Application of biomarkers in the preparation of therapeutic and predictive products for myasthenia gravis

Abstract

This application discloses the use of a biomarker in the preparation of a product for the treatment and prediction of myasthenia gravis, wherein the biomarker is one or more of OSM, CASP-8, TRAIL, TGF-α, FLT3L, IFN-γ, IL-7, and AXIN1. Through multi-omics combined analysis, this application explores the dynamic changes and molecular mechanisms of immune-inflammatory-related proteins after complement inhibition from the dual dimensions of gene transcription and protein expression, and finds a comprehensive biomarker that can systematically evaluate the dynamics of symptom improvement and the degree of disease stability, with the advantages of accurately guiding treatment adjustments and predicting disease outcomes.

Description

Technical Field

[0001] The present invention relates to the field of biomedicine technology, and in particular to the use of a biomarker in the preparation of a product for treating and predicting myasthenia gravis. Background Art

[0002] Myasthenia gravis (MG) is an acquired autoimmune disease mediated by antibodies and dependent on cell-mediated immunity. The core pathological mechanism of MG in patients with anti-acetylcholine receptor (AChR) antibody-positive MG is autoantibody activation of the classical complement cascade, leading to structural disruption of the postsynaptic membrane at the neuromuscular junction (NMJ). Although complement C5 inhibitors, such as eculizumab, can effectively block this pathological process, complement-related hematological markers such as anaphylatoxin C5a, membrane attack complex (MAC), and serum complement hemolytic activity (CH50) fail to accurately reflect the extent of NMJ damage and disease activity. Consequently, current clinical decision-making still relies on symptom scores and antibody titers, which lags in prognosis assessment and treatment decision-making.

[0003] However, the asynchrony between complement inhibition therapy and symptom improvement and the individual variability in symptom fluctuations after drug discontinuation indicate that the current evaluation system has significant limitations.

[0004] It should be noted that the information disclosed in the above background technology section is only used to enhance the understanding of the background of the present disclosure, and therefore may include information that does not constitute prior art known to ordinary technicians in the field. Summary of the Invention

[0005] The technical task of this application is to address the above deficiencies and provide a biomarker for the preparation of products for the treatment and prediction of myasthenia gravis. Through multi-omics combined analysis, this application explores the dynamic changes and molecular mechanisms of immune-inflammatory-related proteins after complement inhibition from the dual dimensions of gene transcription and protein expression, and finds a comprehensive biomarker that can systematically evaluate the dynamics of symptom improvement and the degree of disease stability, which has the advantages of accurately guiding treatment adjustments and predicting disease outcomes.

[0006] To achieve the above objectives, this application provides the following technical solutions:

[0007] According to one aspect of the present application, a biomarker is provided for use in preparing a product for treating and predicting myasthenia gravis, wherein the biomarker is one or more of OSM, CASP-8, TRAIL, TGF-α, FLT3L, IFN-γ, IL-7, and AXIN1.

[0008] In some embodiments, the biomarker is OSM or IFN-γ.

[0009] In some embodiments, the biomarkers include OSM and IL-7.

[0010] According to another aspect of the present application, a biomarker combination is provided, wherein the biomarker combination consists of serum total complement hemolytic activity, OSM expression level and IL-7 concentration.

[0011] According to another aspect of the present application, the use of the biomarker combination in a kit for evaluating the therapeutic effect of myasthenia gravis is also provided.

[0012] In some embodiments, the kit includes reagents for detecting serum total complement hemolytic activity, OSM expression level, and IL-7 concentration.

[0013] In some embodiments, the kit uses ELISA technology or microfluidic chip to detect serum total complement hemolytic activity, OSM expression level and IL-7 concentration.

[0014] According to another aspect of the present application, there is also provided an application of the biomarker combination in constructing a clinical score prediction model for myasthenia gravis.

[0015] In some embodiments, the prediction model includes a quantitative myasthenia gravis score prediction model and a daily living ability score prediction model, and the prediction model is constructed using binary logistic regression analysis.

[0016] In some embodiments, the equation of the quantitative myasthenia gravis score prediction model is: probability of quantitative myasthenia gravis score above the median = -5.1975 + 0.0700 × serum total complement hemolytic activity + 0.6866 × OSM expression level - 0.3134 × IL-7 concentration; the equation of the daily living ability score prediction model is: probability of daily living ability score above the median = -10.2096 + 0.0446 × serum total complement hemolytic activity + 1.6621 × OSM expression level - 0.4761 × IL-7 concentration.

[0017] Compared with the existing technology, the advantages and positive effects of this application are: this application uses multi-omics combined analysis to screen the dynamic changes and molecular mechanisms of immune-inflammatory related proteins after complement inhibition from the dual dimensions of gene transcription and protein expression, and finds comprehensive biomarkers that can systematically evaluate the dynamics of symptom improvement and the degree of disease stability, which has the advantages of accurately guiding treatment adjustments and predicting disease outcomes.

[0018] Furthermore, this application proposed for the first time that the prediction model combining CH50, OSM and IL-7 showed highly reliable prediction ability (C-index=0.889) and cost / benefit decision-making ability (net benefit close to 1.0). BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0020] Figure 1 The volcano plot of differentially expressed proteins in serum proteomics of MG patients before and after eculizumab treatment in the examples of this application is shown.

[0021] Figure 2 Shown is a paired scatter plot of differentially expressed proteins in the serum proteomics of MG patients before and after eculizumab treatment in the examples of this application.

[0022] Figure 3 The volcano plot and bar chart of differentially expressed genes in the transcriptome of MG patients before and after eculizumab treatment in the examples of this application are shown.

[0023] Figure 4 A Venn diagram of the correlation analysis between proteomics and transcriptomics in the examples of this application is shown.

[0024] Figure 5 A scatter plot of the proteomics and transcriptomics correlation analysis in the examples of this application is shown.

[0025] Figure 6 A scatter plot of the correlation analysis between serum proteomic differentially expressed proteins and MG clinical scores before and after eculizumab treatment in the examples of this application is shown.

[0026] Figure 7 The ROC curve of the predictive efficacy of serum proteomic differentially expressed protein levels on the QMG score of eculizumab treatment in the examples of this application is shown.

[0027] Figure 8The correlation between serum CH50 levels and clinical scores before and after eculizumab treatment in the examples of this application is shown.

[0028] Figure 9 The clinical decision-making value verification of the QMG score combined prediction model in the embodiments of this application is shown.

[0029] Figure 10 The clinical decision-making value verification of the ADL score combined prediction model in the embodiment of this application is shown. DETAILED DESCRIPTION

[0030] In order to more clearly understand the above-mentioned objects, features and advantages of the present application, the present application is further described below in conjunction with the accompanying drawings and embodiments. It should be noted that the embodiments of the present application and the features therein can be combined with each other without conflict.

[0031] The abbreviations of the main biomarkers correspond to:

[0032] Oncostatin M (OSM); Caspase-8 (CASP-8); TNF-related apoptosis-inducing ligand (TRAIL);

[0033] Transforming growth factor alpha (TGF-α); FMS-like tyrosine kinase 3 ligand (FLT3L); interferon gamma (IFN-γ); interleukin 7 (IL-7); axis inhibitory protein 1 (AXIN1); quantitative myasthenia gravis (QMG); activities of daily living (ADL).

[0034] The present application will be further described below with reference to the accompanying drawings and specific embodiments.

[0035] In this example, 10 patients with anti-AChR antibody-positive generalized myasthenia gravis (gMG) receiving standard treatment with eculizumab were enrolled. With ethical approval and informed consent, peripheral blood samples were collected before treatment, after a single induction treatment (week 1), and after completing four induction treatments (week 5). After a single induction treatment, serum total complement hemolytic activity (CH50) was measured using a liposome immunoassay. After completing four induction treatments, serum inflammation-related proteins were quantitatively measured using a proximity extension assay (PEA) (Olink Proteomics, Target 96 Inflammation Panel). Peripheral blood leukocyte gene expression profiles were analyzed in conjunction with RNA-seq transcriptome sequencing. Cross-omics data were integrated to delineate the immune regulatory network driven by complement inhibition.

[0036] 1. Screening of biomarkers.

[0037] Through the cross-omics joint analysis of Olink ultra-low abundance proteomics (PEA technology) and Bulk RNA-seq transcriptomics, combined with a longitudinal study design, Figure 1-5 As shown in the results, 18 serum differentially expressed proteins related to the clinical score of myasthenia gravis (MG) were screened out from 92 inflammation-related proteins for the first time (P<0.05), among which OSM and IFN-γ were significant at both gene transcription and protein expression levels.

[0038] (1) Olink ultra-low abundance proteomics screening of differentially expressed proteins.

[0039] Figure 1 The volcano plot of differentially expressed proteins in serum proteomics of MG patients before and after eculizumab treatment in the examples of this application is shown. Specifically, the volcano plot shows the changing trends of all proteins in the differential expression analysis. FC value) represents the difference in protein changes in different treatments (positive value indicates upregulation, negative value indicates downregulation), the vertical axis (P value) represents the statistical significance of the difference in protein abundance value. Gray represents non-differentially expressed proteins, and red represents significantly up-regulated differentially expressed proteins, including Ftl3L (FMS-like Tyrosine Kinase 3 ligand); CSF-1 (Colony Stimulating Factor 1); FGF-19 (Fibroblast Growth Factor 19); CD8A (CD8 Alpha Chain); TRAIL (TNF-Related Apoptosis-Inducing Ligand); IFN-gamma (Interferon-gamma); CCL19 (CC Motif Chemokine Ligand 19); IL7 (Interleukin-7); OPG (Osteoprotegerin); CCL28 (CC Motif Chemokine); Ligand 28 (CC motif chemokine 28). Blue represents significantly downregulated differentially expressed proteins, including CASP-8 (Caspase-8); AXIN1 (Axis Inhibition Protein 1); TGF-alpha (Transforming Growth Factor-alpha); SIRT2 (Sirtuin 2); ST1A1 (Sulfotransferase Family 1A Member 1); STAMBP (Signal Transducing Adaptor Molecule Binding Protein); OSM (Oncostatin M); and IL-22 RA1 (Interleukin-22 Receptor Alpha 1).

[0040] Figure 2 A paired scatter plot of differentially expressed proteins in the serum proteomics of MG patients before and after eculizumab treatment is shown in the examples of this application. The paired scatter plot uses NPX values ​​as the vertical axis, with red and blue representing differential protein expression levels before and after eculizumab treatment, respectively. * indicates statistical significance of differential expression, *P < 0.05, **P < 0.01.

[0041] (2) Bulk RNA-seq transcriptomics screening of differentially expressed genes:

[0042] Figure 3 The volcano plot and bar chart of differentially expressed genes in the transcriptome of MG patients before and after eculizumab treatment in the present application are shown. Fold Change indicates the fold change of gene expression (positive value is up-regulated, negative value is down-regulated), the vertical axis ( Adjusted P value) indicates the significance of the difference, red dots mark (UP) significantly up-regulated genes, blue dots mark (DOWN) significantly down-regulated genes, and gray dots (STABLE) indicate genes with no significant differences; the bar graph on the right shows the distribution of the number of differentially expressed genes (red column (Up): up-regulated, dark blue column (Down): down-regulated, and light blue column (Total): total number).

[0043] (3) Joint analysis of proteomics and transcriptomics:

[0044] Figure 4 This diagram shows a Venn diagram for the correlation analysis of proteomics and transcriptomics in the examples of this application. All_protein represents all proteins quantifiable by the proteome; All_mRNA represents all genes quantifiable by the transcriptome; DE_Protein represents differentially expressed proteins identified by the proteome; and DE_Gene represents differentially expressed genes identified by the transcriptome. The color scale bar on the right uses a color gradient to visually map the number of elements in each region.

[0045] Figure 5 The scatter plot of the correlation analysis between proteomics and transcriptomics in the embodiment of this application is shown. (Ratio) indicates the fold change of protein expression (positive value is up-regulated, negative value is down-regulated), the vertical axis is (FC) represents the fold change of differential gene expression (positive value upregulation, negative value downregulation). Data point color classification: purple points (NDEPs_NDEGs): overlap between non-differentially expressed proteins and non-differentially expressed genes; green points (DEPs_NDEGs): overlap between differentially expressed proteins and non-differentially expressed genes; blue points (NDEPs_DEGs): overlap between non-differentially expressed proteins and differentially expressed genes; red points (DEPs_DEGs): overlap between differentially expressed proteins and differentially expressed genes.

[0046] 2. Verification of the correlation between biomarkers and myasthenia gravis (MG) clinical scores (QMG score and ADL score).

[0047] Figure 6The scatter plot of the correlation analysis between serum proteomic differentially expressed proteins and MG clinical scores before and after eculizumab treatment in the embodiment of this application is shown. In the figure, QMG score is quantitative myasthenia gravis score, and ADL is daily living ability score. Figure 6 As shown in the results, Spearman rank correlation analysis revealed that the dynamic changes in the expression of OSM, CASP-8, TRAIL, TGF-α, FLT3L, IFN-γ, IL-7, and AXIN1 were significantly correlated with the improvement of MG clinical scores (P<0.05). Univariate linear regression analysis further revealed that OSM (β=3.233, 95%CI 0.3279 to 6.138, P<0.05), CASP-8 (β=3.058, 95%CI 0.6723 to 5.443, P<0.05), TGF-α (β=3.962, 95%CI 0.1077 to 7.816, P<0.05), and TRAIL (β=-8.719, 95%CI-16.57 to-0.8645, P<0.05) had potential causal associations with the improvement of MG-QMG scores. OSM (β=1.905, 95%CI 0.4230 to 3.387, P<0.05), CASP-8 (β=1.950, 95%CI 0.8168 to 3.082, p<0.01), and TGF-α (β=2.441, 95%CI 0.4997 to 4.383, P<0.05) were potentially causally associated with the improvement of MG-ADL scores.

[0048] 3. Analysis of the predictive efficacy of serum proteomics differentially expressed protein levels on the QMG score affected by eculizumab treatment.

[0049] Figure 7 The ROC curve of the predictive efficacy of serum proteomics differentially expressed protein levels on the QMG score of eculizumab treatment in the embodiment of the present application is shown, wherein the horizontal axis is 1-specificity and the vertical axis is sensitivity. Each protein is represented by a different color curve, and the AUC value is marked next to it. Figure 7 a is the significantly differentially expressed protein by Spearman correlation analysis. Figure 7 b indicates proteins with significant differences in both Spearman correlation analysis and univariate linear regression analysis. Figure 7As shown in the figure, by drawing the ROC curve, it was found that IL-7 (AUC=0.99, critical value>3.643pg / mL, sensitivity 95%, specificity 100%) has both high sensitivity and high specificity, showing good ability to predict treatment response. The other seven serum differentially expressed proteins all had good predictive efficacy: AXIN1 (AUC=0.9875, cutoff value >3.810 pg / mL, sensitivity 45%), TGF-α (AUC=0.89, <2.442 pg / mL, sensitivity 50%), IFN-γ (AUC=0.7325, cutoff value <4.046 pg / mL, sensitivity 60%), TRAIL (AUC=0.732, cutoff value <4.046 pg / mL, sensitivity 65%), CASP-8 (AUC=0.7994, cutoff value >7.790 pg / mL, sensitivity 67%, specificity 94%), OSM (AUC=0.7513, cutoff value >7.915 pg / mL, sensitivity 67%, specificity 94%), FLT3L (AUC=0.5802, cutoff value <6.024 pg / mL, sensitivity 67%, specificity 94%), and VEGF (AUC=0.8975, cutoff value <6.024 pg / mL, sensitivity 67%, specificity 94%). pg / mL).

[0050] 4. Construct a joint prediction model based on potential therapeutic targets to optimize the complement inhibition treatment cycle.

[0051] Figure 8 The correlation between serum CH50 levels and clinical scores before and after eculizumab treatment is shown in the examples of this application, where **P<0.01. Figure 8 As shown, IL-7 demonstrated excellent response classification performance (AUC = 0.99). However, while its protein level increased significantly after complement inhibition therapy (P < 0.05), the change in mRNA expression was not statistically significant (FDR = 0.078). Complement inhibition, however, downregulates OSM protein expression at the transcriptional level, making it more suitable for tracking the evolution of chronic immune homeostasis. Based on this, the construction of a predictive model combining CH50 (complement activity) + IL-7 + OSM can overcome the limitations of single markers in terms of timeliness and predictive efficacy, providing a comprehensive management strategy for complement inhibition therapy, from acute response to chronic homeostasis.

[0052] Figure 9 The clinical decision-making value verification of the QMG score combined prediction model in the embodiment of this application (calibration curve and decision curve analysis) is shown. Figure 10 The clinical decision-making value verification (calibration curve and decision curve analysis) of the ADL score combined prediction model in the embodiment of the present application is shown, wherein: Figure 9 a and Figure 10The abscissa of the calibration curve represents the probability that the model predicts a clinical score above the median, while the ordinate represents the actual proportion of observed clinical scores above the median. The three curves in the figure represent: the ideal line (black dashed line), which represents the ideal state where the predicted probability exactly equals the actual probability; the apparent model line (gray dashed line), which represents the uncalibrated model's prediction performance on the training data; and the bias-corrected line, which represents the calibration result after 40 bootstrap resampling (B=40) to correct for overfitting. Figure 9 b and Figure 10 b (Decision Curve Analysis): The horizontal axis (threshold probability) represents the minimum risk probability required for intervention, as defined by clinical decision makers. The cost-benefit ratio (CBR) above it converts economic considerations into clinical decision weights (1:100 means 100 units of health loss are avoided for every 1 unit of cost). The vertical axis represents the standardized net benefit, quantifying the clinical benefit efficiency of different strategies. The red curve represents the net benefit curve for the CH50+IL-7+OSM combined biomarker model; the gray curve represents the net benefit assuming intervention for all patients; and the black curve represents the net benefit assuming no intervention for all patients.

[0053] Depend on Figure 9 As shown in the figure, the calibration curve decision curve analysis (DCA) was used to evaluate the prediction probability calibration and clinical decision value. Based on the fluctuation of serum CH50, OSM and IL-7 levels before and after treatment, a QMG score prediction model (QMG_binary) was constructed by binary logistic regression analysis: logit (p QMG ) = -5.1975 + 0.0700 × serum CH50 + 0.6866 × OSM - 0.3134 × IL-7, where p QMGThe % represents the probability of a QMG score above the median. Calibration curve results showed that the model exhibited a systematic overestimation tendency in the predicted probability range of 0.3-0.7 (the Apparent curve deviated from the ideal line by up to 0.25). However, after B=40 bootstrap bias corrections, calibration accuracy significantly improved, with the mean absolute error (MAE) decreasing from 0.138 to 0.098. Specifically, in the high-risk threshold region >0.6, the error between the corrected predicted and observed probabilities remained within ±0.1, confirming the model's ability to risk stratify critically ill patients (C-index = 0.889). The DCA curve further quantified the model's clinical decision-making value: when the high-risk threshold set by clinical decision-makers ranged from 17% to 73% (corresponding to a cost-effectiveness ratio of 1:5 to 3:2), the net benefit of the combined marker-guided personalized treatment regimen remained stable between 0.778 and 0.95, significantly outperforming both the empirical full-intervention strategy (gray line) and the conservative treatment strategy (black line). It is worth noting that the net benefit peaked at 0.97 (sensitivity 95%, specificity 100%) at a threshold probability of 21%, suggesting that the model has the optimal cost-effectiveness balance in the medium-risk range, and preliminarily confirming that the combined prediction model has clinical translation potential.

[0054] Depend on Figure 10 As shown in the figure, after establishing the predictive efficacy of biomarkers related to QMG (quantitative myasthenia gravis) score, an independent validation model based on the ADL (activities of daily living) scoring system was simultaneously constructed to comprehensively evaluate the predictive efficacy of the combined prediction model for patients' muscle function (QMG) and quality of life (ADL). Based on the fluctuations in serum CH50, OSM, and IL-7 levels before and after treatment, a prediction system for ADL score (ADL_binary) was constructed through binary logistic regression analysis: logit (p ADL ) = -10.2096 + 0.0446 × serum CH50 + 1.6621 × OSM - 0.4761 × IL-7, where p ADLThe probability of a QMG score exceeding the median was quantified. Calibration curve results showed that the model systematically overestimated the predicted probability range of 0.4-0.8 (the Apparent curve deviated from the ideal line by up to 0.23). After B = 40 bootstrap bias corrections, the mean absolute error (MAE) decreased from 0.132 to 0.093. The error between the actual and predicted probabilities in the high-risk threshold region > 0.5 remained stable within ±0.08, validating the model's ability to identify patients with severe functional impairment (C-index = 0.938). The DCA curve further quantified its clinical utility: when the risk threshold was set between 19% and 73% (corresponding to a cost-effectiveness ratio of 1:4 to 3:2), the net benefit of the combined biomarker-guided intervention strategy remained in the range of 0.795-0.951, significantly outperforming the empirical full intervention strategy (gray line) and the non-intervention strategy (black line). The net benefit peaked at 0.96 (sensitivity 93%, specificity 92%) at a threshold probability of 22%, highlighting the model's clinical decision-making advantage in moderate-risk patients and revealing its value in accurately assessing the risk of impaired daily living abilities. Notably, the robustness of this combined prediction model in the moderate-risk range is highly consistent with the "moderate-risk priority optimization" strategy proposed in previous studies. This theory states that in medical decision-making, targeted intervention strategies should be prioritized for moderate-risk patients to achieve a balance between maximizing clinical benefit and minimizing resource consumption, thereby avoiding interference from extreme risk areas on clinical translation. In addition, this combined prediction model breaks through the fragmented interpretation of pathological processes by traditional single marker systems. Against the dual background of lagging development of biological markers for myasthenia gravis and pressure to optimize medical cost-effectiveness, it achieves closed-loop verification from health economic value to mechanism explainability.

[0055] The application of this technology in clinical decision-making and efficacy evaluation for myasthenia gravis (MG) is mainly reflected in the development of a rapid detection kit based on the CH50+IL-7+OSM combined prediction model (C-index = 0.889-0.938, peak net benefit > 0.95):

[0056] (1) Technical carrier: Use ELISA technology or microfluidic chip to achieve simultaneous detection of multiple targets (IL-7 / OSM quantification + CH50 activity determination).

[0057] (2) Application scenarios:

[0058] A. Pre-treatment stratification: Screen high-responder patients (AUC=0.99) using IL-7 baseline levels (cutoff value >3.643 pg / mL) to guide the selection of patients suitable for complement inhibitors.

[0059] B. Dynamic monitoring of efficacy: Combined with the dynamic changes in OSM (a decrease of >30% in the fifth week of treatment), the progress of ECM steady-state repair was evaluated and the medication cycle was optimized (e.g., if CH50 <10 U / mL, the maintenance interval was extended).

[0060] Through the above specific embodiments, those skilled in the art can easily implement the present application. However, it should be understood that the present application is not limited to the above specific embodiments. Based on the disclosed embodiments, those skilled in the art can arbitrarily combine different technical features to implement different technical solutions.

Claims

1. Application of a biomarker combination in constructing a clinical score prediction model for myasthenia gravis, characterized in that: The biomarker combination consists of serum total complement hemolytic activity, OSM expression level, and IL-7 concentration. The prediction model includes a quantitative myasthenia gravis score prediction model and a daily living ability score prediction model. The prediction model is constructed using binary logistic regression analysis. The equation of the quantitative myasthenia gravis score prediction model is: probability of quantitative myasthenia gravis score being higher than the median = -5.1975 + 0.0700 × serum total complement hemolytic activity + 0.6866 × OSM expression level - 0.3134 × IL-7 concentration; the equation of the daily living ability score prediction model is: probability of daily living ability score being higher than the median = -10.2096 + 0.0446 × serum total complement hemolytic activity + 1.6621 × OSM expression level - 0.4761 × IL-7 concentration.

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