Application of urine IGLV3-1 protein and / or substance for detecting urine IGLV3-1 protein

Abstract

The invention discloses application of urine IGLV3-1 protein and / or a substance for detecting the urine IGLV3-1 protein, and relates to the technical field of diagnosis and treatment of rheumatoid arthritis. According to the method, IGLV3-1 protein detection in urine is used as a marker, during detection, sample collection is noninvasive, simple, convenient and repeatable, morning urine is used as a sample source, and compared with traditional blood index detection, discomfort and risks of puncture blood sampling are avoided. Moreover, ELISA is adopted for quantitative detection, the operation process standard is unified, the result is stable and repeatable, and clinical popularization and batch detection are facilitated.

Description

Technical Field

[0001] The present invention relates to the technical field of rheumatoid arthritis diagnosis and treatment, and more particularly to the application of urine IGLV3-protein 1 or a substance for detecting urine IGLV3-1 protein. Background Art

[0002] Rheumatoid arthritis (RA) is a chronic, systemic autoimmune disease characterized by joint pain and destruction, accompanied by a pronounced inflammatory response. Its diagnosis and assessment of disease activity are crucial for treatment and management. Current assessment methods primarily rely on the Disease Activity Score in 28 joints (DAS28), which integrates the patient's number of swollen and painful joints, erythrocyte sedimentation rate (ESR) or C-reactive protein (CRP), and a subjective score of disease activity to comprehensively reflect the disease status.

[0003] In the existing technology, inflammatory indicators are often used as auxiliary references for disease monitoring of RA. However, these indicators generally have the problems of poor specificity and susceptibility to multiple inflammatory or infectious conditions, making it difficult to truly reflect the course of RA. In recent years, researchers have attempted to find biomarkers for RA through omics methods in body fluids such as blood and synovial fluid. Some of these markers, such as anti-cyclic citrullinated peptide antibodies, have been explored for diagnosis or disease typing. However, most markers have large expression heterogeneity in the population, poor reproducibility, and the detection methods have not yet been standardized, making them difficult to promote to clinical applications.

[0004] Furthermore, existing detection methods mostly rely on blood sampling, which is somewhat invasive and inconvenient for long-term, frequent monitoring. Furthermore, research on non-invasive urine biomarkers is still in its early stages. Some studies lack systematic validation and mechanistic elucidation, limiting their reliability and applicability. Therefore, there is an urgent need to develop new, stable, reproducible, and non-invasive biomarkers for the identification of RA and assessment of disease activity. Summary of the Invention

[0005] In order to achieve the above objectives, the present invention provides the use of urine IGLV3-1 protein and / or substances for detecting urine IGLV3-1 protein, which overcomes the defects that the above-mentioned non-invasive urine marker research is still in its preliminary stage and lacks systematic verification and mechanism elucidation, resulting in limited reliability and applicability.

[0006] The first aspect of the present invention provides the use of urine IGLV3-1 protein and / or a substance for detecting urine IGLV3-1 protein, wherein the use includes any one of the following:

[0007] 1) Application in the preparation of products for diagnosing rheumatoid arthritis;

[0008] 2) Use in the preparation of drugs for preventing and / or treating rheumatoid arthritis;

[0009] 3) Application in establishing computer models or programs for remote monitoring of rheumatoid arthritis recovery.

[0010] Preferably, the products include ELISA detection reagents and diagnostic kits.

[0011] Preferably, the substance for detecting urine IGLV3-1 protein is an ELISA detection reagent.

[0012] Preferably, the detection reagent specifically recognizes IGLV3-1 protein in urine.

[0013] Preferably, the urinary IGLV3-1 protein is highly expressed in the urine of rheumatoid arthritis patients and lowly expressed in the urine of normal people.

[0014] A second aspect of the present invention provides a product for diagnosing rheumatoid arthritis, comprising urine IGLV3-1 protein and / or a substance for detecting urine IGLV3-1 protein.

[0015] Preferably, the products include ELISA detection reagents and diagnostic kits.

[0016] Preferably, the substance for detecting urine IGLV3-1 protein is an ELISA detection reagent.

[0017] Preferably, the detection reagent specifically recognizes IGLV3-1 protein in urine.

[0018] The third aspect of the present invention provides a drug for preventing and / or treating rheumatoid arthritis, comprising a substance that inhibits the gene encoding the urinary IGLV3-1 protein or inhibits the expression of a signal pathway related to the expression of the urinary IGLV3-1 protein, and also comprises a pharmaceutically acceptable excipient.

[0019] A third aspect of the present invention provides a computer model for remotely monitoring the recovery of rheumatoid arthritis, comprising urine IGLV3-1 protein and / or a substance for detecting urine IGLV3-1 protein.

[0020] Preferably, the products include ELISA detection reagents and diagnostic kits.

[0021] Preferably, the substance for detecting urine IGLV3-1 protein is an ELISA detection reagent.

[0022] Preferably, the detection reagent specifically recognizes IGLV3-1 protein in urine.

[0023] A fourth aspect of the present invention provides a marker combination, comprising ARHGDIB protein, SYPL1 protein and IGLV3-1 protein;

[0024] Preferably, the ARHGDIB protein, the SYPL1 protein and the IGLV3-1 protein are all urine proteins.

[0025] A fifth aspect of the present invention provides an application of a marker combination, wherein the application includes any one of the following:

[0026] 1) Application in the preparation of products for diagnosing rheumatoid arthritis;

[0027] 2) Use in the preparation of drugs for preventing and / or treating rheumatoid arthritis;

[0028] 3) Application in establishing computer models or programs for remote monitoring of rheumatoid arthritis recovery.

[0029] Preferably, the products include ELISA detection reagents and diagnostic kits.

[0030] Through the above technical solution, it can be seen that the technical effects achieved by the present invention are:

[0031] Using urine IGLV3-1 protein as a marker, this method offers non-invasive, simple, and repeatable sample collection. Using morning urine as the sample source, this method avoids the discomfort and risks of blood sampling compared to traditional blood tests. Furthermore, the use of ELISA for quantitative testing offers standardized procedures and stable, repeatable results, facilitating clinical adoption and mass testing.

[0032] Experiments have shown that the IGLV3-1 protein detected by this method is highly correlated with the RA Disease Activity Score (DAS28), effectively differentiating disease activity and promising as an independent or auxiliary biomarker for personalized treatment guidance. This technology can also be further developed into urine test strips, automated detection modules, or intelligent systems integrated with AI predictive models to support precision medicine for RA.

[0033] In summary, the present invention not only breaks through the limitations of existing technologies in terms of detection samples, operation methods and clinical application value, but also provides a new solution for non-invasive detection and individualized management of RA, and has broad prospects for promotion and industrialization. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are merely embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying any creative work.

[0035] Attachment Figure 1 Principal component analysis and cluster analysis of differentially expressed proteins between the RA group and the healthy control group; A: principal component analysis; B: cluster diagram of differentially expressed proteins.

[0036] Attachment Figure 2 Volcano plot of differentially expressed proteins.

[0037] Attachment Figure 3 The enrichment analysis results of differentially expressed proteins; A: biological process; B: molecular function; C: cellular component; D: KEGG pathway.

[0038] Attachment Figure 4 Results of GSEA and PAS analysis of differentially expressed proteins; A: GSEA analysis; B: PAS score.

[0039] Attachment Figure 5 The results of protein interaction analysis.

[0040] Attachment Figure 6 Evaluation and validation of the GBDT model and protein importance ranking; A: GBDT model evaluation; B: importance ranking of differentially expressed proteins; HC: healthy control; RA: rheumatoid arthritis.

[0041] Attachment Figure 7 For screening of differentially expressed proteins.

[0042] Attachment Figure 8 Figure 1 shows the results of ELISA validation; A: The expression level of IGLV3-1 in the RA group and the healthy control group in the first validation; B: The expression level of SYPL1 in the RA group and the healthy control group in the first validation; C: The expression level of IGLV3-1 in the RA group and the healthy control group in the second validation; D: The expression level of SYPL1 in the RA group and the healthy control group in the second validation DETAILED DESCRIPTION

[0043] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0044] The following example randomly selected 10 RA patients from the cohort of the Department of Rheumatology and Immunology of the First Affiliated Hospital of Anhui Medical University as research subjects. In order to reduce the impact of potential age and gender differences on the results, 10 healthy controls were selected from the cohort of the Health Management Center of the hospital according to age (±2 years) and gender (male-to-female ratio 1:1). In addition, this study established a strict sample tracking and management system, and detailed records of sample collection time, processing personnel, processing date and storage location information were kept. Before the start of the study, all participants voluntarily signed an informed consent form after fully understanding the purpose, process, potential risks and benefits of the study. To protect the privacy and data security of the participants, personal identification information was anonymized. This study protocol has been approved by the Ethics Committee of Anhui Medical University (No.: 20210620). All participants collected 50mL of morning urine samples in a fasting state. These urine samples were subjected to routine urine tests in our hospital, and no obvious abnormalities were found.

[0045] Example 1

[0046] Total protein was extracted from the urine sample, and a portion was used for protein quantification and SDS-PAGE analysis to assess protein quality and integrity. The remaining protein sample was then digested with trypsin, and the resulting peptides were desalted before analysis by liquid chromatography-mass spectrometry (LC-MS / MS). The analysis revealed clear and well-resolved protein bands, with no significant tailing observed. These results demonstrate the high quality of the protein sample, making it suitable for subsequent analysis.

[0047] Example 2 Screening of differentially expressed proteins in different populations

[0048] The original data were retrieved by database retrieval, and proteins with expression values ​​≥50% in any group of samples were retained. For proteins with missing values ​​<50%, the mean of the samples in the same group was used to fill in the missing values, and the reliable proteins were obtained after MedianNormalization and log2 logarithmic transformation. Principal Component Analysis (PCA) and correlation analysis were performed on the reliable protein results, and consistency test of biological repeated samples and hierarchical cluster analysis were performed. Based on this distance, the distance matrix of the samples was constructed, and hierarchical cluster analysis was performed. This method helps to evaluate the similarities and differences between samples. The results are shown in Figure 1,Depend on Figure 1 It can be seen that although there is some overlap between the RA group and the healthy control group, the protein profiles of the two groups generally show a trend of separation ( Figure 1 In addition, the cluster heat map of differentially expressed proteins further showed the difference in protein expression profiles between the RA group and the healthy control group ( Figure 1 B in the figure). This result indicates that the protein expression of RA patients has higher disease specificity, which may reflect RA-related biological changes. Based on the credible proteins, two standards were selected to calculate the differences between samples. The fold change (FC; log2 (FC) = log2 (mean of the experimental group) - log2 (mean of the control group)) is used to evaluate the fold change in the expression level of a certain protein between samples; the P-value calculated by the t-test test shows the significance of the difference between samples. The Pearson algorithm was used to calculate the correlation between differentially expressed proteins. The closer the correlation coefficient is to 1, the higher the similarity of the expression patterns between proteins. By comparing the protein expression data of the RA group and the healthy control group, and setting FC ≥ 1.50 or ≤ 0.67, and combining the statistical standard of t-test P < 0.05, a total of 209 differentially expressed proteins were identified. Among them, 51 proteins were downregulated and 158 proteins were upregulated ( Figure 2 ).

[0049] Then, enrichment analysis methods such as Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) were used to annotate and explain the functions and pathways of the proteins. In addition, Gene Set Enrichment Analysis (GSEA) and Protein-Protein Interaction (PPI) analysis were used to conduct a deeper exploration of the pathways and biological processes in which the proteins were located, and the activation level of specific pathways was evaluated by Pathway Activation Strength (PAS). The results showed that after bioinformatics analysis of 209 differentially expressed proteins, 10 biological processes, 10 molecular functions and 10 cellular components, as well as 5 KEGG pathways involved were successfully enriched (P<0.05). The enrichment results of biological processes showed that the three main enriched processes involved response to stress, regulation of stimulus response and response to external stimuli, such as Figure 3 The enrichment results of molecular functions and cellular components are shown in Figure 3 B and Figure 3KEGG pathway enrichment analysis revealed that the differentially expressed proteins were mainly involved in the complement and coagulation cascades and the Staphylococcus aureus infection pathway ( Figure 4 D) in.

[0050] In addition, the results of GSEA showed that the upregulated proteins were mainly related to immune response, e.g. Figure 4 As shown in Figure A. PAS analysis further confirmed the activation of 12 pathways and the inhibition of 5 pathways ( Figure 4 PPI analysis results showed that RA-related differentially expressed proteins play important roles in multiple biological processes ( Figure 5 These proteins play key roles in pathways such as the complement and coagulation cascades, humoral immune responses, and acute inflammatory responses.

[0051] Finally, machine learning (ML) methods were used to screen differentially expressed proteins:

[0052] (1) Dataset division: The processed dataset is randomly divided into a training set (70%) and a test set (30%) in a ratio of 7:3.

[0053] (2) Model selection and feature screening: Five commonly used and high-performance integrated gradient boosting algorithms were selected to screen and evaluate differentially expressed proteins, including Light Gradient Boosting Machine (LightGBM), Extremely Randomized Trees (ExtraTree), Category Boosting (CategoryBoosting), Gradient Boosting Decision Tree (GBDT), and Adaptive Boosting (AdaBoost).

[0054] (3) Model evaluation: In order to evaluate the performance of the model more stably and objectively, a three-fold cross-validation method was used. The choice of three-fold cross-validation was based on the balance between data size and computing resources.

[0055] Compared to high-fold cross-validation, three-fold cross-validation reduces the number of training iterations while ensuring relatively robust model evaluation, thereby reducing computational overhead and time costs. The classification performance of the five machine learning models is shown in Table 1. The accuracies of the LightGBM, ExtraTree, Catboost, AdaBoost, and GBDT models were 0.33, 1.00, 1.00, 0.67, and 0.67, respectively. While the ExtraTree and Catboost models performed well across all metrics, the GBDT model achieved the best average performance, reaching 0.73, and was therefore considered the most suitable model.

[0056] Table 1

[0057]

[0058] (4) Model performance evaluation: Precision, recall, accuracy, and F1 score are used to measure model performance. The F1 score, as the harmonic mean of precision and recall, is used to evaluate the overall performance of the model. These evaluation metrics generally range from 0 to 1, with higher scores indicating better predictive performance. When these metrics are close to or reach 1, it generally indicates that the model performs well on the training data. However, if all of these metrics reach 1, this may be a sign of overfitting of the model.

[0059] (5) Protein importance score and biomarker determination: After the model performance evaluation is completed, the optimized ML algorithm is applied to analyze the contribution of each protein to the model prediction accuracy, which is the so-called feature importance score. The markers are screened by examining the correlation coefficient and cumulative area under the curve (AUC) of the model, and the protein combinations with rich information and the least number are selected as biomarkers. Generally, an AUC value greater than 0.8 indicates that the model has high prediction accuracy and good fitting effect. The results are shown in Figure 6 , Figure 6 Figure A shows the confusion matrix of the GBDT classification model, which shows good classification performance and stability in both the training set and the test set. This result is further confirmed by three-way cross-validation. Figure 6 Panel B shows the top 10 differentially expressed proteins ranked by importance based on the GBDT classification model.

[0060] The accuracy of using single or multiple differentially expressed proteins for RA diagnosis was calculated based on importance ranking and logistic regression algorithm (Table 2). The final selected proteins were shown as follows: Figure 7When using RhoGDP-dissociationinhibitor2 (ARHGDIB) alone for diagnosis, the accuracy reached 90%. However, when the first three differentially expressed proteins, Immunoglobulin lambda variable 3-1 (IGLV3-1), Synaptophysin-like protein 1 (SYPL1), and ARHGDIB, were used together, the accuracy increased to 99%. Considering the diagnostic accuracy and ease of use, the combination of these three proteins achieved the best diagnostic results.

[0061] Table 2

[0062]

[0063] Example 3 ELISA Verification of Urine Protein Biomarkers

[0064] The potential urine biomarkers identified by the GBDT classification model were validated in two independent cohorts using ELISA. The first validation involved urine samples from 12 participants, including 6 RA patients (83.3% female, mean age 53.0 years, age range 42.0-58.0 years) and 6 healthy controls (83.3% female, mean age 52.8 years, age range 44.0-57.0 years). The second validation was expanded to 84 participants, including 42 RA patients (73.8% female, mean age 56.9 years, age range 35.0-72.0 years) and 42 healthy controls (73.8% female, mean age 56.8 years, age range 35.0-71.0 years).

[0065] See the results Figure 8 Two independent validation results showed that IGLV3-1 expression levels were elevated in RA patients compared with healthy controls (P<0.05). However, SYPL1 did not show statistical differences between the two groups, and ARHGDIB expression levels were below the detection limit, failing to successfully validate its potential as a biomarker in this study.

[0066] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the various embodiments can be referenced to each other.

[0067] The above description of the disclosed embodiments is intended to enable one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be readily apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention is not limited to the embodiments shown herein but is intended to conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. Use of urine IGLV3-1 protein and / or a substance for detecting urine IGLV3-1 protein, characterized in that: The application includes any of the following: 1) Application in the preparation of products for diagnosing rheumatoid arthritis; 2) Use in the preparation of drugs for preventing and / or treating rheumatoid arthritis; 3) Application in establishing computer models or programs for remote monitoring of rheumatoid arthritis recovery. Among them, the products include ELISA detection reagents and diagnostic kits.

2. The use according to claim 1, characterized in that The substance for detecting urine IGLV3-1 protein is an ELISA detection reagent; the detection reagent specifically recognizes IGLV3-1 protein in urine.

3. The use according to claim 1, characterized in that The urinary IGLV3-1 protein is highly expressed in the urine of patients with rheumatoid arthritis and is lowly expressed in the urine of normal people.

4. A product for diagnosing rheumatoid arthritis, characterized in that: Including urine IGLV3-1 protein and / or substances for detecting urine IGLV3-1 protein; The products include ELISA test reagents and diagnostic kits; The substance for detecting urine IGLV3-1 protein is an ELISA detection reagent; the detection reagent specifically recognizes IGLV3-1 protein in urine.

5. A drug for preventing and / or treating rheumatoid arthritis, characterized in that: It includes a substance that inhibits the gene encoding the urine IGLV3-1 protein or inhibits the expression of a signal pathway related to the expression of the urine IGLV3-1 protein, and also includes pharmaceutically acceptable excipients.

6. A computer model for remotely monitoring the recovery of rheumatoid arthritis, characterized in that: Including urine IGLV3-1 protein and / or substances for detecting urine IGLV3-1 protein; The products include ELISA test reagents and diagnostic kits; The substance for detecting urine IGLV3-1 protein is an ELISA detection reagent; the detection reagent specifically recognizes IGLV3-1 protein in urine.

7. A marker combination, characterized in that The marker combination includes ARHGDIB protein, SYPL1 protein and IGLV3-1 protein; Wherein, the ARHGDIB protein, the SYPL1 protein and the IGLV3-1 protein are all urine proteins.

8. The use of the marker combination according to claim 7, characterized in that: The application includes any of the following: 1) Application in the preparation of products for diagnosing rheumatoid arthritis; 2) Use in the preparation of drugs for preventing and / or treating rheumatoid arthritis; 3) Application in establishing computer models or programs for remote monitoring of rheumatoid arthritis recovery. Among them, the products include ELISA detection reagents and diagnostic kits.

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