Screening method of serum differential expression protein combination for NMOSD and MOGAD identification

Abstract

The invention discloses a screening method of a serum differential expression protein combination for NMOSD and MOGAD identification, and relates to the technical field of screening of serum differential expression protein combinations, and multi-dimensional screening comprises evaluation of expression stability of candidate differential expression proteins in an NMOSD patient group and an MOGAD patient group. Proteins with large expression fluctuation amplitude or obvious individual difference in the same disease population are removed, so that the consistency of the retained proteins in the disease population is ensured, and compared with the existing diagnosis method based on immune markers, the method has the advantages that the expression characteristics of the blood coagulation related differential proteins in the serum are analyzed; the method not only can improve the identification rate of NMOSD and MOGAD, but also can provide more accurate distinguishing results in different stages of a disease active stage and a remission stage.

Description

Technical Field

[0001] This invention relates to the field of screening techniques for differentially expressed protein combinations in serum, specifically a screening method for differentiating NMOSD from MOGAD. Background Technology

[0002] NMOSD and MOGAD are two distinct inflammatory demyelinating diseases of the central nervous system. Although they share some clinical overlap, their pathological mechanisms, clinical progression, and treatment methods differ significantly. NMOSD is typically mediated by aquaporin 4 antibodies, while MOGAD is caused by anti-MOG antibodies. Because the clinical manifestations of these two diseases are often similar, especially during acute exacerbations, distinguishing them is crucial for diagnosis and treatment. However, current diagnostic methods primarily rely on antibody detection, such as the detection of anti-AQP4 and anti-MOG antibodies. While these methods are widely used in clinical practice, their accuracy remains limited, particularly in the early stages of diagnosis.

[0003] Furthermore, current research largely focuses on the detection of immunological markers, but there is relatively little research on the role of changes in the coagulation system in these diseases. The coagulation system not only plays a role in blood stasis and thrombosis, but is also closely related to the immune response. In some immune-related diseases, abnormalities in the coagulation system are considered to be important factors in the occurrence and development of the disease. In particular, changes in the expression of coagulation-related proteins may become potential biomarkers for diagnosing and differentiating different immune diseases. Therefore, how to use the expression characteristics of coagulation-related differential proteins to help distinguish between NMOSD and MOGAD remains an important topic in current research. Although the coagulation system has shown potential diagnostic value in some immune diseases, there is no existing systematic technical solution for how to effectively screen and evaluate coagulation-related proteins, especially in the differential diagnosis of NMOSD and MOGAD. Therefore, there is an urgent need to develop a new method to further improve the diagnostic accuracy and differential diagnosis of these two diseases through the combination of differentially expressed serum proteins. Summary of the Invention

[0004] This invention provides a method for screening serum differentially expressed protein combinations for the identification of NMOSD and MOGAD, which can effectively solve the problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention provides the following technical solution: a method for screening serum differentially expressed protein combinations for the differentiation of NMOSD and MOGAD, comprising the following steps: S1. Collect peripheral blood samples from NMOSD patients and MOGAD patients respectively, and separate serum samples; S2. Detect protein expression in serum samples to obtain serum protein expression data for NMOSD and MOGAD patients; S3. Based on serum protein expression data, preliminary screening was conducted on the differences in protein expression between NMOSD patients and MOGAD patients to obtain a set of candidate differentially expressed proteins. S4. Based on the expression stability of candidate differentially expressed proteins in the two diseases, the magnitude of differences between diseases, and the intragroup variation, candidate differentially expressed proteins are screened in multiple dimensions to remove proteins with insufficient distinguishing ability or poor stability. S5. Combine and construct candidate protein combinations by combining the expression characteristics of multiple proteins; S6. Evaluate the ability of candidate protein combinations to differentiate between NMOSD and MOGAD patients, and screen for protein combinations that have a stable differentiating effect between the two diseases. S7. A combination of proteins with stable distinguishing ability is used as a serum differentially expressed protein combination for the differentiation of NMOSD and MOGAD.

[0006] According to the above technical solution, the multidimensional screening in S4 includes the assessment of the expression stability of candidate differentially expressed proteins in the NMOSD patient group and the MOGAD patient group. By removing proteins with large expression fluctuations or significant individual differences in the same disease population, the consistency of the retained proteins in the disease population is ensured.

[0007] According to the above technical solution, the multidimensional screening in S4 further includes evaluating the magnitude of the difference between candidate differentially expressed proteins in NMOSD and MOGAD, and preferentially retaining proteins that show significant differences in expression between the two diseases and whose difference direction is stable.

[0008] According to the above technical solution, the combination construction in S5 is not a simple superposition of multiple differentially expressed proteins, but a combination construction based on the complementarity of different proteins in the disease differentiation process. The complementarity includes at least the complementarity of different proteins in terms of expression change direction, difference magnitude or individual distribution characteristics.

[0009] According to the above technical solution, the evaluation of the ability to distinguish candidate protein combinations in step S6 includes comparing the differences in the overall expression distribution of different protein combinations in NMOSD patients and MOGAD patients, and selecting the best protein combination based on the differences in distribution.

[0010] According to the above technical solution, the serum differentially expressed protein combination obtained in S7 consists of at least two differentially expressed proteins, and the composition of the protein combination does not depend on the absolute expression level of a single protein, but is based on the relative expression characteristics of each protein in the combination to distinguish diseases.

[0011] According to the above technical solution, the screening of candidate differentially expressed proteins in S4 is further calculated based on the contribution of the protein to disease differentiation, so as to quantify the ability of different proteins to differentiate between NMOSD and MOGAD. The contribution of protein differentiation is calculated according to the following formula: in: Indicates the first The distinguishing contribution of different proteins; and The values ​​represent the mean expression levels of this protein in NMOSD and MOGAD patients, respectively. and These represent the degree of dispersion in the expression of the corresponding proteins in the two disease populations, respectively. Furthermore, the contribution rate is standardized and calculated as follows: Using the above calculation method, proteins with higher differentiation contributions are preferentially retained for subsequent protein assembly and construction steps.

[0012] According to the above technical solution, in step S6, a protein combination discrimination score is constructed to evaluate the overall discrimination ability of different protein combinations between NMOSD and MOGAD. Protein combination discrimination score is calculated according to the following formula: in: Indicates the protein combination differentiation score; Indicates the first in the sample to be tested Expression levels of certain proteins; This represents the average expression level of the corresponding protein in a reference disease population; Weighting coefficients assigned based on the contribution of proteins to differentiation; Furthermore, the protein combination discrimination scores of different disease populations were compared, and the discrimination index was calculated as follows: The disease identification ability of protein combinations is quantitatively assessed using the aforementioned differentiation scores and differentiation indices.

[0013] According to the above technical solution, the evaluation of candidate protein combinations in S6 further includes the calculation of combination stability and robustness to assess the reliability of protein combinations under different sample conditions. Combinatorial stability is calculated using the following formula: in: Indicates the stability score of the protein combination; and They represent the first Mean expression level and standard deviation of the protein in the disease population; Furthermore, a combined robustness index is introduced, which is calculated as follows: in: Indicates the robustness index of protein combinations; Indicates the first Protein combination discrimination scores calculated in subsample subset analysis; This represents the discrimination score calculated based on the entire sample. By combining stability scores and robustness indices, protein combinations that maintain stable discrimination ability under varying sample conditions are screened.

[0014] According to the above technical solution, the method constructs a comprehensive identification decision index based on the evaluation results of multiple algorithms, which is used to determine the protein combination finally used for the identification of NMOSD and MOGAD. The comprehensive identification decision index is calculated according to the following formula: in: Indicates comprehensive identification and decision-making indicators; Disease discrimination index representing protein composition; Indicates the stability score of the combination; Indicates the robustness index of the combination; These are the weighting coefficients; Furthermore, when the comprehensive identification decision index meets the following conditions, the protein combination is determined as the target protein combination for the identification of NMOSD and MOGAD: Through the above comprehensive evaluation and decision-making rules, a systematic screening and optimization of serum differentially expressed protein combinations can be achieved.

[0015] Compared with the prior art, the beneficial effects of the present invention are as follows: The present invention has a scientific and reasonable structure, is safe and convenient to use, and compared with the existing diagnostic methods based on immune markers, the present invention can not only improve the differentiation rate of NMOSD and MOGAD by analyzing the expression characteristics of coagulation-related differential proteins in serum, but also provide more accurate differentiation results at different stages of disease activity and remission. By using high-throughput proteomics and targeted protein detection, this invention can systematically screen coagulation-related proteins that show significant differences in expression levels between two diseases. Through multidimensional screening, combination construction, and other algorithm optimizations, the optimal protein combination is obtained, thereby achieving accurate differentiation between NMOSD and MOGAD. Unlike traditional single-marker detection methods, this invention proposes a multi-level screening and evaluation method based on protein combinations. This method not only improves diagnostic accuracy but also overcomes the instability of single-protein markers due to disease progression and individual differences. In particular, in the analysis of changes in protein expression characteristics at different stages of the disease, this invention can more sensitively reflect fluctuations in the disease state and provide a more comprehensive means of disease monitoring.

[0016] By combining the analysis of coagulation-related proteins and immunological markers, this invention not only provides a new technical approach for the identification of NMOSD and MOGAD, but also provides a new perspective for the study of the relationship between the coagulation system and immune response. Ultimately, the serum differentially expressed protein combination screening method provided by this invention can be applied in a variety of clinical scenarios, significantly improving the diagnostic efficiency and accuracy of diseases, and has great clinical application prospects and promotion value. Attached Figure Description

[0017] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used together with the embodiments of the invention to explain the invention and do not constitute a limitation thereof.

[0018] In the attached diagram: Figure 1 This is a schematic diagram of the serum differential protein combination screening process of NMOSD and MOGAD in this invention; Figure 2 This is a schematic diagram of the screening method steps of the present invention. Detailed Implementation

[0019] The preferred embodiments of the present invention will be described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are for illustration and explanation only and are not intended to limit the present invention.

[0020] Example: Figure 1-2 As shown, the present invention provides a technical solution: a screening method for serum differentially expressed protein combinations for the identification of NMOSD and MOGAD, comprising the following steps: S1. Collect peripheral blood samples from NMOSD patients and MOGAD patients respectively, and separate serum samples; S2. Detect protein expression in serum samples to obtain serum protein expression data for NMOSD and MOGAD patients; S3. Based on serum protein expression data, preliminary screening was conducted on the differences in protein expression between NMOSD patients and MOGAD patients to obtain a set of candidate differentially expressed proteins. S4. Based on the expression stability of candidate differentially expressed proteins in the two diseases, the magnitude of differences between diseases, and the intragroup variation, candidate differentially expressed proteins are screened in multiple dimensions to remove proteins with insufficient distinguishing ability or poor stability. S5. Combine and construct candidate protein combinations by combining the expression characteristics of multiple proteins; S6. Evaluate the ability of candidate protein combinations to differentiate between NMOSD and MOGAD patients, and screen for protein combinations that have a stable differentiating effect between the two diseases. S7. A combination of proteins with stable distinguishing ability is used as a serum differentially expressed protein combination for the differentiation of NMOSD and MOGAD.

[0021] According to the above technical solution, the multidimensional screening in S4 includes at least the assessment of the expression stability of candidate differentially expressed proteins in the NMOSD patient group and the MOGAD patient group. By removing proteins with large expression fluctuations or significant individual differences in the same disease population, the consistency of the retained proteins in the disease population can be ensured. The expression stability assessment is based on the expression distribution characteristics of proteins in each disease group, so that the proteins entering the subsequent combination construction steps are not dependent on the extreme expression values ​​of individual samples, thereby improving the applicability and reliability of the final protein combination in different samples.

[0022] According to the above technical solution, the multidimensional screening in S4 further includes evaluating the magnitude of the difference between candidate differentially expressed proteins in NMOSD and MOGAD. By preferentially retaining proteins that show significant differences in expression between the two diseases and whose difference direction is stable, the uncertainty of disease differentiation caused by slight expression differences is reduced. By using the above screening method, the proteins entering the protein assembly construction step can have a clear distinguishing trend between the two diseases, thereby improving the ability of the constructed protein assemblies to identify diseases.

[0023] According to the above technical solution, the combination construction in S5 is not a simple superposition of multiple differentially expressed proteins, but a combination construction based on the complementarity of different proteins in the disease differentiation process. The complementarity includes at least the complementarity of different proteins in terms of expression change direction, difference magnitude or individual distribution characteristics. By combining proteins with different distinguishing properties, the overall protein combination achieves a higher disease distinguishing effect than any single protein, thereby reducing the identification error caused by non-specific factors affecting a single protein.

[0024] According to the above technical solution, the evaluation of the ability to distinguish candidate protein combinations in step S6 includes comparing the differences in the overall expression distribution of different protein combinations in NMOSD patients and MOGAD patients, and selecting the best protein combination based on the differences in distribution. By repeatedly validating the performance of different combinations in two disease populations, protein combinations that can maintain stable distinguishing ability under conditions of sample expansion or repeated testing are screened, thereby improving the robustness of the screened protein combinations in practical applications.

[0025] According to the above technical solution, the serum differentially expressed protein combination obtained in S7 consists of at least two differentially expressed proteins, and the composition of the protein combination does not depend on the absolute expression level of a single protein, but is based on the relative expression characteristics of each protein in the combination to distinguish diseases. By using relative expression characteristics for combined screening, the obtained protein combinations can maintain good applicability under different detection platforms or experimental conditions, thereby reducing the impact of differences in detection conditions on disease identification results.

[0026] According to the above technical solution, in S4, the screening of candidate differentially expressed proteins is further calculated based on the contribution of the protein to disease differentiation, so as to quantify the ability of different proteins to distinguish between NMOSD and MOGAD. The contribution of protein differentiation is calculated according to the following formula: in, Indicates the first The distinguishing contribution of different proteins; and The values ​​represent the mean expression levels of this protein in NMOSD and MOGAD patients, respectively. and These represent the degree of dispersion in the expression of the corresponding proteins in the two disease populations, respectively. Furthermore, the contribution rate is standardized and calculated as follows: Using the above calculation method, proteins with higher differentiation contributions are preferentially retained for subsequent protein assembly and construction steps.

[0027] According to the above technical solution, in S6, a protein combination discrimination score is constructed to evaluate the overall discrimination ability of different protein combinations between NMOSD and MOGAD. Protein combination discrimination score is calculated according to the following formula: in, Indicates the protein combination differentiation score; Indicates the first in the sample to be tested Expression levels of certain proteins; This represents the average expression level of the corresponding protein in a reference disease population; Weighting coefficients assigned based on the contribution of proteins to differentiation; Furthermore, the protein combination discrimination scores of different disease populations were compared, and the discrimination index was calculated as follows: The disease identification ability of protein combinations is quantitatively assessed using the aforementioned differentiation scores and differentiation indices.

[0028] According to the above technical solution, the evaluation of candidate protein combinations in S6 further includes the calculation of combination stability and robustness to assess the reliability of protein combinations under different sample conditions. Combinatorial stability is calculated using the following formula: in, Indicates the stability score of the protein combination; and They represent the first Mean expression level and standard deviation of the protein in the disease population; Furthermore, a combined robustness index is introduced, which is calculated as follows: in, Indicates the robustness index of protein combinations; Indicates the first Protein combination discrimination scores calculated in subsample subset analysis; This represents the discrimination score calculated based on the entire sample. By combining stability scores and robustness indices, protein combinations that maintain stable discrimination ability under varying sample conditions are screened.

[0029] According to the above technical solution, the method constructs a comprehensive identification decision index based on the evaluation results of multiple algorithms, which is used to determine the protein combination finally used for the identification of NMOSD and MOGAD. The comprehensive identification decision index is calculated according to the following formula: in, Indicates comprehensive identification and decision-making indicators; Disease discrimination index representing protein composition; Indicates the stability score of the combination; Indicates the robustness index of the combination; These are the weighting coefficients; Furthermore, when the comprehensive identification decision index meets the following conditions, the protein combination is determined as the target protein combination for the identification of NMOSD and MOGAD: Through the above comprehensive evaluation and decision-making rules, a systematic screening and optimization of serum differentially expressed protein combinations can be achieved.

[0030] Finally, it should be noted that the above descriptions are merely preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for screening serum differentially expressed protein combinations for the differentiation of NMOSD and MOGAD, characterized in that: Includes the following steps: S1. Collect peripheral blood samples from NMOSD patients and MOGAD patients respectively, and separate serum samples; S2. Detect protein expression in serum samples to obtain serum protein expression data for NMOSD and MOGAD patients; S3. Based on serum protein expression data, preliminary screening was conducted on the differences in protein expression between NMOSD patients and MOGAD patients to obtain a set of candidate differentially expressed proteins. S4. Based on the expression stability of candidate differentially expressed proteins in the two diseases, the magnitude of differences between diseases, and the intragroup variation, candidate differentially expressed proteins are screened in multiple dimensions to remove proteins with insufficient distinguishing ability or poor stability. S5. Combine and construct candidate protein combinations by combining the expression characteristics of multiple proteins; S6. Evaluate the ability of candidate protein combinations to differentiate between NMOSD and MOGAD patients, and screen for protein combinations that have a stable differentiating effect between the two diseases. S7. A combination of proteins with stable distinguishing ability is used as a serum differentially expressed protein combination for the differentiation of NMOSD and MOGAD.

2. The method for screening serum differentially expressed protein combinations for the identification of NMOSD and MOGAD according to claim 1, characterized in that, The multidimensional screening in S4 includes an assessment of the expression stability of candidate differentially expressed proteins in the NMOSD and MOGAD patient groups. Proteins with large expression fluctuations or significant individual differences in the same disease population are removed to ensure the consistency of the retained proteins in the disease population.

3. The method for screening serum differentially expressed protein combinations for the identification of NMOSD and MOGAD according to claim 1, characterized in that, The multidimensional screening in S4 further includes evaluating the magnitude of the difference in candidate differentially expressed proteins between NMOSD and MOGAD, by preferentially retaining proteins that show significant and stable differences in expression between the two diseases.

4. The method for screening serum differentially expressed protein combinations for the identification of NMOSD and MOGAD according to claim 1, characterized in that, The combination construction in S5 is not a simple superposition of multiple differentially expressed proteins, but a combination construction based on the complementarity of different proteins in the disease differentiation process. The complementarity includes at least the complementarity of different proteins in terms of expression change direction, difference magnitude or individual distribution characteristics.

5. The method for screening serum differentially expressed protein combinations for the identification of NMOSD and MOGAD according to claim 1, characterized in that, The evaluation of the ability to distinguish candidate protein combinations in step S6 includes comparing the differences in the overall expression distribution of different protein combinations in NMOSD patients and MOGAD patients, and selecting the best protein combination based on the differences in distribution.

6. The method for screening serum differentially expressed protein combinations for the identification of NMOSD and MOGAD according to claim 1, characterized in that, The serum differentially expressed protein combination obtained in S7 consists of at least two differentially expressed proteins, and the composition of the protein combination does not depend on the absolute expression level of a single protein, but is based on the relative expression characteristics of each protein in the combination to distinguish diseases.

7. The method for screening serum differentially expressed protein combinations for the differentiation of NMOSD and MOGAD according to claim 1, characterized in that, In S4, the screening of candidate differentially expressed proteins is further calculated based on the contribution of the protein to disease differentiation, so as to quantify the ability of different proteins to distinguish between NMOSD and MOGAD. The contribution of protein differentiation is calculated according to the following formula: in: Indicates the first The distinguishing contribution of different proteins; and The values ​​represent the mean expression levels of this protein in NMOSD and MOGAD patients, respectively. and These represent the degree of dispersion in the expression of the corresponding proteins in the two disease populations, respectively. Furthermore, the contribution rate is standardized and calculated as follows: Using the above calculation method, proteins with higher differentiation contributions are preferentially retained for subsequent protein assembly and construction steps.

8. The method for screening serum differentially expressed protein combinations for the identification of NMOSD and MOGAD according to claim 1, characterized in that, In S6, a protein combination discrimination score is constructed to evaluate the overall discrimination ability of different protein combinations between NMOSD and MOGAD. Protein combination discrimination score is calculated according to the following formula: in: Indicates the protein combination differentiation score; Indicates the first in the sample to be tested Expression levels of certain proteins; This represents the average expression level of the corresponding protein in a reference disease population; Weighting coefficients assigned based on the contribution of proteins to differentiation; Furthermore, the protein combination discrimination scores of different disease populations were compared, and the discrimination index was calculated as follows: The disease identification ability of protein combinations is quantitatively assessed using the aforementioned differentiation scores and differentiation indices.

9. The method for screening serum differentially expressed protein combinations for the identification of NMOSD and MOGAD according to claim 1, characterized in that, The evaluation of candidate protein combinations in S6 further includes the calculation of combination stability and robustness to assess the reliability of the protein combinations under different sample conditions. Combinatorial stability is calculated using the following formula: in: Indicates the stability score of the protein combination; and They represent the first Mean expression level and standard deviation of the protein in the disease population; Furthermore, a combined robustness index is introduced, which is calculated as follows: in: Indicates the robustness index of protein combinations; Indicates the first Protein combination discrimination scores calculated in subsample subset analysis; This represents the discrimination score calculated based on the entire sample. By combining stability scores and robustness indices, protein combinations that maintain stable discrimination ability under varying sample conditions are screened.

10. The method for screening serum differentially expressed protein combinations for the identification of NMOSD and MOGAD according to claim 1, characterized in that, The method constructs a comprehensive identification decision index based on the evaluation results of multiple algorithms, which is used to determine the final protein combination used for the identification of NMOSD and MOGAD. The comprehensive identification decision index is calculated according to the following formula: in: Indicates comprehensive identification and decision-making indicators; Disease discrimination index representing protein composition; Indicates the stability score of the combination; Indicates the robustness index of the combination; These are the weighting coefficients; Furthermore, when the comprehensive identification decision index meets the following conditions, the protein combination is determined as the target protein combination for the identification of NMOSD and MOGAD: Through the above comprehensive evaluation and decision-making rules, a systematic screening and optimization of serum differentially expressed protein combinations can be achieved.

Images