Use of nk cell-specific gene detection reagent in preparation of nmosd diagnostic kit

Abstract

The application provides use of an NK cell specific gene detection reagent in preparation of an NMOSD diagnostic kit, and belongs to the technical field of biological detection. The application first finds that 20 specific genes in human peripheral blood NK cells can be used as biomarkers for diagnosing neuromyelitis optica spectrum disorders. After the expression amounts of the 20 specific genes are detected, the random forest model is used for analysis, so that early screening of neuromyelitis optica spectrum disorders can be realized. The application avoids the difficulty and risk of using cerebrospinal fluid as a detection sample, simplifies the operation, reduces the diagnosis cost, and has a shorter time required for antibody detection compared to other methods, so that neuromyelitis optica spectrum disorders can be quickly diagnosed, the patient can start treatment earlier, the prognosis of the patient is improved, the patient has high acceptance, and the application has practical application value.

Description

Technical Field

[0001] This invention belongs to the field of biological detection technology, specifically relating to the use of NK cell-specific gene detection reagents in the preparation of NMOSD diagnostic kits. Background Technology

[0002] Neuromyelitis optica spectrum disorder (NMOSD) is an immune-mediated chronic inflammatory disease of the central nervous system, characterized by demyelination, axonal damage, and neuronal loss as the main pathological changes in the central nervous system. The incidence is significantly higher in women than in men. The one-year relapse rate of NMOSD is approximately 60%, and the five-year relapse rate is approximately 90%. Most patients experience residual neurological symptoms, severely impacting their quality of life. NMOSD is characterized by high relapse rates, high disability rates, and a heavy disease burden. The primary pathogenic mechanism is the selective binding of aquaporin-4 antibody (AQP4-ab) to AQP4, leading to astrocyte dysfunction and loss. Lesions are mainly located in the optic nerve and spinal cord, but can also affect the central nervous system through a compromised blood-brain barrier.

[0003] According to the Chinese Guidelines for the Diagnosis and Treatment of Neuromyelitis Optic Spectrum Disorders (2021 Edition), the current diagnosis of NMOSD mainly relies on a comprehensive assessment of antibody testing, clinical manifestations, and imaging examinations. Among these, the diagnosis is highly dependent on antibody testing results. However, most hospitals lack the facilities to perform antibody testing and must send samples to external laboratories. The results take a considerable amount of time to become clear, causing a delay in diagnosis and potentially affecting the patient's ability to begin treatment and their prognosis.

[0004] Chinese invention patent application CN118169396A discloses that sBCMA levels in cerebrospinal fluid can be used as a biomarker for the diagnosis of antibody-mediated central nervous system autoimmune diseases such as NMOSD. However, this method uses cerebrospinal fluid as the test sample, requiring invasive procedures such as lumbar puncture, making sample acquisition more difficult compared to peripheral blood. Finding a biomarker with a simpler sample acquisition method for the diagnosis of NMOSD is of great significance for the clinical treatment of NMOSD.

[0005] Natural killer (NK) cells are important immune cells in the body. They originate from bone marrow lymphoid stem cells, and their differentiation and development depend on the bone marrow and thymus microenvironment. They are mainly distributed in the bone marrow, peripheral blood, liver, spleen, lungs, and lymph nodes. They are closely related to anti-tumor, anti-viral infection, and immune regulation. Currently, NK cell transplantation combined with stem cell transplantation is being used clinically to treat acute leukemia. Previous reports have confirmed significant differences in NK cell counts between healthy individuals and NMOSD patients, and NK cells also have some value in differentiating NMOSD from multiple sclerosis (MS). However, changes in NK cell-specific gene expression in NMOSD patients have not yet been reported. Summary of the Invention

[0006] In order to address the problems existing in the prior art, the purpose of this invention is to provide the use of NK cell-specific gene detection reagents in the preparation of NMOSD diagnostic kits.

[0007] This invention provides the use of reagents for detecting NK cell-specific gene expression in the preparation of NMOSD diagnostic kits, wherein the specific genes are one or more of MT-CO1, MT-CO3, TMSB10, HLA-B, RPLP1, MT-RNR1, PTMA, EEF1A1, RPL34, CST7, RPS27A, RPS18, RPL10, HLA-A, RPL30, CFL1, MT-ND3, FTL, TXNIP, and RPL3.

[0008] Furthermore, the specific genes are MT-CO1, MT-CO3, TMSB10, HLA-B, RPLP1, MT-RNR1, PTMA, EEF1A1, RPL34, CST7, RPS27A, RPS18, RPL10, HLA-A, RPL30, CFL1, MT-ND3, FTL, TXNIP, and RPL3.

[0009] Furthermore, the reagent for detecting NK cell-specific gene expression is a reagent for detecting heterogeneous NK cell gene expression in human peripheral blood.

[0010] Furthermore, the reagents for detecting specific gene expression in NK cells are qPCR detection reagents, bulk-RNA detection reagents, and reagents used in single-cell sequencing methods.

[0011] The present invention also provides an NMOSD diagnostic kit, which includes reagents for detecting NK cell-specific gene expression; said specific gene is one or more selected from MT-CO1, MT-CO3, TMSB10, HLA-B, RPLP1, MT-RNR1, PTMA, EEF1A1, RPL34, CST7, RPS27A, RPS18, RPL10, HLA-A, RPL30, CFL1, MT-ND3, FTL, TXNIP and RPL3;

[0012] Preferably, the specific genes are MT-CO1, MT-CO3, TMSB10, HLA-B, RPLP1, MT-RNR1, PTMA, EEF1A1, RPL34, CST7, RPS27A, RPS18, RPL10, HLA-A, RPL30, CFL1, MT-ND3, FTL, TXNIP, and RPL3;

[0013] More preferably, the reagent for detecting NK cell-specific gene expression is a reagent for detecting heterogeneous NK cell gene expression in human peripheral blood.

[0014] Furthermore, the reagents for detecting specific gene expression in NK cells are qPCR detection reagents, bulk-RNA detection reagents, and reagents used in single-cell sequencing methods.

[0015] The present invention also provides an NMOSD diagnostic system, the system comprising the following parts:

[0016] Input module: Used to input the expression levels of NK cell-specific genes into the random forest model. The specific genes are one or more of the following: MT-CO1, MT-CO3, TMSB10, HLA-B, RPLP1, MT-RNR1, PTMA, EEF1A1, RPL34, CST7, RPS27A, RPS18, RPL10, HLA-A, RPL30, CFL1, MT-ND3, FTL, TXNIP, and RPL3.

[0017] Prediction module: The random forest model is used to process NK cell-specific gene expression data to obtain NMOSD diagnostic results;

[0018] Output module: Used to output NMOSD diagnostic results.

[0019] Furthermore, the specific genes are MT-CO1, MT-CO3, TMSB10, HLA-B, RPLP1, MT-RNR1, PTMA, EEF1A1, RPL34, CST7, RPS27A, RPS18, RPL10, HLA-A, RPL30, CFL1, MT-ND3, FTL, TXNIP, and RPL3.

[0020] Furthermore, the NK cell-specific gene expression level is the heterogeneous gene expression level of NK cells in human peripheral blood.

[0021] The present invention also provides a computer-readable storage medium having a computer program stored thereon for implementing the aforementioned NMOSD diagnostic system.

[0022] This invention is primarily applied to human peripheral blood. Single-cell sequencing technology was used to compare the expression levels of 20 specific genes (MT-CO1, MT-CO3, TMSB10, HLA-B, RPLP1, MT-RNR1, PTMA, EEF1A1, RPL34, CST7, RPS27A, RPS18, RPL10, HLA-A, RPL30, CFL1, MT-ND3, FTL, TXNIP, RPL3) in NK cells of NMOSD patients with those in the healthy control group. Based on this, this invention provides reliable evidence that the expression levels of specific genes in NK cells of peripheral blood can serve as molecular markers for the diagnosis of NMOSD.

[0023] The expression levels of NK cell-specific genes MT-CO1, MT-CO3, TMSB10, HLA-B, MT-RNR1, EEF1A1, CST7, RPS27A, RPL10, HLA-A, RPL30, CFL1, MT-ND3, FTL, TXNIP, and RPL3 are decreased in NMOSD patients; and / or the expression levels of NK cell-specific genes PTMA, RPLP1, RPL34, and RPS18 are increased.

[0024] The key to this invention is that it has been determined that the expression levels of 20 specific genes of NK cells in human peripheral blood are significantly associated with the risk of NMOSD. Therefore, NMOSD can be diagnosed by detecting the expression levels of these 20 specific genes of NK cells in human peripheral blood. As for the specific methods for detecting the expression levels of these 20 specific genes of NK cells in human peripheral blood, various methods disclosed in the prior art can be used.

[0025] The present invention has achieved the following beneficial effects:

[0026] This invention is the first to discover 20 specific genes in human peripheral blood NK cells that can serve as biomarkers for diagnosing neuromyelitis optica spectrum disorders (NMO). By detecting the expression levels of these 20 specific genes and analyzing the results using a random forest model, early screening for NMO can be achieved. This invention avoids the difficulties and risks associated with using cerebrospinal fluid as a test sample, simplifies the procedure, reduces diagnostic costs, and requires less time than antibody testing. It enables rapid diagnosis of NMO, allowing patients to begin treatment earlier, improving prognosis. It has high patient acceptance and practical application value.

[0027] This invention can be used to guide the screening of patients with neuromyelitis optica spectrum disorders in clinical practice, to study the pathogenesis of neuromyelitis optica spectrum disorders, and to explore potential biological targets for the treatment of neuromyelitis optica spectrum disorders, and has broad application prospects.

[0028] Obviously, based on the above description of the present invention, and according to common technical knowledge and conventional methods in the field, various other modifications, substitutions or alterations can be made without departing from the basic technical concept of the present invention.

[0029] The following detailed embodiments further illustrate the above-described content of the present invention. However, this should not be construed as limiting the scope of the present invention to the following embodiments. All technologies implemented based on the above-described content of the present invention fall within the scope of the present invention. Attached Figure Description

[0030] Figure 1 A schematic diagram of single-cell UMAP results for patients with neuromyelitis optica spectrum disorder and healthy controls: A is the clusters plot after single-cell dimensionality reduction clustering; B is the celltype plot after single-cell annotation.

[0031] Figure 2 This is a volcano diagram of differentially expressed genes in NK cells.

[0032] Figure 3 This is a graph showing the expression levels of the top 20 differentially expressed genes in NK cells.

[0033] Figure 4 ROC curve analysis and area under the curve (AUC) results of the top 20 differentially expressed genes on NK cells for the diagnosis of neuromyelitis optica spectrum disorders.

[0034] Figure 5 ROC curve analysis and area under the curve (AUC) results of 20 randomly selected differentially expressed genes in NK cells for the diagnosis of neuromyelitis optica spectrum disorders. Detailed Implementation

[0035] All raw materials and equipment used in this invention are known products, obtained by purchasing commercially available products. Experimental methods without specified conditions are conventional methods and conditions well-known in the art, or according to the conditions recommended by the instrument manufacturer.

[0036] The main technologies involved in the specific implementation are as follows:

[0037] I. Extraction of peripheral blood mononuclear cells (PBMCs)

[0038] (1) Collect 20 ml of peripheral blood from healthy controls and patients with neuromyelitis optica spectrum disorder using blood collection tubes with EDTA.

[0039] (2) Centrifugation: 3100 rpm, time 10 min, speed increase: 9, speed decrease: 7;

[0040] (3) Remove the supernatant, inject an equal volume of physiological saline to the blood cells, mix well, add the mixed liquid to a centrifuge tube with Ficoll separation solution, centrifuge: 1400g, 25min, up 9, down 0;

[0041] (4) Transfer the intermediate layer to another centrifuge tube, add physiological saline to 15 ml, wash, and mix with a Pasteur tube. Centrifuge: 600 g, 10 min, 9 ml increments, 7 ml decrement;

[0042] (5) Remove the supernatant, add red blood cell lysis buffer to 10 ml, and centrifuge again: 300 g, 10 min, 9 ml up, 7 ml down.

[0043] (6) After removing the supernatant, add physiological saline to 15 ml and wash 3 times to obtain PBMC.

[0044] II. Single-cell sequencing

[0045] Reagents: Phosphate-buffered saline (PBS); red blood cell lysis buffer; Ficoll separation buffer; physiological saline; trypan blue staining solution; Ampure XP purification magnetic beads.

[0046] Instruments: Cell counter; PCR instrument; magnetic rack; high-speed refrigerated centrifuge; constant temperature shaking metal bath; 10xGenomics system; Illumina next-generation sequencer.

[0047] Experimental steps:

[0048] Peripheral venous blood was collected from patients with acute neuromyelitis optica spectrum disorder and from a sex- and age-matched healthy control group.

[0049] (1) Peripheral blood PBMCs were extracted using the above method, and the cell viability of each sample was confirmed to be approximately 90% by trypan blue. Sample cultures with high cell viability were selected for library preparation.

[0050] (2) Single-cell RNA-seq (transcriptome sequencing technology) library preparation and sequencing: immediately after cell suspension preparation, library preparation is performed using the 10x Chromium Single Cell 3' Library Kit and Chromium Single Cell 30v3 Reagents (from 10x Genomics). Approximately 11,000 cells are loaded per sample. Subsequent sequencing is performed on the Illumina platform (high-throughput sequencing platform) according to the manufacturer's instructions.

[0051] (3) Preprocessing and quality control of single-cell RNA-seq data: The fastq (nucleic acid sequence) files generated by sequencing are processed using Cellranger (a single-cell toolbox, version 5.0.1), which is run under default parameters for alignment, filtering, barcode demultiplexing and UMI counting. The human reference genome GRCh38 is used for alignment. Quality control is performed using the R package Seurat (an integrated single-cell data analysis software, version 4.3.0.1), with the following criteria: ① 800 < nFeature-RNA (number of genes detected in each cell) < 2500; ② 1000 ≤ nCount-RNA (total number of molecules detected in the cell) ≤ 20000; ③ percentage of mitochondrial genes < 13%. Mitochondrial genes and ribosomal genes are removed. After filtering, cells are screened from all samples, and further analysis is performed using the R package "Seurat" with default settings.

[0052] Example 1: Study on diagnosis of neuromyelitis optica spectrum disorders using specific genes in peripheral blood NK cells

[0053] 1. Screening of specific genes in peripheral blood NK cells

[0054] In the previous work, the inventor extracted monocytes from peripheral venous blood of patients with acute phase neuromyelitis optica spectrum disorders and gender- and age-matched healthy control subjects, and found 20 NK cell-specific genes (MT-CO1, MT-CO3, TMSB10, HLA-B, RPLP1, MT-RNR1, PTMA, EEF1A1, RPL34, CST7, RPS27A, RPS18, RPL10, HLA-A, RPL30, CFL1, MT-ND3, FTL, TXNIP, RPL3) related to the diagnosis of neuromyelitis optica spectrum disorders through single-cell sequencing and data analysis. The specific single-cell RNA-seq data analysis method is as follows:

[0055] (1) RNA-seq data analysis: All single-cell RNA-seq data were analyzed together using the "Seurat" R package to identify NK cell marker genes. Five filtering conditions were applied to retain high-quality RNA-seq data: removing genes expressed in fewer than 3 single cells, removing cells expressing more than 3000 but less than 100 genes, removing cells with more than 20% mitochondrial genes, removing cells with more than 50% ribosomal genes, and removing cells with more than 1% erythrocyte genes. Then, the RNA-seq data were normalized using the "Log Normalize" method of the "Normalize Data" function. The top 2000 highly variable genes were identified using the "FindVariableFeatures" function (the function filters based on the relationship between the mean and variance of gene expression, reflecting the degree of differential expression in different cells), and the expression levels of highly variable genes were normalized using the "ScaleData" function. Then, principal component analysis was performed to reduce the dimensionality of the expression levels of the top 2000 highly variable genes in the RNA-seq data using the "RunPCA" function. ElbowPlot was used to identify significant principal components (PCs). Clustering was performed using Find Clusters at a resolution of 1. The principal components were then used to generate a two-dimensional representation using Unified Manifold Approximation and Projection (UMAP). Differentially expressed genes (DEGs) for each cluster were computed using the 'FindAllMarkers' function from the 'Seurat' package via the Wilcoxon test. To identify marker genes for each cluster, corrected p-values ​​<0.05 and |log2(fold change)|>1 were used as thresholds. For cluster annotation, GNLY and KLRD1 were used as marker genes for NK cells. A schematic diagram of single-cell UMAP results from patients with neuromyelitis optica spectrum disorder and healthy controls is shown below. Figure 1 As shown.

[0056] (2) Identifying differentially expressed genes between the two groups: The R package 'Findmarkers' was used to identify DEGs. Differentially expressed genes were screened using a threshold of log2 fold change (FC) = 0.25, a minimum PCT of 0.1, and a corrected P < 0.05. The expression levels of these differentially expressed genes were then extracted. The volcano plot of differentially expressed genes in NK cells is shown below. Figure 2 As shown in the figure. The expression levels of the top 20 differentially expressed genes in NK cells are as follows: Figure 3As shown. The top 20 differentially expressed genes in NK cells are MT-CO1, MT-CO3, TMSB10, HLA-B, RPLP1, MT-RNR1, PTMA, EEF1A1, RPL34, CST7, RPS27A, RPS18, RPL10, HLA-A, RPL30, CFL1, MT-ND3, FTL, TXNIP, and RPL3. Compared with healthy controls, NMOSD patients showed decreased expression of the NK cell-specific genes MT-CO1, MT-CO3, TMSB10, HLA-B, MT-RNR1, EEF1A1, CST7, RPS27A, RPL10, HLA-A, RPL30, CFL1, MT-ND3, FTL, TXNIP, and RPL3, while PTMA, RPLP1, RPL34, and RPS18 were increased. These 20 genes are considered as NK cell-specific genes for diagnosing neuromyelitis optica spectrum disorders.

[0057] 2. Construction of a diagnostic model for neuromyelitis optica spectrum disorders

[0058] Based on the approved ethical review protocol and diagnostic criteria, six patients with neuromyelitis optica spectrum disorder and six age- and sex-matched healthy individuals were selected, and 20 ml of peripheral venous blood was collected from each. The severity of the disease was assessed using the EDSS score. This study fully complied with ethical standards for human trials and was approved by the Ethics Committee of Chongqing Medical University. Informed consent was obtained from all participants before the study. Peripheral venous blood samples were used to extract PBMCs according to the above method, and single-cell RNA-seq data were obtained using the above single-cell sequencing method to determine the expression levels of the 20 NK cell-specific genes. Simultaneously, the expression levels of 20 randomly selected differentially expressed genes (RASSF4, IFI44L, TWISTNB, MMP25.AS1, CXCR2, RHOB, CUTALP, SNORD3A, HIPK2, DDX60, H3F3AP4, IFI44, KIR2DL3, PLSCR1, RNU5F.1, OAS1, TYMP, MX2, RPL10P9, IGHG3) were detected.

[0059] The expression levels of the top 20 differentially expressed genes in NK cells (MT-CO1, MT-CO3, TMSB10, HLA-B, RPLP1, MT-RNR1, PTMA, EEF1A1, RPL34, CST7, RPS27A, RPS18, RPL10, HLA-A, RPL30, CFL1, MT-ND3, FTL, TXNIP, RPL3) and 20 randomly selected differentially expressed genes (RASSF4, IFI44L, TWISTNB, MMP25.AS1, CXCR2, RHOB, CUTALP, SNORD3A, HIPK2, DDX60, H3F3AP4, IFI44, KIR2DL3, PLSCR1, RNU5F.1, OAS1, TYMP, MX2, RPL10P9, IGHG3) were incorporated into a random forest model, serving as the experimental group and the control group, respectively. Patients with neuromyelitis optica spectrum disorder and healthy individuals were randomly assigned to two groups (4 for the training set and 2 for the test set) to train and test a random forest model, resulting in a random forest model for diagnosing neuromyelitis optica spectrum disorder. A cutoff of 0.5 was used between the training and test sets. Receiver operating characteristic (ROC) curve analysis was employed to evaluate the diagnostic value of the model and key genes.

[0060] 3. Results

[0061] Figure 4 ROC curve analysis and area under the curve (AUC) results of the top 20 differentially expressed genes on NK cells for the diagnosis of neuromyelitis optica spectrum disorders. Figure 4 It can be seen that the AUC of the ROC curve analysis for diagnosing neuromyelitis optica spectrum disorders using a diagnostic model constructed using the top 20 differentially expressed genes on NK cells is 0.8. Figure 5 The results showed that the AUC of the ROC curve analysis for diagnosing neuromyelitis optica spectrum disorder (NMDS) using a diagnostic model constructed from 20 randomly selected differentially expressed NK cell genes was 0.73. This indicates that using the top 20 differentially expressed NK cell genes as a diagnostic model has a good diagnostic effect on NMDS and can distinguish between NMDS patients and healthy individuals.

[0062] Example 2: Detection method for neuromyelitis optica spectrum disorder using a diagnostic system based on specific genes in peripheral blood NK cells.

[0063] First, peripheral blood was collected from the patients to be tested using conventional methods in the field, and NK cells were isolated from them. The expression levels of 20 specific genes (MT-CO1, MT-CO3, TMSB10, HLA-B, RPLP1, MT-RNR1, PTMA, EEF1A1, RPL34, CST7, RPS27A, RPS18, RPL10, HLA-A, RPL30, CFL1, MT-ND3, FTL, TXNIP, RPL3) in the NK cells were detected using an Illumina next-generation sequencer.

[0064] Then, the expression levels of the above 20 specific genes were input into the random forest model of Example 1 to obtain the diagnostic results of neuromyelitis optica spectrum disease.

[0065] The diagnostic system for neuromyelitis optica spectrum disorders of the present invention comprises the following parts:

[0066] Input module: Used to input the expression levels of NK cell-specific genes into the random forest model. The specific genes are MT-CO1, MT-CO3, TMSB10, HLA-B, RPLP1, MT-RNR1, PTMA, EEF1A1, RPL34, CST7, RPS27A, RPS18, RPL10, HLA-A, RPL30, CFL1, MT-ND3, FTL, TXNIP, and RPL3.

[0067] Prediction module: The random forest model is used to process NK cell-specific gene expression data to obtain NMOSD diagnostic results;

[0068] Output module: Used to output NMOSD diagnostic results.

[0069] In summary, this invention is the first to discover 20 specific genes in human peripheral blood NK cells that can serve as biomarkers for diagnosing neuromyelitis optica spectrum disorders (NMO). By detecting the expression levels of these 20 specific genes and analyzing the results using a random forest model, early screening for NMO can be achieved. This invention avoids the difficulties and risks associated with using cerebrospinal fluid as a test sample, simplifies the procedure, reduces diagnostic costs, and requires less time than antibody testing. It enables rapid diagnosis of NMO, allowing patients to begin treatment earlier, improving prognosis. It has high patient acceptance and practical application value.

Claims

1. The use of reagents for detecting NK cell-specific gene expression in the preparation of NMOSD diagnostic kits, characterized in that: The specific gene is one or more of the following: MT-CO1, MT-CO3, TMSB10, HLA-B, RPLP1, MT-RNR1, PTMA, EEF1A1, RPL34, CST7, RPS27A, RPS18, RPL10, HLA-A, RPL30, CFL1, MT-ND3, FTL, TXNIP, and RPL3.

2. The use according to claim 1, characterized in that: The specific genes are MT-CO1, MT-CO3, TMSB10, HLA-B, RPLP1, MT-RNR1, PTMA, EEF1A1, RPL34, CST7, RPS27A, RPS18, RPL10, HLA-A, RPL30, CFL1, MT-ND3, FTL, TXNIP, and RPL3.

3. The use according to claim 1 or 2, characterized in that: The reagent for detecting NK cell-specific gene expression is a reagent for detecting heterogeneous gene expression in NK cells in human peripheral blood.

4. The use according to claim 1 or 2, characterized in that: The reagents used to detect specific gene expression in NK cells include qPCR reagents, bulk-RNA reagents, and reagents used in single-cell sequencing methods.

5. An NMOSD diagnostic kit, characterized in that: It includes reagents for detecting NK cell-specific gene expression; said specific gene is one or more of MT-CO1, MT-CO3, TMSB10, HLA-B, RPLP1, MT-RNR1, PTMA, EEF1A1, RPL34, CST7, RPS27A, RPS18, RPL10, HLA-A, RPL30, CFL1, MT-ND3, FTL, TXNIP, and RPL3; Preferably, the specific genes are MT-CO1, MT-CO3, TMSB10, HLA-B, RPLP1, MT-RNR1, PTMA, EEF1A1, RPL34, CST7, RPS27A, RPS18, RPL10, HLA-A, RPL30, CFL1, MT-ND3, FTL, TXNIP, and RPL3; More preferably, the reagent for detecting NK cell-specific gene expression is a reagent for detecting heterogeneous NK cell gene expression in human peripheral blood.

6. The NMOSD diagnostic kit according to claim 5, characterized in that: The reagents used to detect specific gene expression in NK cells include qPCR reagents, bulk-RNA reagents, and reagents used in single-cell sequencing methods.

7. An NMOSD diagnostic system, characterized in that: The system includes the following components: Input module: Used to input the expression levels of NK cell-specific genes into the random forest model. The specific genes are one or more of the following: MT-CO1, MT-CO3, TMSB10, HLA-B, RPLP1, MT-RNR1, PTMA, EEF1A1, RPL34, CST7, RPS27A, RPS18, RPL10, HLA-A, RPL30, CFL1, MT-ND3, FTL, TXNIP, and RPL3. Prediction module: The random forest model is used to process NK cell-specific gene expression data to obtain NMOSD diagnostic results; Output module: Used to output NMOSD diagnostic results.

8. The NMOSD diagnostic system according to claim 7, characterized in that: The specific genes are MT-CO1, MT-CO3, TMSB10, HLA-B, RPLP1, MT-RNR1, PTMA, EEF1A1, RPL34, CST7, RPS27A, RPS18, RPL10, HLA-A, RPL30, CFL1, MT-ND3, FTL, TXNIP, and RPL3.

9. The NMOSD diagnostic system according to claim 7, characterized in that: The NK cell-specific gene expression level refers to the heterogeneous gene expression level of NK cells in human peripheral blood.

10. A computer-readable storage medium, characterized in that: It stores a computer program for implementing the NMOSD diagnostic system as described in any one of claims 7 to 9.

Images