Biomarkers for predicting or monitoring recurrence of NMOSD and their uses

Abstract

The present invention discloses a biomarker for predicting or monitoring recurrence of NMOSD and its application, which belongs to the field of biomedical technology. The biomarker is any one or two or more of APOE, SRGN, NACA, HABP2, BLVRB, PRG4, S100A8, S100A9, TRAP5, IGFBP5, ST13, FST, LTF, CFHR3, CALD1, PRDX1, PRDX4, PRDX5, GRB2, PLXNB2, TIMP1, TOLLIP, DUSP3, MTPN, ARHGDIB, Wdr44, DBI, HSPB1, THBS1, MAPRE2, FLNA, RAB11A, SRI, IGLL1, TLN1, CD59, FGA, CXCL10, CXCL12, PDGFA, CCL5. The biomarkers can be used to prepare reagents or kits for predicting or monitoring the recurrence of neuromyelitis optica spectrum disorder, and can predict the risk of a subject suffering from neuromyelitis optica spectrum disorder or monitor the probability of recurrence of neuromyelitis optica spectrum disorder in a patient or subject.

Description

[Technical Field]

[0001] The present invention relates to a biomarker for predicting the prognosis and recurrence of NMOSD, which belongs to the field of biomedical technology, and specifically to a biomarker for predicting or monitoring the recurrence of NMOSD and its use.

[0002] This application claims priority from the following Chinese patent applications:

[0003] Application date: August 10, 2021 Application number: 202110911380.4 Title of invention: Biomarkers for predicting or monitoring recurrence of NMOSD and their uses [Background technology]

[0004] Neuromyelitis optica spectrum disorder (NMOSD) is an immune-mediated demyelinating disease of the central nervous system (CNS). It primarily affects the optic nerve and spinal cord simultaneously or sequentially, resulting in acute or subacute demyelinating lesions. There has long been debate as to whether NMOSD is an independent disease or a variant of multiple sclerosis (MS). Since Lennon et al. discovered antibodies against aquaporin-4 (AQP4) in the serum of NMO patients in 2004, NMOSD has been widely recognized as a distinct CNS autoimmune disease, distinct from MS, driven primarily by humoral immunity. The mechanism of NMOSD is as follows: sensitized B lymphocytes produce specific antibodies, which bind to complement, depositing and destroying AQP4 on the surface of astrocytes. At the same time, innate immune cells such as macrophages and eosinophils chemotactically exude and secrete inflammatory factors, resulting in the loss of myelin sheaths and necrosis of axons and brain tissue. The main clinical manifestations are optic neuritis and acute transverse myelitis, which may be single or multiple and may occur over periods of weeks, months, or even years. Although there are treatment recommendations based on small clinical studies and expert consensus (e.g., glucocorticoids, gamma globulin, azathioprine, and rituximab), there is currently no optimal treatment for NMOSD due to a lack of large-sample, randomized, double-blind, controlled clinical trials for NMOSD.

[0005] Due to the heterogeneity of clinical symptoms, the severity of neurological impairment after relapse, and variability in treatment response, there is an urgent need to develop reliable and sensitive biomarkers for the onset, relapse, and progression of NMOSD. Detection of AQP4 antibodies (AQP4-IgG) in serum can support the diagnosis of seropositive NMOSD. However, it is still unclear whether AQP4-IgG levels correlate with disease activity, severity, response to treatment, or long-term outcome. Furthermore, biomarkers for seronegative NMOSD patients have not yet been identified or validated. Therefore, there is great hope for the establishment and validation of biomarkers that can be used to predict the prognosis and relapse of NMOSD.

[0006] Single-omics data analysis is typically used to explain the association between a specific biochemical marker and a disease, but it cannot explain the complex causal relationships within. Technological advances have ushered in the "omics era," enabling us to collect and integrate data and information from various molecular levels. The integration of these multi-omics data means that thousands of proteins (proteomics), genes (genomics), RNA (transcriptomics), and metabolites (metabolomics) can be studied simultaneously. Artificial intelligence provides new insights into complex biological systems, revealing the network of interactions between all molecular levels. This approach combines experimental data at multiple molecular levels with computational models, processing the entire system and facilitating the identification of data with diagnostic, prognostic, and therapeutic value.

[0007] Biomarkers can suggest the pathophysiological process of NMOSD, have value in predicting NMOSD risk, and serve as the basis for clinical diagnosis and treatment. However, no biomarkers with high predictive value for NMOSD pathology or disease progression have been identified, failing to meet clinical needs. Therefore, the search for biological markers that can rapidly and accurately predict the onset, progression, and prognosis of the disease holds great promise for clinical application. At the same time, integrating data obtained by histological techniques with clinical information will help us better understand the pathological process of NMOSD and identify new intervention targets for NMOSD, thereby improving the clinical management of NMOSD patients. Summary of the Invention [Problem to be solved by the invention]

[0008] To solve the above technical problems, a biomarker for NMOSD prediction or recurrence monitoring and its use are provided, which can contribute to a deeper understanding of the pathophysiology of NMOSD, provide new opportunities for diagnosis and prognosis, and improve clinical services for NMOSD patients. [Means for solving the problem]

[0009] In order to achieve the above technical object, the present invention discloses the use of a reagent for determining the level of a biomarker in a biological sample in the preparation of a kit for predicting or monitoring the recurrence of neuromyelitis optica spectrum disorder, wherein the biomarker is any one or two or more of APOE, SRGN, NACA, HABP2, BLVRB, PRG4, S100A8, S100A9, TRAP5, IGFBP5, ST13, FST, LTF, CFHR3, CALD1, PRDX1, PRDX4, PRDX5, GRB2, PLXNB2, TIMP1, TOLLIP, DUSP3, MTPN, ARHGDIB, Wdr44, DBI, HSPB1, THBS1, MAPR2, FLNA, RAB11A, SRI, IGLL1, TLN1, CD59, FGA, CXCL10, CXCL12, PDGFA, and CCL5.

[0010] Further, the biomarkers are any one or two or more of APOE, SRGN, CD59, HABP2, PRG4, FGA, S100A8, LTF, CXCL10, CXCL12, and CFHR3.

[0011] Furthermore, the biomarkers are any two or three of APOE, SRGN, CD59, HABP2, PRG4, FGA, S100A8, LTF, CXCL10, CXCL12, and CFHR3.

[0012] Further, the biomarkers include APOE / FGA, APOE / CXCL10, APOE / CXCL12, APOE / S100A8, APOE / CFHL3, APOE / CD59, SRGN / PRG4, SRGN / HABP2, SRGN / FGA, SRGN / CXCL10, SRGN / CXCL12, SRGN / S100A8, SRGN / CFHL3, SRGN / LTF, PRG4 / HABP2, PRG4 / FGA, PRG4 / CXCL10, PRG4 / CXCL12, PRG4 / S100A8, PRG4 / CFHL3, PRG4 / LTF, HABP2 / FGA, HABP2 / CXCL10, HABP2 / CXCL12, HABP2 / S100A8, HABP2 / CFHL3, and HAB The protein may be any one or a combination of two or more of P2 / LTF, FGA / CXCL10, FGA / CXCL12, FGA / S100A8, FGA / CFHL3, FGA / LTF, FGA / CD59, CXCL10 / CXCL12, CXCL10 / S100A8, CXCL10 / CFHL3, CXCL10 / LTF, CXCL12 / S100A8, CXCL12 / LTF, S100A8 / CFHL3, S100A8 / LTF, CFHL3 / LTF, CFHL3 / CD59, SRGN / FGA / HABP2, S100A8 / CFHL3 / CXCL10, S100A8 / CFHL3 / CXCL12, PRG4 / CFHL3 / S100A8, and CXCL10 / CFHL3 / FGA.

[0013] Furthermore, the biomarker is any one of APOE / CXCL10, APOE / S100A8, SRGN / CFHL3, PRG4 / CFHL3, HABP2 / CFHL3, FGA / CXCL10, FGA / CXCL12, FGA / S100A8, FGA / CFHL3, FGA / CD59, CXCL10 / CXCL12, CXCL10 / S100A8, CXCL10 / CFHL3, CXCL12 / S100A8, CXCL12 / LTF, S100A8 / CFHL3, CFHL3 / LTF, CFHL3 / CD59, S100A8 / CFHL3 / CXCL10, S100A8 / CFHL3 / CXCL12, PRG4 / CFHL3 / S100A8, and CXCL10 / CFHL3 / FGA.

[0014] Additionally, the kit includes detection agents for determining the level of each biomarker in a subject.

[0015] Furthermore, the detection agent quantitatively measures the level of biomarkers in the biological sample by proteomics technology, including DIA mode, and specifically includes the following steps:

[0016] 1) Spectral library construction: The spectral library is a step of collecting non-redundant, high-quality peptide fragment information of all detectable biological samples as a peptide fragment identification template for subsequent data analysis, where the high-quality peptide fragment information includes fragment ion intensities and retention times that describe the peptide fragment spectral peak characteristics.

[0017] 2) Acquisition of biological sample data in DIA mode: This step involves simultaneously collecting ion characteristics of each peptide fragment based on fragment mass and retention time using high-resolution mass spectrometry. Compared to traditional methods that extract and fragment single ions, DIA mode allows for the acquisition of a wide parent ion window, allowing for simultaneous fragmentation of multiple peptide fragment ions. By fully collecting information on all detectable protein spectral peaks in a sample, it is possible to analyze a large number of samples with high reproducibility.

[0018] 3) Data analysis: Using Spectronaut software, the spectra collected in step 2) are deconvoluted, effectively realizing the analysis of quantitative measurements of each protein in the biological sample being measured.

[0019] Furthermore, the spectral library is constructed from data collected by DDA detection of a biological sample of interest.

[0020] Furthermore, the biological sample is exosomes in whole blood, serum or plasma, preferably peripheral plasma or astrocyte-derived exosome proteins.

[0021] Additionally, the kit further comprises an extraction agent for extracting exosome proteins in a biological sample.

[0022] Additionally, the kit further comprises a pretreatment agent for treating the biological sample.

[0023] Furthermore, the level of said biomarker may be the protein or mRNA level of the biomarker.

[0024] Furthermore, the kit is used to monitor the risk of developing neuromyelitis optica spectrum disorder in a subject within 5 years (preferably 1 year) of prognosis.

[0025] Furthermore, the subject is a mammal, preferably a human.

[0026] A second object of the present invention is to provide a kit for predicting or monitoring the recurrence of neuromyelitis optica spectrum disorder, which includes a detection agent for determining the level of a biomarker in a subject's body, an extraction agent for extracting exosomal proteins in a biological sample, and a pretreatment agent and buffer for treating the biological sample.

[0027] A third object of the present invention is to provide a system for predicting or monitoring the recurrence of neuromyelitis optica spectrum disorder, which is used to detect the level of each biomarker in a biological sample provided by a subject, compare it with a normal or reference expression level, and feed back the comparison result to a system operator. [Effects of the Invention]

[0028] The technical solution provided by the embodiments of the present invention has the following advantages over the prior art:

[0029] 1. The present invention provides biomarkers for predicting or monitoring the recurrence of NMOSD and uses thereof. These biomarkers can be used to prepare reagents or kits for predicting or monitoring the recurrence of neuromyelitis optica spectrum disorder, to predict the risk of a subject suffering from neuromyelitis optica spectrum disorder, or to monitor the probability of recurrence of neuromyelitis optica spectrum disorder in a patient or subject within 5 years.

[0030] 2. The biomarker group provided by the present invention may contribute to a deeper understanding of the pathophysiology of NMOSD, provide new opportunities for diagnosis and prognosis, and improve clinical services for NMOSD patients. [Brief explanation of the drawings]

[0031] The accompanying drawings herein, which are incorporated herein as part of this specification, illustrate embodiments in accordance with the invention and are used in conjunction with the specification to explain the principles of the invention.

[0032] In order to more clearly describe the embodiments of the present invention or the technical solutions in the prior art, the accompanying drawings that need to be used in the description of the embodiments or the prior art are briefly described below, and obviously, those skilled in the art can obtain other drawings based on these accompanying drawings without any creative work. [Figure 1] FIG. 1 is a diagram showing an exosome test using an NTA particle tracer in an example. [Figure 2] FIG. 1 shows TEM images of exosomes in an example. [Figure 3] FIG. 1 is a schematic diagram showing the results of an examination of exosome-specific proteins using Western blotting in an example. [Figure 4]FIG. 1 shows the differential expression results of 41 biomarkers designed according to the present invention in exosome proteomic identification derived from peripheral plasma and astrocytes of NMOSD patients and healthy individuals. [Figure 5] FIG. 1 shows differentially expressed proteins screened using enzyme-linked immunosorbent assay in the Examples. [Figure 6] FIG. 1 shows the correlation between protein concentration and the degree of acute clinical disorder in NMOSD in an example. [Figure 7] FIG. 1 shows ROC curves for each combination biomarker group. [Figure 8] FIG. 1 shows ROC curves for each combination biomarker group. DETAILED DESCRIPTION OF THE INVENTION

[0033] In order to make the above objects, features and advantages of the present invention more clearly understood, the embodiments of the present invention will be further described below. It should be noted that the embodiments of the present invention and the features in the embodiments can be combined with each other as long as they are not contradictory to each other.

[0034] Terminology As used herein, the term "neuromyelitis optica spectrum disorder (NMOSD)" refers to an immune-mediated demyelinating disease of the central nervous system that primarily manifests as acute or subacute demyelinating lesions affecting the optic nerves and spinal cord simultaneously or sequentially.

[0035] As used herein, a biomarker is a biochemical indicator of change or alterability in system, organ, tissue, cellular, and subcellular structure or function, for purposes such as diagnosing disease, determining disease staging, or assessing the safety and effectiveness of new drugs or treatments in a target population.

[0036] As used herein, "diagnosis" and similar terms refer to the identification of a specific disease.

[0037] As used herein, "prediction" and related terms refer to a description of the likely outcome of a particular condition (e.g., neuromyelitis optica spectrum disorder). The term "monitoring" refers to a subject at risk of developing neuromyelitis optica spectrum disorder, a patient who has not been diagnosed with neuromyelitis optica spectrum disorder, but who is at risk of developing neuromyelitis optica spectrum disorder based on various clinical or medical assessments.

[0038] As used herein, "sample," "biological sample," "test sample," "specimen," "sample from a subject," and "patient sample" are used interchangeably and may be a sample of blood, tissue, urine, serum, plasma, amniotic fluid, cerebrospinal fluid, placental cells or tissue, endothelial cells, leukocytes, or monocytes. Samples may be obtained directly from a patient by certain methods discussed herein or others known in the art, or may be pretreated to alter the characteristics of the sample (e.g., by filtration, distillation, extraction, concentration, centrifugation, inactivation of interfering components, addition of reagents, etc.).

[0039] Information on the 41 proteins involved in the present invention is shown in Table 1 below.

[0040] [Table 1]

[0041] In Table 1, CFHR3 and CFHL3 refer to the same protein.

[0042] In the following description, numerous details are set forth to provide a thorough understanding of the present invention; however, the present invention may be practiced in other ways than those described herein, and it is apparent that the embodiments in the specification are merely some of the embodiments of the present invention, and not all of the embodiments.

[0043] Example 1 Test Subjects and Materials: 1. Experimental subjects This study involved patients admitted to Beijing Tiantan Hospital affiliated to Capital Medical University between October 2018 and November 2019.

[0044] Among them, the clinical diagnosis of the above NMOSD patients is as follows:

[0045] (1) The age of onset ranges from 18 to 80 years old. (2) NMOSD patients were clearly diagnosed according to the 2015 Wingerchuk NMOSD diagnostic criteria; (3) AQP4 antibody in the blood is positive, (4) This is an acute episode: (a) New onset or significant worsening of neurological symptoms, such as optic neuritis, transverse myelitis, or acute brain injury (i.e., the patient must be seen within 7 days of symptom onset). (b) The new onset symptoms must persist for at least 48 hours and not be attributable to other clinical factors (e.g., fever, infection, injury, or side effects of concomitant medications). (c) The new onset symptoms must be consistent with objective clinical signs of sensory, motor, or visual hypersensitivity disturbances confirmed by a clinician. (d) A single episode of paroxysmal symptoms (e.g., tonic convulsions) is not considered an acute episode. (e) The absence of significant changes in laboratory tests and the presence of fatigue, mood changes, or sensory symptoms of bladder / defecation urges or incontinence are insufficient to determine the presence of an acute episode.

[0046] Clinical information included general patient demographics and clinical characteristics, Kurtzke's Expanded Disability Status Scale (EDSS) score, blood and cerebrospinal fluid-related indicators, and acute treatment plan.

[0047] Follow-up data will include EDSS scores at onset, 3 months, 6 months, and 1 year after onset, number of relapses, blood and biochemical indicators, imaging data, and treatment plans during remission.

[0048] 2. Sample collection The sample collection and storage process for NMOSD patients and healthy controls was as follows.

[0049] (1) Plasma collection: Peripheral blood was collected into purple tubes containing the anticoagulant EDTA or heparin and centrifuged at 3000 rpm for 10 minutes at 2-8°C within 30 minutes of sample collection. The upper plasma layer was collected and stored in bulk at -80°C. Repeated freezing and thawing of samples was avoided. Note: Samples must be thoroughly centrifuged to avoid hemolysis and the presence of particles.

[0050] (2) Extraction of exosomes from peripheral plasma: Astrocyte-derived exosomes are extracted from plasma by immunosorbent assay as follows.

[0051] (2.1) Specifically, magnetic beads are coated with antibodies as follows.

[0052] (2.11) 100 μg of astrocyte surface glutamate transporter antibody (EAAT2-IgG) was collected and added to the ultrafiltration tube, followed by repeated washing.

[0053] (2.12) Add 8 to 10 μL of biotin to a final concentration of 10 mM to the IgG, and let stand at room temperature for 1 to 2 hours.

[0054] (2.13) Add all of the IgG-Biotin to the magnetic beads and shake them at room temperature in a 360° shaker for 2 hours.

[0055] (2.14) After antibody binding, the magnetic beads (IgG-Beads) were inverted upside down 20 times, placed on a magnetic rack, and left to stand for 1 minute. The supernatant was then aspirated, and 1 mL of PBS was added. This step was repeated three times, after which 1 mL of 0.1% PBSA (a mixture formed by dissolving PBS in BSA) was added.

[0056] (2.15) Add an appropriate amount of 10 mM biotin at a ratio of 2 mg beads:6 μL 10 mM biotin, and vortex shake at room temperature for 2 hours.

[0057] (2.2) Specifically, exosomes are extracted as follows.

[0058] (2.21) Remove the sample and add an appropriate amount of protease inhibitor (PIC). (2.22) Centrifuge at 2000G, 4°C, for 15 minutes to remove the supernatant, then centrifuge at 14000G, 4°C, for 30 minutes to remove the supernatant.

[0059] (2.23) Add 500 μL of the supernatant to 100 μL of IgG-Beads and rotate at 360°C for 20 hours at 4°C.

[0060] (2.3) Specifically, exosomes are eluted as follows.

[0061] (2.31) Add 70 μL of 0.1 M glycine (pH = 3.0) to the sample after binding to IgG-Beads, and shake at room temperature for 15 minutes.

[0062] (2.32) Place in a magnetic rack, let stand for 1 minute, discard the supernatant, add 5 μL of 1 mM Tris (pH = 7.0) to the supernatant, and repeat this step once.

[0063] (2.4) Specifically, exosome quantification and characterization are performed as follows.

[0064] (2.41) Using the NTA particle tracer, the results are shown in Figure 1. As can be seen in conjunction with Figure 1, the diameter of most EAAT2-highly expressing extracellular vesicles was between 100 and 150 nm, i.e., within the standard particle size range of exosomes.

[0065] (2.42) Using TEM scanning, the specific morphology is shown in Figure 2. As can be seen in conjunction with Figure 2, astrocyte-derived exosomes in plasma are cup-shaped or bidisc-shaped particles with a diameter of approximately 100 nm.

[0066] (2.43) Exosome-specific proteins were detected by Western blot, and the results are shown in Figure 3. As can be seen in conjunction with Figure 3, the exosome protein lysate expressing EAAT2 has a positive band at the standard position of the CD63 protein.

[0067] (3) The extraction of exosome proteins specifically includes the following steps:

[0068] (3.1) Add SDS-free lysate and 1x Cocktail containing EDTA at a final concentration to the exosome sample, leave on ice for 5 minutes, and then add DTT to a final concentration of 10 mM.

[0069] (3.2) Sonicate on ice for 2 minutes, centrifuge at 25,000 G and 4°C for 15 minutes, and remove the supernatant.

[0070] (3.3) Add DTT to a final concentration of 10 mM and leave in a water bath at 56°C for 1 hour.

[0071] (3.4) Add IAM to a final concentration of 55 mM and let stand in a dark room for 45 minutes.

[0072] (3.5) Centrifuge at 25,000 G and 4°C for 15 minutes, remove the supernatant, and use the supernatant liquid as the protein solution.

[0073] <Example 2> Proteomics techniques are used to quantitatively measure protein expression levels in biological samples and to screen for differentially expressed proteins.

[0074] 1. Mass spectrometry specifically includes the following steps:

[0075] 1) Protease hydrolysis: 100 μg of protein solution from each sample was taken, and 2.5 μg of trypsin enzyme was added at a protein:enzyme ratio of 40:1, and hydrolyzed at 37°C for 4 hours. The final enzyme product was desalted on a StrataX column and vacuum-dried.

[0076] 2) High pH RP method: Equal amounts of peptide fragments in all samples were diluted with A (5% ACN, pH 9.8) and then injected into a Shimadzu LC-20AD liquid chromatograph. Liquid analysis was performed using a 5 μm, 4.6 x 250 mm Gemini C18 column. Gradient elution was performed at a flow rate of 1 mL / min: 5% mobile phase B (95% ACN, pH 9.8) for 10 min, 5% to 35% mobile phase B for 40 min, 35% to 95% mobile phase B for 1 min, mobile phase B for 3 min, and 5% mobile phase B for 10 min. Elution peaks were monitored at a wavelength of 214 nm, and one set was collected every minute. This was combined with the chromatographic elution peak plot to form 10 portions, which were then dried at low temperature.

[0077] 3) DDA library construction and DIA quantitative detection (Nano-LC-MS / MS): Extracted peptide fragments were redissolved in A (2% ACN, 0.1% FA), centrifuged at 20,000 G for 10 minutes, and the supernatant was removed and sampled. Separation was performed on an UltiMate 3000 UHPLC. After the sample was concentrated and desalted by pumping it through a trap column, it was connected in series to a homemade C18 column (150 μm diameter C18 column, column diameter 1.8 μm, column length approximately 35 cm). Separation was performed at a rate of 500 nL / min using a high-efficiency gradient from 0 to 5 min (CAN, 98%, FA, 0.1%). Within 5 to 120 min, the flow rate of B-type fluid increased from 5% to 25%. Within 120 to 160 min, the flow rate of B-type fluid increased from 25% to 35%. Within 160-170 min, the flow rate of B-type fluid increased from 35% to 80%. Within 170-175 min, the flow rate of B-type fluid was 80%. Within 175-180 min, the flow rate of B-type fluid was 5%. The nanoliter fluid separation terminal was directly connected to a mass spectrometer and measured according to the following parameters:

[0078] 4) DDA Library Construction and Detection: Peptide fragments extracted by the liquid-phase method were ionized using a nanoESI ion source and fed into a tandem mass spectrometer Q-Exactive HF X (Thermo Fisher Scientific, San Jose, CA) for DDA detection. The main parameters were: ion source voltage set to 1.9 kV, primary mass analysis scans were performed in the mass-to-charge ratio range of 350-1500, resolution set to 120,000, and maximum ion injection time (MIT) was 100 ms. Secondary mass analysis mass spectral fragmentation was performed using HCD, fragmentation energy set to NCE28, resolution set to 30,000, maximum ion injection time set to 100 ms, and power exclusion set to 30 s. The initial m / z value of the secondary mass spectrum was 100. For the second fragmentation, 20 matrices with peak intensities of 5E4 or higher from 2+ to 6+ were selected. AGC is set to the strongest matrix of 3E64 in the first stage, AGC is set to 1E6, 2E5.

[0079] 5) DIA mass spectrometry detection: Peptide fragments separated by the liquid-phase method were ionized using a nanoESI ion source and fed into a tandem mass spectrometer Q-Exactive HF X (Thermo Fisher Scientific, San Jose, CA) in data independent acquisition (DIA) mode. The main parameters were: ion source voltage set to 1.9 kV, mass spectrometry scan from 1-400-1,250 m / z, resolution set to 120,000, maximum ion injection time set to 50 ms, 400-1,250 m / z divided into 45 windows, each successively opened and acquired. The fragmentation method was HCD, maximum ion injection time (MIT) set to automatic mode, and fragments were detected in the Orbitrap with a resolution of 30,000, fragmentation energy set to 22.5, 2527.5, and AGC set to 1E6.

[0080] 6) Data analysis: Using Spectronaut software, the spectra collected in the above steps are analyzed to achieve quantitative determination of each protein in the biological sample being measured. The differentially expressed proteins screened are shown in Table 2 below.

[0081] [Table 2]

[0082] As can be seen from Table 2, peripheral plasma and astrocyte-derived exosome proteomics identified 19 elevated proteins and 22 depressed proteins, with fold changes >2, P < 0.05.

[0083] Figure 4 also visualizes the differential expression results of 41 biomarkers in peripheral plasma and astrocyte-derived exosome proteomics identification of NMOSD patients and healthy individuals.

[0084] Example 3 Quantitative validation of differentially expressed proteins using ELISA technology In this example, we used enzyme-linked immunosorbent assay (ELISA) technology, which has high specificity and sensitivity, to test the proteins listed in Table 2 above, using a corresponding ELISA kit for each protein. Figure 5 shows that the concentrations of proteins such as APOE, SRGN, CD59, HABP2, PRG4, FGA, S100A8, LTF, CXCL10, CXCL12, and CFHR3 in the plasma of NMOSD patients were significantly higher than those in the plasma of healthy controls. This is consistent with the results of in vitro quantitative measurements using the proteomics technology described above. By determining the content of one or more proteins in a biological sample and comparing it with the normal content, disease prediction or the probability of disease recurrence can be monitored.

[0085] In this example, the relationship between the above protein concentrations and the severity of acute clinical disorder in NMOSD was further investigated. Specifically, the correlation between the content of each biomarker in patient plasma and the EDSS score was examined, and Figure 6 was obtained. As can be seen from Figure 6, increases in the concentrations of SRGN, FGA, PRG4, CXCL12, and S100A8 proteins promote the progression of acute clinical disorder in NMOSD, while CD59 protein plays a protective role in the acute phase of NMOSD.

[0086] Example 4 Prediction of NMOSD recurrence by protein combinations ROC analysis was performed using any combination of proteins such as APOE, SRGN, CD59, HABP2, PRG4, FGA, S100A8, LTF, CXCL10, CXCL12, and CFHR3, and the analysis results are shown in Tables 3 and 4 below.

[0087] [Table 3]

[0088] [Table 4]

[0089] The ROC curves for several combinations are shown in Figures 7 and 8. As can be seen from Tables 3 and 4 and Figures 7 and 8 above, the composite biomarker group designed using the present invention has a relatively high prediction accuracy for NMOSD disease and provides a basis for clinical treatment.

[0090] <Example 5> The present invention further discloses a kit for the above 41 biomarkers, which includes a detection agent for determining the level of the biomarker in a subject's body, an extraction agent for extracting exosome proteins from a biological sample, and a pretreatment agent and buffer for treating the biological sample, wherein the detection agent can quantitatively measure the level of the biomarker in the biological sample by proteomics technology, and the extraction agent, pretreatment agent, and buffer are all described in Examples 1 and 2.

[0091] Example 6 The present invention further discloses a system for monitoring the prediction or recurrence of neuromyelitis optica spectrum disorder, the system comprising the above-mentioned kit, and the system is used to detect the level of each biomarker in a biological sample provided by a subject, compare it with a normal or reference expression level, and feed back the comparison result to a system operator.

[0092] In summary, the biomarkers for predicting or monitoring recurrence of NMOSD designed in the present invention can be used to prepare reagents or kits for predicting or monitoring recurrence of neuromyelitis optica spectrum disorder, and can predict the risk of a subject suffering from neuromyelitis optica spectrum disorder or monitor the probability of recurrence of neuromyelitis optica spectrum disorder in patients or subjects. These biomarkers will contribute to a deeper understanding of the pathophysiology of NMOSD and provide new opportunities for diagnosis and prognosis, thereby improving clinical services for NMOSD patients.

Claims

1. 1. Use of a reagent for determining the level of a biomarker in a biological sample in the preparation of a kit for predicting or monitoring recurrence of neuromyelitis optica spectrum disorder, comprising: The biological samples are peripheral plasma and astrocyte-derived exosomes from patients with neuromyelitis optica spectrum disorder and healthy individuals; The use, characterized in that the biomarker is any one or a combination of two or more of APOE / FGA, APOE / CXCL10, APOE / CXCL12, APOE / S100A8, APOE / CFHL3, and APOE / CD59.

2. The use according to claim 1, characterized in that the biomarker is APOE / CXCL10 or APOE / S100A8.

3. The kit includes a detection agent for determining the level of each biomarker in a subject, the detection agent quantitatively measuring the level of the biomarker in the biological sample by a proteomics technique including a DIA mode, and specifically: 1) constructing a spectral library: the spectral library collects non-redundant, high-quality peptide fragment information of all detectable biological samples as a peptide fragment identification template for subsequent data analysis; 2) Acquiring measured biological sample data in DIA mode: using high-resolution mass spectrometry to simultaneously collect ions characteristic of each peptide fragment in terms of fragment mass number and retention time; 3) Data analysis: using Spectronaut software to deconvolute the spectra collected in step 2) to achieve an effective analysis of the quantitative determination of each protein in the biological sample to be measured.

4. 4. The use according to claim 3, wherein the spectral library is constructed using data collected by DDA detection of a biological sample of interest.

5. The use according to claim 1, characterized in that the kit further comprises an extractant for extracting exosome proteins in a biological sample, and a pretreatment agent for treating the biological sample.

6. The use according to claim 1 or 2, characterized in that the kit is used to monitor a subject's risk of developing neuromyelitis optica spectrum disorder within a prognosis of 5 years, and the subject is a mammal.

7. A kit for predicting or monitoring recurrence of neuromyelitis optica spectrum disorder, comprising: The method includes: detecting a detection agent for determining the level of a biomarker in a subject; extracting an exosome protein in a biological sample; and pretreatment and buffering agents for processing the biological sample. The biological samples are peripheral plasma and astrocyte-derived exosomes from patients with neuromyelitis optica spectrum disorder and healthy individuals; A kit for predicting or monitoring recurrence of neuromyelitis optica spectrum disorder, characterized in that the biomarkers are any one or a combination of two or more of APOE / FGA, APOE / CXCL10, APOE / CXCL12, APOE / S100A8, APOE / CFHL3, and APOE / CD59.

8. 1. A system for predicting or monitoring recurrence of neuromyelitis optica spectrum disorder, comprising: detecting the level of each biomarker in a biological sample provided by the subject and comparing it with a normal or reference expression level, and feeding back the comparison results to a system operator; The biological samples are peripheral plasma and astrocyte-derived exosomes from patients with neuromyelitis optica spectrum disorder and healthy individuals; A system for predicting or monitoring the recurrence of neuromyelitis optica spectrum disorder, characterized in that the biomarkers are any one or a combination of two or more of APOE / FGA, APOE / CXCL10, APOE / CXCL12, APOE / S100A8, APOE / CFHL3, and APOE / CD59.

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