug-in MCODE. Module 1 included 15 nodes and 90 edges with a cluster score (density times number of members) of 12.857 (B in fig. 3). Module 2 has 3 nodes and 3 edges, scoring 3 (C in fig. 3). And then using LASSO machine learning algorithm to further screen the core shared gene by taking the gene expression level of the candidate central gene in the two modules as a characteristic. In GSE50772, six genes (NFKBIA, IL1RN, CXCL2, IL1B, MMP9, and CD 163) were identified as pivot genes (a and B in fig. 4). In GSE66360, ten genes (NFKBIA, IL1RN, FN1, FOS, IL1B, PTGS2, JUN, MMP9, ZFP36 and CD 163) were identified as pivot genes (C and D in fig. 4). Finally, the selection of overlapping genes (CD 163, IL1B, IL1RN, MMP9 and NFKBIA) as core shared genes may be closely related to SLE and CVD pathogenesis (E in fig. 4). The differential expression of the 5 core shared genes in SLE and CVD is shown in table 1.

TABLE 15 differential expression of core sharing genes in SLE and CVD

4 core sharing Gene diagnostic efficacy against disease

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) (a-E in fig. 5). The same ROC analysis was performed for these genes in GSE66360 dataset, each biomarker showing robust predictive performance: CD163 (auc=0.847), IL1B (auc=0.869), IL1RN (auc=0.861), MMP9 (auc=0.859) and NFKBIA (auc=0.848) (a-E in fig. 6). Then, we externally validated the diagnostic efficacy of the core shared gene in both data sets associated with SLE (GSE 50772) and CVD (GSE 66360). The results showed that all five genes had AUC values greater than 0.6, demonstrating good diagnostic accuracy for detection of SLE (A-E in FIG. 7) and CVD (A-E in FIG. 8). Finally, the core sharing gene can be used as a reliable biomarker for diagnosing SLE, CVD and SLE concurrent CVD diseases, and has a certain clinical value.

5 test sample verification of diagnostic efficacy

5.1 animal experiments

MRL/lpr mouse model: belonging to the spontaneous SLE mouse model, SLE is induced by inducing massive proliferation of immune cells including B cells and T cell macrophages due to Fas antigen expression defect induced by mutation of lymphoproliferative gene (lpr). Serologic SLE expression can be seen in MRL/lpr mice at the earliest age of 8 weeks, and phenotypes such as proteinuria and generalized lymphadenectasis can be seen at 12 weeks of age. The model not only accords with SLE expression in serology and pathology, but also can better simulate the characteristics of human SLE in the aspects of skin loss and lymphadenitis. The study therefore used MRL/lpr mice (n=3) as the SLE blood sample source and the control group (n=3) used normal healthy mice as the control group blood sample source. Both mice were 8 weeks old.

5.2 Peripheral Blood Mononuclear Cell (PBMC) isolation and RT-qPCR

Blood samples were added to tubes containing ethylenediamine tetraacetic acid (EDTA). According to the instructions, PBMC were isolated by density gradient centrifugation at 18-20℃using Ficoll-Paque (Sigma-Aldrich, USA) reagent. After centrifugation, PBMCs corresponding to the liquid stratification were aspirated. mu.L of Trizol (Takara, japan) was added to disrupt the cells, and after leaving on ice for 5 minutes, 200. Mu.L of chloroform (SINOPHARM, china), an equal volume of isopropanol (SINOPHARM, china) and absolute ethanol (SINOPHARM, china) were added sequentially. After each reagent addition, the mixture is fully and evenly mixed, kept stand on ice and centrifuged at low temperature. Discarding the organic solventThe RNA pellet obtained was dissolved in an appropriate amount of DEPC treated water and the concentration was measured using Nanodrop 2000 spectrophotometry (Thermo Fisher Scientific, USA). Genomic DNA was removed using PrimeScript RT kit (TaKaRa, japan) and cDNA was synthesized by reverse transcription. An eight-tube per well 20. Mu.L reaction System was constructed based on the SYBR GreenER Supermix (TaKaRa, japan) kit and Real-Time fluorescent quantitative PCR was performed on a 7500Real-Time PCR System (Thermo Fisher Scientific, USA). We use 2 ^–ΔΔCt The method analyzes the expression of CD163, IL1B, IL RN, MMP9 and/or NFKBIA based on normalized relative expression of β -actin.

The present study takes peripheral blood samples from 3 MRL/lpr mice and 3 normal mice and isolates PBMC, followed by analysis of differences in expression of CD163, IL1B, IL1RN, MMP9 and/or NFKBIA in MRL/lpr mice and normal mice using RT-qPCR. The results showed (fig. 9-13) that CD163, IL1B, IL1RN, MMP9 and/or NFKBIA were all highly expressed in PBMCs of SLE group (p < 0.01). Of these, CD163 was most differentially expressed in SLE and control groups.

The specificity and the sensitivity of 5 indexes are detected, and the results show that the specificity and the sensitivity of the SLE diagnosis by CD163, IL1B, IL1RN, MMP9 and NFKBIA are more than 70 percent, which proves that the 5 indexes have the potential of being used as SLE diagnosis markers.

Example 2

1 immune infiltration of GSEA and core shared gene

We performed a Gene Set Enrichment Analysis (GSEA) on SLE (FIGS. 14-18) and CVD (FIGS. 19-23) samples, and found that inflammation and immune responses were involved in the common pathogenic process. The relationship between the core sharing genes and immune cells in SLE (a in fig. 24) and CVD (B in fig. 24) was also studied, noting that the core genes are closely related to most immune cells.

2TF-mRNA, miRNA-mRNA and drug target networks

The key transcription factors of the core shared genes were predicted using the ChEA3 platform (fig. 25). The results were enriched for 611 cross transcription factors, with the top 10 transcription factors ranked according to 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). Furthermore, the PubChem database was used to determine possible drugs of interest from the core shared genes. The results predicted a total of 59 target drugs, including 37 for IL1B, 3 for IL1RN, 12 for MMP9, 9 for NFKBIA (fig. 28).

In summary, chronic inflammation, autoimmunity, and the presence of 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 prevalent in SLE patients. However, the exact mechanism of the link between systemic lupus erythematosus and cardiovascular disease is not completely understood. In the present study we focused on the associated genetic features, potential regulatory targets and pathways, and therapeutic molecules that might help to control this disease. The discovery of the 5 core sharing genes can be used as a reliable biomarker for diagnosing systemic lupus erythematosus, cardiovascular diseases and cardiovascular diseases complicated with systemic lupus erythematosus, and has a certain clinical value. The core shared gene also serves as a novel predictor for facilitating accurate medical treatment.

The above embodiments are only illustrative of the preferred embodiments of the present invention and are not intended to limit the scope of the present invention, and various modifications and improvements made by those skilled in the art to the technical solutions of the present invention should fall within the protection scope defined by the claims of the present invention without departing from the design spirit of the present invention.

Claims (6)

1. A biomarker for diagnosing cardiovascular disease associated with systemic lupus erythematosus, wherein the biomarker comprises CD163, IL1B, IL RN, MMP9, and/or NFKBIA.

2. Use of an agent that detects the expression level of a biomarker of claim 1 in the manufacture of a diagnostic product for cardiovascular disease associated with systemic lupus erythematosus.

3. The use according to claim 2, wherein the product is a kit or a reagent.

4. A product for diagnosing systemic lupus erythematosus, cardiovascular disease, or cardiovascular disease complicated with systemic lupus erythematosus, comprising an agent that detects the expression level of the biomarker of claim 1.

5. The product of claim 4, wherein the product is a kit or reagent.

6. Use of the biomarker of claim 1 in screening for a therapeutic drug for systemic lupus erythematosus complicated with cardiovascular disease.