Teenager myasthenia gravis eye muscle type-whole body type transformation risk prediction marker and application thereof

Abstract

The invention provides a teenager myasthenia gravis eye muscle type-whole body type transformation risk prediction marker and application thereof. Through GWAS analysis and research, two risky gene regions related to the transformation of the adolescent pure eye muscle type myasthenia gravis to the whole body type are obtained, and single nucleotide polymorphism (SNP) sites with statistically significant association are successfully identified.

Description

Technical Field

[0001] This invention relates to predictive biomarkers for the risk of conversion from ocular to systemic myasthenia gravis in adolescents and their applications, belonging to the field of biomedicine. Background Technology

[0002] Childhood and adolescent myasthenia gravis (MG) are collectively referred to as juvenile myasthenia gravis (JMG). The age of onset is generally considered to be before 18 years of age, and the incidence rate shows significant racial differences. In Asian populations, particularly in China, the number of JMG patients is 50%, while the rate is relatively lower in other countries.

[0003] Previous cohort studies have shown that pediatric myasthenia gravis patients generally have milder symptoms and better prognoses. Patients with congenital myasthenia gravis (CMG) onset during puberty tend to have more severe disease and a relatively worse prognosis than those with pre-pubertal onset. Furthermore, adolescents with ocular myasthenia gravis (OMG) who have older onset, thymic hyperplasia, and lack early immunotherapy are more prone to transforming into generalized myasthenia gravis (GMG). Factors influencing the transformation from OMG to GMG have been widely reported in adult MG studies; however, research in jugular myasthenia gravis (JMG) is relatively limited. Summary of the Invention

[0004] To address the above problems, this invention provides a biomarker for predicting the risk of ocular-to-systemic conversion in adolescent myasthenia gravis and its application.

[0005] To achieve the above objectives, the specific technical solution provided by the present invention is as follows: The first aspect of the present invention provides a SNP marker for detecting the risk of transformation from ocular myasthenia gravis to systemic myasthenia gravis, wherein the SNP marker is rs79606458, rs1059582 or a combination of rs79606458 and rs1059582.

[0006] In this invention, the genome annotation background for rs79606458 is Chr6:32489813 (GRCh37.p13), belonging to the HLA-DRB5 region. The genome annotation background for rs1059582 is Chr6:32552017 (GRCh37.p13), belonging to the HLA-DRB1 region.

[0007] A second aspect of the present invention provides a kit for predicting the risk of transformation from ocular myasthenia gravis to systemic myasthenia gravis, the kit comprising reagents for detecting HLA regions.

[0008] Furthermore, the HLA region includes HLA-DRB1 and HLA-DRB5.

[0009] Furthermore, the kit includes reagents for detecting the combined genotypes of rs79606458, rs1059582, or rs79606458, rs1059582.

[0010] Furthermore, the reagents include those used to detect the SNP marker genotype by TaqMan probe method, sequencing method, microarray method, mass spectrometry of flight, restriction fragment length polymorphism method, single strand conformation polymorphism method, allele-specific PCR, SNaPshot method, SNPlex method, denaturing high performance liquid chromatography or denaturing gradient gel electrophoresis.

[0011] Furthermore, the kit also includes reagents for collecting and processing subject samples.

[0012] In the context of this invention, the term "sample" as used refers to a composition obtained from or derived from a subject (e.g., an individual of interest) that contains cells and / or other molecular entities to be characterized and / or identified based on, for example, physical, biochemical, chemical, and / or physiological characteristics. For instance, a sample refers to any sample derived from a subject of interest that is expected or known to contain cells and / or molecular entities to be characterized.

[0013] In the context of this invention, a subject refers to any individual of interest, preferably a living organism using an immune checkpoint inhibitor, including humans, other mammals, preferably primates, and particularly preferably adolescents.

[0014] In some embodiments, the samples include oral swabs, tissues, blood, urine, saliva, semen, breast milk, cerebrospinal fluid, tears, sputum, mucus, ascites, pleural effusion, amniotic fluid, bladder irrigation fluid, and bronchoalveolar lavage fluid.

[0015] Furthermore, the subjects were adolescents.

[0016] The third aspect of the present invention provides the application of a reagent for detecting the genotype of SNP sites in a sample in the preparation of a product for predicting the risk of conversion from ocular myasthenia gravis to systemic myasthenia gravis, wherein the SNP sites are rs79606458, rs1059582 or a combination of rs79606458 and rs1059582.

[0017] Furthermore, the reagents for detecting SNP locus genotypes include reagents used in any of the following methods: TaqMan probe method, sequencing method, microarray method, mass spectrometry of flight, restriction fragment length polymorphism method, single strand conformation polymorphism method, allele-specific PCR, SNaPshot method, SNPlex method, denaturing high performance liquid chromatography or denaturing gradient gel electrophoresis.

[0018] Furthermore, the product is a reagent kit, chip, nucleic acid membrane strip, or high-throughput sequencing system.

[0019] In this invention, the product may comprise a solid substrate such as a chip, a glass slide, an array, etc., having reagents capable of detecting one or more blood biomarkers or other sample-derived biomarkers fixed at predetermined locations on the substrate. As an illustrative example, reagents fixed at discrete predetermined locations may be provided to the chip for detecting the genotype of SNP biomarkers in a blood sample, their concentration in any quantity or any combination thereof.

[0020] Furthermore, the product also includes reagents for collecting and processing subject samples.

[0021] Furthermore, the samples include oral swabs, tissues, blood, urine, saliva, semen, breast milk, cerebrospinal fluid, tears, sputum, mucus, ascites, pleural effusion, amniotic fluid, bladder irrigation fluid, and bronchoalveolar lavage fluid.

[0022] Furthermore, the subjects were adolescents.

[0023] The fourth aspect of this invention provides a method for constructing a risk prediction model for the conversion of adolescent myasthenia gravis ocular type to systemic type. The method includes the following steps: genotypes of the SNP markers described in the first aspect of this invention in the sample, wherein the genotype data are from the population of adolescents with myasthenia gravis ocular type converting to systemic type and the non-converted population; and inputting the genotype data into a machine learning algorithm to construct a prediction model.

[0024] In some embodiments, the methods for constructing the predictive model are known to those skilled in the art, and the steps of associating SNP marker genotypes with a certain probability or risk can be implemented and realized in different ways. Preferably, the SNP marker genotype is mathematically associated with the risk of conversion from the ocular type of fundamental juvenile myasthenia gravis to the systemic type. The determination of the SNP marker genotype can be combined with other clinical characteristics using any suitable existing technical mathematical methods, and a predictive model can be constructed using machine learning algorithms.

[0025] The machine learning includes algorithmic models developed using various development tools; these tools include one or more of TensorFlow, Scikit-Learn, PyTorch, OpenNN, RapidMiner, Azure Machine Learning, Apache Mahout, Shogun, KNIME, Vertex AI, H2Oai, Anaconda, Keras, Tableau, Fast.ai, Catalyst, Amazon ML, MLJAR, and Spell. The algorithmic models include one or more of convolutional neural networks, autoencoders, deep belief networks, linear regression, logistic regression, Lasso regression, Ridge regression, linear discriminant analysis, K-nearest neighbors, decision trees, perceptrons, support vector machines, ensemble learning, correlation analysis, Naive Bayes, AdaBoost, GBDT, XGBoost, LightGBM, CatBoost, or random forests.

[0026] A fifth aspect of the present invention provides a system for predicting the risk of conversion from ocular to systemic myasthenia gravis in adolescents, the system comprising an input device for inputting genotypes of HLA regions.

[0027] Furthermore, the system also includes an output device for outputting the prediction results.

[0028] Furthermore, the system also includes a device for detecting SNPs.

[0029] Furthermore, the system also includes an evaluation result transmission device.

[0030] Furthermore, the system also includes a collection device for collecting subject sample information.

[0031] The system may be a user's electronic device or a computer system remotely located relative to that electronic device.

[0032] A sixth aspect of the present invention provides a computer device for predicting the risk of transformation from ocular to systemic myasthenia gravis in adolescents, comprising a memory, a processor, and instructions stored in the memory, wherein the processor executes the instructions to perform the following steps: Obtain data to obtain the genotypes of the SNP markers described in the first aspect of this invention in the samples.

[0033] The data is processed by inputting the genotype data into the prediction model obtained by the construction method described in the fourth aspect of the present invention for classification prediction, and obtaining classification results for the test sample as having a high risk of conversion from ocular myasthenia gravis to systemic myasthenia gravis and a low risk of conversion from ocular myasthenia gravis to systemic myasthenia gravis.

[0034] Output the prediction results.

[0035] Among them, computer equipment can be mobile electronic devices.

[0036] The processor can also be called a Central Processing Unit (CPU). A processor may be an integrated circuit chip with signal processing capabilities. A processor can also be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. A general-purpose processor can be a microprocessor or any conventional processor.

[0037] A seventh aspect of the present invention provides a computer-readable storage medium for predicting the risk of transformation from ocular to systemic myasthenia gravis in adolescents, having instructions stored thereon that, when executed by a processor, perform the following steps: Obtain data to obtain the genotypes of the SNP markers described in the first aspect of this invention in the samples.

[0038] The data is processed by inputting the genotype data into the prediction model obtained by the construction method described in the fourth aspect of the present invention for classification prediction, and obtaining classification results for the test sample as having a high risk of conversion from ocular myasthenia gravis to systemic myasthenia gravis and a low risk of conversion from ocular myasthenia gravis to systemic myasthenia gravis.

[0039] Output the prediction results.

[0040] The storage medium of this invention stores program instructions capable of implementing the aforementioned computer-based colorectal cancer risk prediction. These program instructions can be stored in the storage medium as a software product and include several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) or processor to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks, or computer devices such as computers, servers, mobile phones, and tablets.

[0041] It should be understood that the system, device, and readable medium described in this invention can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the shown or discussed mutual couplings, direct couplings, or communication connections may be indirect couplings or communication connections between devices or units through some interfaces, and may be electrical, mechanical, or other forms. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0042] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0043] Advantages and benefits of the present invention: The present invention provides SNP loci and combinations thereof that can independently predict the risk of ocular-to-systemic transformation of myasthenia gravis in adolescents. By detecting specific SNP genotypes, high-risk individuals who are ocular-type patients can be identified in advance to transform into systemic-type patients. This enables risk prediction, early intervention and personalized treatment, significantly improves patient prognosis and avoids serious complications such as respiratory muscle weakness caused by disease progression. Attached Figure Description

[0044] Figure 1 Manhattan plot (A) and QQ plot (B) were used for GWAS analysis of JMG whole-body type transformation.

[0045] Figure 2 Linkage disequilibrium (LD) diagram for the upstream and downstream 20kb regions of HLA-DRB1 and HLA-DRB5.

[0046] Figure 3 PRS score plot for GWAS analysis of JMG whole-body type conversion. Where A represents the logistic regression model explaining PT 10. -12 A is the variance ratio up to 0.1; B is the z-transform density distribution of the best performing PT (P<0.001); C is the ROC curve of the predictive performance of PRS on the transformation of OMG to GMG; D is the odds ratio (OR) of the logistic regression model for PT with P<0.001.

[0047] Figure 4The figures show the single-cell sequencing results before and after JMG treatment in healthy controls. A represents the distribution of HLA-DRB1 in different cell subpopulations of PBMCs; B represents the distribution of HLA-DRB5 in different cell subpopulations of PBMCs; and C compares the expression levels of HLA-DRB1 and HLA-DRB5 in different cell subpopulations before and after JMG treatment in healthy controls.

[0048] Figure 5 To verify the independent SNP sites in the sample set and the ROC curves for predicting conversion risk using combined diagnostic methods. Detailed Implementation

[0049] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0050] Example I. Experimental Methods 1. Sample Source: The screening group consisted of whole blood samples from 490 AChR-Ab positive JMG patients frozen in a -80℃ sample bank. All samples were obtained from the Myasthenia Gravis Treatment Center of Shijiazhuang People's Hospital. DNA was extracted from the children using a DNA extraction kit for subsequent whole-genome SNP sequencing. All family members of the children agreed to participate in this study and signed informed consent forms. The included children met the following criteria: (1) They met the diagnostic criteria for JMG, namely, age of onset <18 years, typical clinical symptoms, positive neostigmine test or typical electrophysiological examination, and positive acetylcholine receptor antibody (AChR-Ab); (2) They were of Han nationality; (3) They had no history of other autoimmune diseases; and (4) They had no family history of MG.

[0051] 2. GWAS Genotyping and Statistical Analysis: GWAS genotyping was performed on 490 JMG patients (including 401 non-transformed and 89 transformed patients) using Illumina ASA Asian genome-wide SNP array technology. The differences in allele frequencies of each SNP between the two groups were compared using an additive model analysis. Bonferroni correction and FDR correction were used to adjust for p-values, with p < 10 considered acceptable. -8 For statistical significance, SNPs that were statistically significant in the conversion of adolescents from simple ocular muscle type to systemic type were detected.

[0052] 3. Graphical Analysis of GWAS Data: Using the ggplot package in R, key information such as SNP number, chromosome number, physical location, and corresponding p-value are extracted from the GWAS analysis to create Manhattan plots and QQ plots. The p-values ​​are converted to -log10(p-value) form as the Y-axis coordinate, visually presenting the p-value distribution of all SNP loci across the entire genome. LD blocks within the 20kb upstream and downstream regions (±20kb) of the HLA-DRB1 and HLA-DRB5 genes are analyzed.

[0053] 4. PRSice (polygenic risk assessment) analysis: A self-written script was used to filter SNP sites, removing duplicate sites, SNP sites with chromosome 0 and M, and the remaining 715,588 SNP sites were used for subsequent analysis.

[0054] The filtered SNP sites were used to perform GWAS association analysis with the OMG transformation phenotype using the plink software to obtain the P-value and BETA value (quantitative trait) or OR value (qualitative trait) associated with each SNP site.

[0055] The association results of the OMG transformation phenotype were analyzed using PRSice software, and the results were provided with a P<0.001 threshold standard.

[0056] The PRS scores calculated based on the above thresholds are standardized and plotted to show the distribution of PRS scores for each sample.

[0057] Based on the provided PRS scores, the data were divided into 10 groups from smallest to largest. The group with the lowest score was used as the control group, and the remaining 9 groups were used as experimental groups. Logistic regression was performed on 202 SNPs in sequence, and SNPs with a p-value less than 0.05 were retained. Then, the intersection of the SNPs obtained from the above 9 logistic regressions was calculated.

[0058] 5. Single-cell sequencing analysis: Single-cell sequencing was performed to compare JMG patients with healthy controls, as well as to analyze the changes in HLA-DRB1 and HLA-DRB5 gene levels in JMG patients before and after treatment.

[0059] 6. Validation of Predictive Efficacy: A new validation sample set was collected from the Myasthenia Gravis Treatment Center of Shijiazhuang People's Hospital according to the above inclusion criteria. This sample set included 58 non-converted patients and 39 converted patients. PCR validation was performed on the HLA-DRB1 (rs79606458) and HLA-DRB5 (rs1059582) loci, and receiver operating characteristic (ROC) curves were plotted to evaluate the predictive ability of HLA-DRB1 (rs79606458) and HLA-DRB5 (rs1059582) loci, both individually and in combination, for disease conversion.

[0060] II. Experimental Results 1. GWAS analysis to screen key SNPs for the transformation from simple ocular myasthenia gravis to systemic JMG. 490 JMG patients (401 non-transformed and 89 transformed) underwent GWAS genotyping using Illumina ASA Asian genome-wide SNP array technology, followed by statistical analysis. The allele frequencies of each SNP were compared between the two groups. Additive model analysis was used, and Bonferroni and FDR corrections were applied to adjust for p-values. In this study, p < 10 was considered acceptable. -8 For statistical significance, two SNPs were detected that were statistically significant for the transformation of adolescent myasthenia gravis from the ocular type to the generalized type, as shown in Table 1.

[0061] Table 1. SNPs associated with the transformation from ocular myasthenia gravis to systemic myasthenia gravis in JMG patients in the GWAS study

[0062] Note: Chr: Chromosome number; SNP: rs number; Nearest gene: gene containing the chromosome; EA: effect allele; OA: other allele; EAF: effect allele frequency.

[0063] Drawing Manhattan diagrams and QQ diagrams ( Figure 1 The distribution of p-values ​​for all SNP sites across the entire genome after GWAS analysis is presented visually. LD block analysis of the 20kb upstream and downstream regions (±20kb) of the HLA-DRB1 and HLA-DRB5 genes revealed no direct linkage. Figure 2 ).

[0064] 2. PRSice (Polygenic Risk Assessment) Analysis and Performance Validation The results are as follows Figure 3 As shown, the logistic regression model explains PT 10 -12 The variance ratio of up to 0.1 indicates that the model has a good ability to capture data variation. Figure 3 B in the diagram shows the z-transform density distribution of the best performance PT (P<0.001), with the control group of unconverted patients (orange) and the case group of converted patients (purple). Figure 3 In the C and D sections, the performance of the polygenic risk score (PRS) in predicting the conversion of OMG to GMG was evaluated using ROC curves, with a P-value of 3.13e. -5 With AUC=0.72 and R2liability=0.95%, the efficacy and odds ratio (OR) of PRS in conversion prediction were clarified, revealing the relationship and degree of influence between SNP and disease conversion from the perspective of risk association.

[0065] 3. Single-cell sequencing Single-cell sequencing analysis showed that the levels of HLA-DRB1 and HLA-DRB5 were higher in JMG patients than in healthy controls. After treatment, as patients' symptoms completely subsided, the levels of HLA-DRB1 and HLA-DRB5 genes also decreased. Figure 4 ).

[0066] 4. Validation of predictive performance The ROC curves for the validation sample set showed that the AUCs for predicting disease transformation by HLA-DRB1 (rs79606458) and HLA-DRB5 (rs1059582) loci individually and in combination were 0.794, 0.734, and 0.834, respectively, with P < 0.001 (see [link to data]). Figure 5 (and Table 2).

[0067] Table 2. Value and efficacy of key SNPs in predicting transformation in independent and combined diagnostics.

[0068] The above description of the embodiments is only for understanding the method and core ideas of the present invention. It should be noted that those skilled in the art can make various improvements and modifications to the present invention without departing from the principles of the invention, and these improvements and modifications will also fall within the protection scope of the claims of the present invention.

Claims

1. A SNP biomarker for detecting the risk of transformation from ocular myasthenia gravis to generalized myasthenia gravis, characterized in that, The SNP markers are rs79606458, rs1059582, or a combination of rs79606458 and rs1059582.

2. A kit for predicting the risk of transformation from ocular to generalized myasthenia gravis, characterized in that, The kit includes reagents for detecting HLA regions; Preferably, the HLA region includes HLA-DRB1 and HLA-DRB5.

3. The reagent kit according to claim 2, characterized in that, The kit includes reagents for detecting the combined genotypes of rs79606458, rs1059582, or rs79606458, rs1059582; Preferably, the reagents include those used to detect the SNP marker genotype by TaqMan probe method, sequencing method, microarray method, mass spectrometry of flight, restriction fragment length polymorphism method, single strand conformation polymorphism method, allele-specific PCR, SNaPshot method, SNPlex method, denaturing high performance liquid chromatography or denaturing gradient gel electrophoresis.

4. The reagent kit according to claim 2, characterized in that, The kit also includes reagents for collecting and processing subject samples; Preferably, the samples include oral swabs, tissues, blood, urine, saliva, semen, breast milk, cerebrospinal fluid, tears, sputum, mucus, ascites, pleural effusion, amniotic fluid, bladder irrigation fluid, and bronchoalveolar lavage fluid. Preferably, the subject is an adolescent.

5. The application of reagents for detecting SNP loci genotypes in samples in the preparation of products for predicting the risk of conversion from ocular myasthenia gravis to systemic myasthenia gravis, characterized in that... The SNP sites are rs79606458, rs1059582 or a combination of rs79606458 and rs1059582; Preferably, the reagents for detecting SNP locus genotypes include reagents used in any of the following methods: TaqMan probe method, sequencing method, microarray method, mass spectrometry of flight, restriction fragment length polymorphism method, single strand conformation polymorphism method, allele-specific PCR, SNaPshot method, SNPlex method, denaturing high performance liquid chromatography or denaturing gradient gel electrophoresis.

6. The application according to claim 5, characterized in that, The products are reagent kits, chips, nucleic acid membrane strips, or high-throughput sequencing systems; Preferably, the product further includes reagents for collecting and processing subject samples; Preferably, the samples include oral swabs, tissues, blood, urine, saliva, semen, breast milk, cerebrospinal fluid, tears, sputum, mucus, ascites, pleural effusion, amniotic fluid, bladder irrigation fluid, and bronchoalveolar lavage fluid. Preferably, the subject is an adolescent.

7. A method for constructing a risk prediction model for the transformation of adolescent myasthenia gravis ocular myasthenia gravis into systemic myasthenia gravis, characterized in that, The construction method includes the following steps: the genotype of the SNP marker as described in claim 1 in the sample, wherein the genotype data comes from the population of adolescents with myasthenia gravis who have transformed from ocular myasthenia gravis to systemic myasthenia gravis and the population that have not transformed; and inputting the genotype data into a machine learning algorithm to construct a prediction model.

8. A system for predicting the risk of adolescent myasthenia gravis transformation from ocular to generalized type, characterized in that, The system includes an input device for inputting genotypes of HLA regions; Preferably, the system further includes an output device for outputting the prediction results; Preferably, the system further includes a SNP detection device; Preferably, the system further includes an evaluation result transmission device; Preferably, the system further includes a collection device for collecting subject sample information.

9. A computer device for predicting the risk of transformation from ocular to generalized myasthenia gravis in adolescents, comprising a memory, a processor, and instructions stored in the memory, characterized in that, The processor executes the instructions to perform the following steps: Obtain data, and obtain the genotype of the SNP marker described in claim 1 in the sample; The data is processed by inputting the genotype data into the prediction model obtained by the construction method described in claim 7 for classification prediction, and obtaining classification results for the test sample as having a high risk of conversion from ocular myasthenia gravis to systemic myasthenia gravis and a low risk of conversion from ocular myasthenia gravis to systemic myasthenia gravis. Output the prediction results.

10. A computer-readable storage medium for predicting the risk of transformation from ocular to generalized myasthenia gravis in adolescents, having instructions stored thereon, characterized in that, When the instruction is executed by the processor, it performs the following steps: Obtain data, and obtain the genotype of the SNP marker described in claim 1 in the sample; The data is processed by inputting the genotype data into the prediction model obtained by the construction method described in claim 7 for classification prediction, and obtaining classification results for the test sample as having a high risk of conversion from ocular myasthenia gravis to systemic myasthenia gravis and a low risk of conversion from ocular myasthenia gravis to systemic myasthenia gravis. Output the prediction results.

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