RGMA gene polymorphism biomarker, kit and application

Abstract

The RGMA gene polymorphism biomarker, the kit and the application can be effectively used for disease course monitoring, prognosis evaluation and precise treatment decision of neuromyelitis optica pedigree diseases, and are rapid, convenient and accurate and reliable in result. Once a patient suffering from the neuromyelitis optica pedigree disease is diagnosed, accurate prediction can be provided for future outcome of the disease only through one-time detection, a basis is provided for clinical treatment decision, meanwhile, a certain basis is provided for follow-up fundamental research and clinical research, and the method has high application and research value. The invention relates to an RGMA (arginine-glycine-aspartic acid) gene polymorphism biomarker, which comprises rs725458-CC and rs4778099-AA (arginine-glycine-aspartic acid).

Description

Technical Field

[0001] The present invention relates to the technical field of biomedical testing, and in particular to an RGMA gene polymorphism biomarker, a kit for the RGMA gene polymorphism biomarker, and application of the RGMA gene polymorphism biomarker in predicting the disease activity of neuromyelitis optica spectrum disorders. Background Art

[0002] Neuromyelitis optica spectrum disorder (NMOSD) is a severe and highly relapsing autoimmune disease that primarily attacks the optic nerve, spinal cord, and brain tissue. The heterogeneity of NMOSD complicates its clinical management. This variability is reflected in the wide variation in severity of attacks, irregular time intervals between initial and subsequent relapses, and variable responses to immunotherapy. Current biomarkers, whether genetic, molecular, or cellular, are inadequate in accurately predicting disease activity and response to treatment. Therefore, the identification of effective biomarkers is crucial to develop personalized treatment strategies to improve outcomes for patients with NMOSD.

[0003] Repulsive guidance molecule A (RGMA) belongs to the family of membrane-associated glycophosphatidylinositol-linked glycoproteins and was originally discovered as an axon repulsion factor in the chicken retina-midbrain system. Recent studies have revealed the role of RGMA in neuroinflammatory processes, especially in central nervous system (CNS) diseases such as NMOSD. A 2022 study conducted in Japan showed that RGMA-mediated signaling affects the interaction of immune cells during NMOSD attacks, specifically by attracting and activating neutrophils to disrupt astrocyte function.

[0004] However, studies exploring the association between RGMA gene polymorphisms and NMOSD are still relatively scarce. Therefore, the present invention aims to fill this gap and investigate the association between RGMa gene polymorphisms and various aspects of NMOSD, including clinical characteristics, immune cell activation, and treatment response. Elucidate these associations, advance the understanding of the pathophysiology of NMOSD, and promote the development of predictive biomarkers to enable the application of personalized treatment strategies. Summary of the invention

[0005] In order to overcome the defects of the prior art, the technical problem to be solved by the present invention is to provide an RGMA gene polymorphism biomarker, which can serve as a key indicator for predicting early recurrence of neuromyelitis optica spectrum disorders. Combining these markers with clinical data can improve the accuracy of recurrence risk prediction, thereby achieving a more targeted treatment strategy.

[0006] The technical solution of the present invention is: the RGMA gene polymorphism biomarker includes rs725458-CC and rs4778099-AA.

[0007] A kit for RGMA gene polymorphism biomarkers is also provided, wherein the biomarkers include one of the following: rs725458-CC and rs4778099-AA, rs725458-TT, rs4778099-GG, rs725458-TT and rs4778099-GG, and the sample is serum.

[0008] Also provided is the application of RGMA gene polymorphism biomarker for predicting early relapse of neuromyelitis optica spectrum disorders.

[0009] The RGMA gene polymorphism biomarkers of the present invention can be used as key indicators for predicting early recurrence of neuromyelitis optica spectrum disorders. Combining these markers with clinical data can improve the accuracy of recurrence risk prediction, thereby achieving a more targeted treatment strategy for predicting neuromyelitis optica spectrum disorders. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] Figure 1 Shown is the distribution of RGMA genotypes in NMOSD patients and healthy controls, where NMOSD denotes neuromyelitis optica spectrum disorder; HC denotes healthy controls. Figure 1 A is the distribution of RGMA genotypes in NMOSD patients and healthy controls at rs725458. Figure 1 B: Distribution of RGMA genotypes in NMOSD patients and healthy controls at rs4778099.

[0011] Figure 2 Shown is the survival analysis of the time to first relapse according to different RGMA genotypes. Figure 2 A) and rs4778099-AA( Figure 2 Among patients with genotype B, 13 and 12, respectively, experienced their first relapse within 2 years of onset.

[0012] Figure 3 Shown is a multivariate analysis using a Cox proportional hazards model. Factor selection was optimized by multivariate Cox analysis and stepwise regression methods. The combined CC|AA genotype and clinical variables such as age at onset and onset symptoms (BON and CVO) showed statistically significant predictive value. BON is bilateral optic neuritis and CVO is periventricular organ syndrome.

[0013] Figure 4The nomogram is shown to quantify the results of the Cox multivariate analysis. The red line represents a patient with the rs725458|rs4778099 genotype of CC|AA, who is 18 years of age or older and whose symptoms do not involve the BON and CVO areas. The total score of this patient is 85.9+0+0+0=85.9, corresponding to a 1-year recurrence probability of 0.697, and a 3-year and 5-year recurrence probability of 0.973 and 0.989, respectively. DETAILED DESCRIPTION

[0014] This RGMA gene polymorphism biomarker includes rs725458-CC and rs4778099-AA.

[0015] A kit for RGMA gene polymorphism biomarkers is also provided, wherein the biomarkers include one of the following: rs725458-CC and rs4778099-AA, rs725458-TT, rs4778099-GG, rs725458-TT and rs4778099-GG, and the sample is serum.

[0016] Also provided is the application of RGMA gene polymorphism biomarker for predicting early relapse of neuromyelitis optica spectrum disorder NMOSD.

[0017] The RGMA gene polymorphism biomarkers of the present invention can serve as key indicators for predicting early recurrence of neuromyelitis optica spectrum disorders. Combining these markers with clinical data can improve the accuracy of recurrence risk prediction, thereby achieving more targeted treatment strategies for predicting neuromyelitis optica spectrum disorders.

[0018] Preferably, the RGMA gene polymorphism biomarker also includes rs725458-TT. The application of this RGMA gene polymorphism biomarker is to predict the occurrence of long spinal cord lesions.

[0019] Preferably, the RGMA gene polymorphism biomarker also includes rs4778099-GG. The application of this RGMA gene polymorphism biomarker is to predict a high AQP4-IgG negative rate.

[0020] Preferably, the RGMA gene polymorphism biomarker also includes rs725458-TT and rs4778099-GG. The application of this RGMA gene polymorphism biomarker is to predict the increase of CD3+T lymphocyte level.

[0021] The embodiments of the present invention are described in detail below.

[0022] 1. Participant recruitment and inclusion criteria

[0023] This study recruited 117 patients diagnosed with NMOSD in the Department of Neurology of the China-Japan Friendship Hospital between October 2015 and June 2024. The diagnosis of NMOSD was based on the 2015 international consensus diagnostic criteria, including AQP4-IgG positive and negative cases. AQP4-IgG status was determined by cell-based testing performed by an independent medical examination agency (AQP4-IgG kit; EUROIMMUN, Beijing, China). Patients included in the RGMA gene polymorphism analysis had to meet the following conditions: 1) diagnosed with NMOSD, regardless of whether AQP4-IgG was positive; 2) agreed to additional blood samples; 3) clinical follow-up time exceeded 6 months; 4) other diseases that might interfere with the results were excluded.

[0024] In addition, the study also recruited 100 healthy controls, matched with NMOSD patients in terms of gender, age and ethnicity. The blood samples of these controls were derived from specimens discarded during routine health examinations at the Physical Examination Center of the China-Japan Friendship Hospital.

[0025] This study has been approved by the Ethics Committee of the China-Japan Friendship Hospital (approval number: 2024-KY-242). All participants (or guardians of minor participants) signed informed consent before RGMA gene polymorphism typing.

[0026] 2. Clinical and radiological assessment of NMOSD patients

[0027] Clinical data for this study were obtained by retrospectively extracting electronic medical records from hospitals and outpatient clinics. Neurological function was assessed at key time points using the Expanded Disability Status Scale (EDSS), including during each attack, before and after each immunosuppressive therapy (IST), and at the final follow-up visit. Data collected included age at onset, sex, AQP4-IgG status, presence of other coexisting autoimmune diseases, type and severity of attack symptoms, time to first relapse (TFR) after onset, annual relapse rate (ARR), incidence of severe attacks (measured by EDSS score), and major attack symptoms (such as optic neuritis (ON), transverse myelitis (TM), or other symptoms). Severe attacks were defined as EDSS scores of more than 6 points or visual acuity less than 0.1 (logMAR eye chart). Relapse was defined as the onset of new symptoms or worsening symptoms at least 30 days after the previous attack and lasting more than 24 hours. Imaging characteristics included the incidence of longitudinally extended transverse myelitis (LETM) and the number of affected spinal cord segments (ISCS). LETM was defined as lesions in more than three or more spinal segments. In addition, the specific affected spinal cord segment and its location were recorded. If the cervical lesion extended to the medullary region on MRI, it was counted as one spinal cord segment. All clinical and imaging data were analyzed to identify possible associations with RGMA gene polymorphisms.

[0028] 3. Treatment intervention and outcome measurement

[0029] This study evaluated the efficacy of multiple immunosuppressive therapies (ISTs) used for at least six months in patients with NMOSD. The ISTs evaluated included oral corticosteroids, mycophenolate mofetil, rituximab, satelizumab, inelizumab, azathioprine, and cyclosporine. Several treatment outcome measures were used in the analysis: relapse rate after IST, TFR after IST, and change in EDSS score. The reduction in EDSS score was determined by calculating the difference between the EDSS score before treatment and the score after the most recent follow-up.

[0030] 4. RGMA gene polymorphism typing

[0031] Single nucleotide polymorphism (SNP) typing of the RGMA gene was performed in an independent medical laboratory by Sanger sequencing. Two SNPs, rs725458 and rs4778099, were selected based on their potential functional significance and related studies. Genomic DNA from peripheral blood samples was extracted using magnetic bead extraction reagent from Bestnovo (Jiangsu, China). The quality and concentration of DNA were assessed by Qubit 2.0 Fluorometer (Thermo Fisher Scientific, USA). The target region containing the SNP was amplified using 2× Taq MasterMix (Dye) reagent (Cat. No.: CW0682L, Conway Century Biotechnology Co., Ltd.) and the specified primers. The size of the amplified PCR product was detected by electrophoresis and then sequenced using a 3730XL Genetic Analyzer. The sequencing data were aligned with the reference sequence in the NCBI database (https: / / www.ncbi.nlm.nih.gov / ). According to the identified genotypes, the patients were classified into different subgroups for subsequent analysis.

[0032] 5. Acute phase immune cell profile analysis

[0033] Peripheral blood samples were obtained during each clinical episode, provided that the patient was not receiving acute immunotherapy (such as high-dose glucocorticoid pulse therapy, plasma exchange, or intravenous immunoglobulin therapy) and had no infection, recent surgery, or trauma-related stress. The analysis included the total white blood cell count and its differential components, such as neutrophils, lymphocytes, eosinophils, and basophils. In addition, a detailed analysis of the acute phase immune profile was performed by measuring the counts of CD3+ T cells and their subsets, CD19+ B cells, and NK cells.

[0034] White blood cell counts and their classification were determined using an automated hematology analyzer (e.g., Sysmex XE-5000). Lymphocyte subtypes were analyzed by flow cytometry. Peripheral blood mononuclear cells were separated by Ficoll-Paque density gradient centrifugation. These cells were stained with fluorescently labeled monoclonal antibodies against CD3, CD4, CD8, CD19, CD16, and CD56 to identify and quantify T cells (CD3+), helper T cells (CD4+), cytotoxic T cells (CD8+), B cells (CD19+), and NK cells (CD3-CD16+ / CD56+). This detailed immune cell profiling was used to investigate the association between acute phase immune cell populations and the RGMA genotype in NMOSD patients.

[0035] 6. Statistical Analysis

[0036] Descriptive statistics were used to summarize the epidemiological and clinical characteristics of the study population. Differences in SNP allele and genotype frequencies between NMOSD patients and healthy controls were analyzed by chi-square test or Fisher's exact test. Logistic regression models were used to evaluate the associations between SNP genotypes and clinical parameters, adjusting for potential confounding factors. The proportions of immune cell subsets in the acute and remission phases were compared using paired t-tests.

[0037] For survival analysis and Cox regression analysis, the survival package (version 3.5-7) was used for analysis, and the survminer package (version 0.4.9) was used to plot survival curves. Data statistics were managed using the tableone package (version 0.13.2), and stepwise regression analysis was performed using the boot package (version 1.3-28.1). Forest plots were generated using the forestploter package (version 1.1.2), while nomograms were created using the regplot package (version 1.1). All statistical analyses were performed using R version 4.0.3 or SPSS version 26.0, and the significance level was set at P value < 0.05.

[0038] The results of the study are as follows:

[0039] 1. RGMA genotype distribution

[0040] Genotype distribution of RGMA gene SNPs rs725458 and rs4778099 in NMOSD patients and healthy controls Figure 1 As shown. For SNP rs725458 ( Figure 1-A), the genotype frequencies of NMOSD patients were as follows: TT type 50 cases (43%), CT type 49 cases (42%), CC type 18 cases (15%). These frequencies were not significantly different from those of the healthy control group, where the distribution was: TT type 49 cases (49%), CT type 37 cases (37%), CC type 14 cases (14%). Similarly, for SNP rs4778099 ( Figure 1 -B), the genotype frequencies of NMOSD patients were: GG type 57 cases (49%), AG type 43 cases (37%), AA type 17 cases (14%). No significant difference was observed compared with the healthy control group, the latter distribution was: GG type 56 cases (56%), AG type 34 cases (34%), AA type 10 cases (10%).

[0041] 2. Association of RGMA genotype with clinical characteristics and immunotherapy response

[0042] The proportion of AQP4-IgG negative status in patients with rs4778099-GG genotype was significantly higher than that in patients with rs4778099-AG genotype (P<0.05). In addition, rs4778099-AA genotype carriers were more likely to have CVO syndrome at the first onset, and the difference was statistically significant compared with rs4778099-AG genotype carriers (P<0.05). In addition, the ON attack rate in patients with rs725458-TT genotype was significantly higher than that in patients with rs725458-CT genotype (P<0.05).

[0043] In terms of disease progression, the time to first relapse after onset was significantly shorter in patients with rs725458-CC genotype and rs4778099-AA genotype than in patients with rs725458-TT / CT genotype and rs4778099-GG / AG genotype (P<0.05). Regarding the annual relapse rate (ARR), the ARR of rs725458-CC genotype carriers was significantly lower than that of TT and CT genotype carriers (P<0.05). Similarly, the ARR of rs4778099-AA genotype carriers was significantly lower than that of GG genotype carriers (P<0.05).

[0044] Logistic regression analysis showed that the ARR of rs725458-CC genotype carriers was significantly lower than that of non-carriers (P=0.046, OR=8.119, 95% CI=1.053-63.723). In addition, the time of first relapse after onset of rs4778099-AA genotype carriers was significantly shorter than that of non-carriers (P=0.034, OR=0.182, 95% CI=0.038-0.880). The proportion of AQP4-IgG negative state in rs4778099-GG genotype carriers was significantly higher than that in non-carriers (P=0.016, OR=3.512, 95% CI=1.264-9.763).

[0045] 3. Association between RGMA genotype and imaging features

[0046] Patients with rs725458-TT genotype were more likely to show LETM, and the difference was statistically significant compared with CT or CC genotype (P<0.05). In addition, the number of affected spinal cord segments (ISCS) in patients with TT or CT genotype was significantly more than that in patients with CC genotype (P<0.05), and the lesions mainly affected the cervical and thoracic spinal cord. For rs4778099SNP, ISCS in GG and AG genotype carriers were significantly more than that in AA genotype carriers (P<0.05), and the lesions were mainly located in the cervical and thoracic spinal cord.

[0047] Further logistic regression analysis showed that the incidence of LETM in rs725458-TT genotype carriers was significantly higher than that in non-carriers (P=0.015, OR=0.307, 95% CI=0.119-0.792), while the incidence of LETM in rs725458-CC genotype carriers was significantly lower than that in non-carriers (P=0.040, OR=3.130, 95% CI=1.056-9.279).

[0048] 4. Association between RGMA genotype and the status of peripheral blood immune cells in the acute phase

[0049] In the 34 patients in our study, the data of peripheral blood immune cell profiles of patients with acute-stage NMOSD were analyzed. The study found that the levels of eosinophils, CD3+T lymphocytes and their subsets in patients carrying the rs725458-TT genotype and rs4778099-GG genotype were significantly higher than those in patients carrying the rs725458-CC and rs4778099-AA genotypes (P<0.05). In contrast, the levels of lymphocytes, especially CD3+T lymphocytes and their subsets, in patients carrying the rs725458-CT and rs4778099-AG genotypes were significantly higher than those in patients carrying the rs725458-CC and rs4778099-AA genotypes (P<0.05).

[0050] 5. Survival analysis of the time to first relapse in NMOSD patients

[0051] Survival analysis ( Figure 2 ) showed that the first relapse time of rs725458-CC (A) and rs4778099-AA (B) genotype carriers was significantly earlier than that of rs725458-TT / CT (P=0.0253) and rs4778099-GG / AG (P=0.0156) genotype carriers. All patients carrying rs725458-CC and rs4778099-AA genotypes experienced their first relapse within 2 years after onset.

[0052] 6. Multivariate Cox proportional hazards model analysis

[0053] Table 1 summarizes the significant variables identified by univariate Cox analysis, including rs725458-CC and rs4778099-AA genotypes, sex, age at onset, symptoms at onset (such as BON, CVO, or mixed symptoms), and EDSS score at onset. Univariate analysis showed that both rs725458-CC and rs4778099-AA genotypes were significant risk factors for early relapse. Due to the high correlation between these genotypes, they were combined into a single variable, rs725458|rs4778099, divided into CC|AA and other categories.

[0054] Table 1. Cox univariate analysis of time to first relapse

[0055]

[0056] In Table 1 , EDSS is the Expanded Disability Status Scale.

[0057] These variables were included in the multivariate Cox proportional hazards model to evaluate their combined effects on early recurrence. The model was optimized using stepwise regression, and the significant variables finally determined included: rs725458|rs4778099 combined genotype (HR=2.36, 95% CI [1.25-4.44], P=0.008), age of onset (HR=2.71, 95% CI [1.32-5.57], P=0.007), first symptom of BON (HR=2.71, 95% CI [1.26-5.81], P=0.010) and first symptom of CVO (HR=2.21, 95% CI [1.38-3.55], P=0.001), as shown in Table 1. Figure 3 shown.

[0058] In order to more clearly show the relationship between these significant variables, we used a nomogram to present the results of multivariate analysis ( Figure 4 ). For example, the nomogram shows a patient with rs725458|rs4778099 genotype "CC|AA", aged 18 years or older, and no symptoms of CVO or BON. The patient's total score is 85.9+0+0+0=85.9, corresponding to a 1-year recurrence probability of 0.697, and 3-year and 5-year recurrence probabilities of 0.973 and 0.989, respectively.

[0059] In summary, this study shows that RGMA gene polymorphisms, especially rs725458-CC and rs4778099-AA genotypes, are important predictors of early relapse in NMOSD. Combining these genetic markers with clinical variables such as age of onset and symptoms of onset significantly improves the accuracy of early relapse prediction and provides a reliable framework for risk assessment. This comprehensive assessment method helps identify high-risk patients, thereby enabling earlier and more personalized interventions, which may improve clinical outcomes and reduce relapse rates.

[0060] The above description is only a preferred embodiment of the present invention and does not limit the present invention in any form. Any simple modification, equivalent changes and modifications made to the above embodiments based on the technical essence of the present invention are still within the protection scope of the technical solution of the present invention.

Claims

1. RGMA gene polymorphism biomarker, characterized by: It includes rs725458-CC and rs4778099-AA.

2. The RGMA gene polymorphism biomarker according to claim 1, characterized in that: It also includes rs725458-TT.

3. The RGMA gene polymorphism biomarker according to claim 1, characterized in that: It also includes rs4778099-GG.

4. The RGMA gene polymorphism biomarker according to claim 1, characterized in that: It also includes rs725458-TT and rs4778099-GG.

5. A kit for RGMA gene polymorphism biomarker, characterized in that: The biomarkers include one of the following: rs725458-CC and rs4778099-AA, rs725458-TT, rs4778099-GG, rs725458-TT and rs4778099-GG, and the sample is serum.

6. The use of the RGMA gene polymorphism biomarker according to claim 1, characterized in that: For predicting early relapse of neuromyelitis optica spectrum disorder (NMOSD).

7. The use of the RGMA gene polymorphism biomarker according to claim 2, characterized in that: Used to predict the extent of long spinal cord lesions.

8. The use of the RGMA gene polymorphism biomarker according to claim 3, characterized in that: Used to predict high AQP4-IgG negative rate.

9. The use of the RGMA gene polymorphism biomarker according to claim 4, characterized in that: Used to predict elevated levels of CD3+T lymphocytes.

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