Use of protein markers in preparing diagnostic kit for rheumatoid arthritis

Abstract

The present application relates to the application of protein markers in the preparation of a diagnostic kit for rheumatoid arthritis, and belongs to the technical field of biological medicine. The present application screens protein markers for diagnosing rheumatoid arthritis from plasma and plasma exosomes. Specifically, the AUC of C4BPA from plasma exosomes is 0.878, and the specificity is as high as 94.1%, which has a significant advantage in excluding non-RA populations. C4BPA and diamine oxidase (DAO) from plasma both have good diagnostic value in the independent diagnosis of RA, with AUCs of 0.844 and 0.946, respectively. The combined diagnostic model based on plasma C4BPA and plasma DAO has an AUC of 0.965. Therefore, the protein markers of the present application have accurate diagnostic effects on rheumatoid arthritis, and have good application prospects for the auxiliary diagnosis and risk assessment of RA.

Description

Technical Field

[0001] This invention relates to the field of biomedical technology, and in particular to the application of protein biomarkers in the preparation of diagnostic kits for rheumatoid arthritis. Background Technology

[0002] The diagnosis and risk assessment of rheumatoid arthritis (RA) currently rely primarily on clinical manifestations, imaging examinations, and laboratory indicators. Commonly used serological tests include rheumatoid factor (RF), anti-citrullinated protein antibodies (ACPA), and inflammatory markers (such as C-reactive protein and erythrocyte sedimentation rate), combined with imaging (ultrasound, X-ray, or MRI) to assess joint inflammation and bone erosion. However, the current diagnostic system still has significant limitations: First, in the early stages of RA or when disease activity is low, clinical manifestations and inflammatory markers may be atypical; second, the diagnostic sensitivity based on traditional serological markers is insufficient, with some patients being "serologically negative" (RF and / or ACPA negative), leading to missed diagnoses; third, inflammatory markers are easily affected by infection, comorbidities, and medications, limiting their specificity; fourth, while imaging examinations are helpful in assessing joint damage, their application in early screening, dynamic monitoring, and large-scale populations is still limited by cost, equipment, and operational dependence. Therefore, there is an urgent need for novel indicators with stable sources, high sensitivity and high specificity to establish stable and reliable early diagnosis and risk assessment models to help RA early diagnosis, early treatment and precise management. Summary of the Invention

[0003] Therefore, the technical problem to be solved by the present invention is to overcome the problem of insufficient sensitivity and limited specificity of existing diagnostic markers for rheumatoid arthritis.

[0004] To address the aforementioned technical problems, this invention provides the application of protein biomarkers in the preparation of diagnostic kits for rheumatoid arthritis (RA). This invention screened protein biomarkers for diagnosing RA from plasma and plasma exosomes. Specifically, the area under the ROC curve (AUC) of complement-binding protein α chain (C4BPA) from plasma exosomes was 0.878, with a specificity as high as 94.1%, demonstrating a significant advantage in excluding non-RA individuals. Both plasma-derived C4BPA and diamine oxidase (DAO) showed good diagnostic value in the independent diagnosis of RA, with AUCs of 0.844 and 0.946, respectively. A combined diagnostic model based on plasma C4BPA and plasma DAO further improved the AUC to 0.965. Therefore, the protein biomarkers of this invention have accurate diagnostic effects for RA and show promising translational applications for the auxiliary diagnosis and risk assessment of RA.

[0005] The first objective of this invention is to provide the application of a reagent for detecting the content of protein biomarkers in the preparation of a diagnostic kit for rheumatoid arthritis, wherein the protein biomarkers include complement-binding protein α chain and / or diamine oxidase.

[0006] Furthermore, the test sample for the diagnostic kit is plasma and / or plasma exosomes.

[0007] Furthermore, the C4BPA is derived from plasma or plasma exosomes, and the DAO is derived from plasma.

[0008] Furthermore, the UniProt number of the complement-binding protein α chain is P04003, and the UniProt number of the diamine oxidase is P14920.

[0009] A second objective of the present invention is to provide a diagnostic kit for rheumatoid arthritis, the diagnostic kit containing reagents for detecting the levels of protein biomarkers, said protein biomarkers including complement-binding protein α chain and / or diamine oxidase.

[0010] Furthermore, the diagnostic kit also includes reagents for extracting exosomes from plasma.

[0011] Furthermore, the reagents include those for detecting the concentration or content of the protein marker in the sample to be tested by nuclear magnetic resonance, chromatography, spectroscopy, mass spectrometry, or a combination thereof.

[0012] A third objective of this invention is to provide a reagent for detecting the concentration or content of a protein marker in a test sample, wherein the protein marker is the aforementioned protein marker, and the test sample is plasma or plasma exosomes.

[0013] Furthermore, the application of protein biomarkers in the preparation of diagnostic products for rheumatoid arthritis, wherein the protein biomarkers are those described above.

[0014] Furthermore, the rheumatoid arthritis diagnostic product includes antibodies or probes that can specifically recognize and bind to the protein markers.

[0015] A fourth objective of this invention is to provide an application of a protein biomarker in constructing a rheumatoid arthritis prediction model, wherein the protein biomarker is the aforementioned protein biomarker.

[0016] Compared with the prior art, the above-described technical solution of the present invention has the following advantages:

[0017] This invention provides protein biomarkers that can be used for the auxiliary diagnosis of rheumatoid arthritis (RA). The area under the ROC curve (AUC) of plasma exosome C4BPA is 0.878, with a specificity as high as 94.1%, demonstrating a significant advantage in excluding non-RA individuals. Both plasma C4BPA and plasma DAO show good diagnostic value in the independent diagnosis of RA, with AUCs of 0.844 and 0.946, respectively. The combined diagnostic model based on plasma C4BPA and plasma DAO further improves the AUC to 0.965. Attached Figure Description

[0018] Figure 1 This is a characteristic identification diagram of plasma exosomes (EVs). In this diagram, A is the particle size distribution and statistical parameters obtained by nanoparticle tracking analysis (NTA). D10 represents the particle size corresponding to a cumulative distribution ratio of 10% in the system, and D90 represents the particle size corresponding to a cumulative distribution ratio of 90% in the system. B is a morphological observation image of EVs by transmission electron microscopy (TEM), taken at a scale bar of 200 nm. Typical membrane vesicle-like structures are visible, consistent with the morphological characteristics of EVs.

[0019] Figure 2 This is a graph showing the differential expression of plasma exosomes in Example 1, where A is a volcano plot of differentially expressed proteins; and B is a graph showing the results of LASSO regression feature screening.

[0020] Figure 3 This is a graph showing the ELISA validation results of HRG, CPB2, C4BPA, and SCYL1 at the plasma level.

[0021] Figure 4 The graphs show the differences in the expression of C4BPA and DAO in plasma exosomes and plasma. In the graphs, A represents the expression level of C4BPA in plasma exosomes between the RA patient group and the healthy control group; B represents the difference in DAO concentration in plasma between the RA patient group and the healthy control group in Example 2.

[0022] Figure 5 These are diagnostic efficacy graphs for C4BPA in plasma exosomes and DAO in plasma. A is the diagnostic efficacy evaluation graph for RA of C4BPA in plasma exosomes, and B is the diagnostic efficacy evaluation graph for RA of DAO in plasma.

[0023] Figure 6 This is a graph showing the difference in expression levels of plasma C4BPA and plasma DAO between the RA validation group and the healthy validation group in Example 3.

[0024] Figure 7 This is a comparison of ROC curves of single-index models and combined models of plasma C4BPA and plasma DAO in the RA validation group and healthy validation group in Example 3 for the diagnosis of RA. Detailed Implementation

[0025] The present invention will be further described below with reference to the accompanying drawings and specific embodiments, so that those skilled in the art can better understand and implement the present invention. However, the embodiments described are not intended to limit the present invention.

[0026] To facilitate understanding of the overall implementation scheme and verification path of the present invention, the sample types, sample sizes, detection methods, and corresponding biomarkers of each embodiment are summarized in Table 1. Specifically, Example 1 screens protein biomarkers for RA based on plasma exosome proteomics and evaluates their diagnostic efficacy; Examples 2 and 3 use enzyme-linked immunosorbent assay (ELISA) to test for indicators such as DAO and C4BPA in plasma samples and evaluate their diagnostic efficacy.

[0027] Table 1 Sample Information Related to the Examples

[0028]

[0029] Example 1: Plasma exosome proteomics analysis, screening and evaluation of RA protein biomarkers

[0030] 1. Research Subjects

[0031] Rheumatoid arthritis (RA) participants meeting the inclusion and exclusion criteria were recruited from the Rheumatology and Immunology Outpatient Department of the First Affiliated Hospital of Soochow University; healthy volunteers were female participants without RA from the same population. All study subjects were from the same population, namely Han Chinese residing in Suzhou City, Jiangsu Province. The study was approved by the Ethics Committee of Soochow University, and all participants signed written informed consent forms.

[0032] (1) Sample found: 26 patients with RA and 68 healthy controls. RA group: 45.8±10.5 years old; healthy control group: 67.0±9.4 years old. Age was used as a covariate to correct for confounding effects in the statistical analysis.

[0033] (2) Discovery sample subset: 18 healthy control group members (age 58.7±14.0 years) were selected from the discovery sample subset and 26 RA disease group members were selected to form the discovery sample subset for screening and cross-validation of RA-related proteins.

[0034] 2. Inclusion and Exclusion Criteria

[0035] Inclusion criteria: Han Chinese, female; RA patients diagnosed according to the 2010 American College of Rheumatology / European League Against Rheumatism (ACR / EULAR) classification criteria for rheumatoid arthritis.

[0036] Exclusion criteria: history of cancer, severe infection, recent use of antibiotics, use of probiotics, known history of inflammatory bowel disease, other autoimmune diseases (such as systemic lupus erythematosus, multiple sclerosis, etc.), diabetes, and other diseases that affect immune or metabolic status. All participants signed informed consent forms.

[0037] 3. Blood sample collection and plasma separation

[0038] Peripheral venous blood was collected from the subjects, and plasma was separated according to established standard operating procedures (e.g., fasting collection in the morning, followed by centrifugation to remove blood cells and debris after standing at room temperature). The plasma was aliquoted and stored at -80°C for later use. Repeated freeze-thaw cycles were avoided on the plasma samples before subsequent exosome extraction.

[0039] 4. Isolation and identification of plasma exosomes

[0040] (1) Capture of exosomes (EVs)

[0041] Add 200 µL of plasma to a 2 mL centrifuge tube. Add 40 µL of magnetic beads (EVlent Magnetic) and mix thoroughly. Add 200 µL of Incubation Buffer I. Gently incubate at room temperature for 3 h (to allow the EVs to fully bind to the magnetic beads).

[0042] (2) Removing impurities and washing

[0043] Adsorb magnetically for 3 min, then discard the supernatant. Add 1 mL of Incubation Buffer II, and mix by inverting approximately 20 times. Adsorb magnetically for 3 min, then discard the supernatant. Add 1 mL of Washing Buffer, and mix by inverting approximately 20 times. Adsorb magnetically for 3 min, then discard the supernatant; repeat the washing process a total of 3 times.

[0044] (3) EVs were obtained by elution

[0045] Add 100 µL of elution buffer and vortex / vibrate for 15 min. Magnetically aspirate for 3 min and collect the supernatant (elution buffer 1). Add another 100 µL of elution buffer, vortex for 15 min, magnetically aspirate for 3 min, and collect the supernatant (elution buffer 2). Combine elution buffers 1 and 2 to obtain EV eluent (total volume approximately 200 µL).

[0046] (4) Characteristic identification of EV

[0047] The particle size was mainly distributed in the range of 60-200 nm by a nanoparticle tracking analyzer; transmission electron microscopy revealed a typical membrane-like vesicle structure, consistent with the morphological characteristics of EVs.

[0048] 5. Quantitative proteomics detection and differential protein screening were performed using four-dimensional data-independent acquisition technology (4D-DIA).

[0049] (1) Extraction and pretreatment of exosome proteins

[0050] ① Take the plasma exosome (EV) sample obtained from the sample found in Example 1, add exosome-specific lysis buffer, and collect the supernatant after complete lysis as protein extraction solution. The protein concentration is determined using the quinolinic acid (BCA) method.

[0051] ② Enzymatic desalting: Using the Nomi Micro / Universal Proteomics Digestion Kit (QLBIO MagicOmics-MMB8X), add 20 μL of protein to an octet containing MMB beads (the kit's magnetic beads), and incubate at 37°C for 30 min. Add 45 μL of binding buffer and incubate at room temperature with shaking for 15 min. After incubation, discard the supernatant and wash the MMB beads three times with washing buffer. Resuspend the magnetic beads in 20 μL of digestion working solution and incubate at 37°C for at least 4 h. After incubation, add 5 μL of quench buffer to terminate the digestion and lyophilize.

[0052] ③ Fractionation: The lyophilized peptide sample was dissolved in 100 μL of mobile phase A (100% water, 0.1% formic acid), centrifuged at 14000g for 20 min, and the supernatant was collected for fractionation using high-performance liquid chromatography (HPLC). The flow rate was 0.7 mL / min, and the separation gradient is shown in Table 2. Mobile phase B consisted of 80% acetonitrile and 0.1% formic acid.

[0053] Table 2 Separation Gradients

[0054]

[0055] (2) Mass spectrometry analysis using liquid chromatography-mass spectrometry (LC-MS / MS)

[0056] ① Data-dependent acquisition mode (DDA) mass spectrometry setup

[0057] Mobile phases A (100% water, 0.1% formic acid) and B (80% acetonitrile, 0.1% formic acid) were prepared. The lyophilized powder was dissolved in 10 µL of mobile phase A, centrifuged at 14000 g for 20 min at 4 °C, and 400 ng of the supernatant was injected for LC-MS analysis. The elution conditions for LC-MS are shown in Table 3. A timsTOF_HT mass spectrometer with a captive spray ion source was used. Data-dependent acquisition (DDA) mode was employed, with a mass-to-charge ratio (m / z) of 300–1500 and a first-order mass spectrometry resolution of 60000 (1222 m / z). The accumulation time in the TIMS tunneling was set to 50 ms. The capillary voltage was set to 1.5 kV, and the mobility was 0.70–1.30 cm⁻¹. 2 / (V). The total cycle time was 1.23 s. Raw mass spectrometry detection data were generated. The separation flow rate was 300 nL / min, and the separation gradient is shown in Table 3.

[0058] Table 3 Separation Gradients

[0059]

[0060] ② DIA sample testing

[0061] Mobile phases A (100% water, 0.1% formic acid) and B (80% acetonitrile, 0.1% formic acid) were prepared. The lyophilized powder was dissolved in 10 µL of mobile phase A, centrifuged at 14000 g for 20 min at 4 °C, and 400 ng of the supernatant was injected for LC-MS analysis. The elution conditions for LC-MS are shown in Table 4, with a flow rate of 500 nL / min. A TIMS TOF_HT mass spectrometer with a Captive Spray ion source was used. Mass spectrometry was acquired in data-independent acquisition (DIA) mode, with a mass-to-charge ratio (m / z) of 100–1700. The primary mass spectrometry resolution was set to 60000 (1222 m / z), and the accumulation time in the TIMS tunnel was set to 100 ms. The capillary voltage was set to 1.6 kV, and the mobility was 0.6–1.6 cm⁻¹. 2 / (V). The total cycle time is 1.1 s, with 10 parallel accumulation-serial fragmentation (PASEF) cycles to generate raw mass spectrometry detection data.

[0062] Table 4 Separating Gradients

[0063]

[0064] (3) Data processing

[0065] ① Database: The selection of a database is based on the required species, the completeness of the database annotation, and the reliability of the sequences. The following principles are followed when selecting a database: if the organism has already been sequenced, directly select the database for that species; if the organism has not been sequenced, select the proteomics database of the major class most relevant to the sample being tested. The database used in this study is: Homosapiens SP (protein count: 20,407, database: uniprot).

[0066] ② Search software: Use Spectronaut software for database search. The parameter settings are shown in Table 5.

[0067] Table 5 Parameter Settings

[0068]

[0069] ③ Quantification and normalization: Quantify the DIA data. The quantitative values ​​can be transformed by log2 and normalized between samples. Missing values ​​can be imputed using methods such as the K-Nearest Neighbors (KNN) algorithm.

[0070] ④ Quality Control (QC): Sample consistency, outliers, and batch drift can be assessed through principal component analysis (PCA), clustering, correlation heatmaps, etc.; if necessary, mixed QC samples can be added for process monitoring.

[0071] (4) Screening of RA protein markers in exosomes

[0072] In the sample, the expression levels of EV protein were compared between the RA patient group and the healthy control group. The differential expression threshold was set as follows: fold change (FC) ≥ 2 or FC ≤ 0.5, and the p-value was corrected for Bonferroni. A total of 156 differentially expressed proteins were identified, of which 61 were upregulated and 95 were downregulated. Figure 2 (A in the middle).

[0073] Based on the differentially expressed protein set, LASSO (Least Absolute Contraction and Selection Operator) regression was further used for feature screening, resulting in a set of 8 candidate proteins. The relative importance of each candidate protein in the LASSO model is as follows: Figure 2 As shown in B in the diagram.

[0074] Table 6. Candidate Proteins (English-Chinese Bilingual)

[0075]

[0076] Differential analysis was performed on the discovered sample subset using the same data processing workflow to obtain the differentially expressed proteins and their directions of difference within the subset. The intersection of these differentially expressed proteins with the eight candidate proteins identified above yielded four proteins: HRG, CPB2, C4BPA, and SCYL1, which were determined as candidate protein biomarkers for RA.

[0077] 6. Feasibility assessment of plasma-level ELISA validation and translation

[0078] To evaluate the detectability and consistency of candidate biomarkers in routine blood testing scenarios, this embodiment selected 40 cases from the RA validation group and 40 cases from the healthy validation group used in Example 3 as samples. ELISA was used to quantitatively detect the plasma free levels of the four candidate differentially expressed proteins (C4BPA, CPB2, SCYL1, and HRG) obtained from screening. Figure 3 The results showed that, compared with the healthy validation group, the RA validation group had increased plasma C4BPA levels and decreased CPB2 levels; no significant differences were observed in SCYL1 and HRG. Further analysis of plasma exosomal proteomics results revealed that the direction of C4BPA variation at the free plasma level was consistent with the trend at the plasma exosomal level. Figure 4 (A in the original text). This result confirms that C4BPA has cross-sample type stability and is of greater clinical development value.

[0079] 7. ROC Performance Evaluation

[0080] ROC curve analysis showed that exosome C4BPA exhibited excellent diagnostic efficacy in distinguishing RA patients from healthy controls. Its area under the curve (AUC) was as high as 0.878 (95% confidence interval (CI): 0.774–0.962). At a cutoff value of 3.416, the specificity of this biomarker reached 94.1%, and the sensitivity was 76.9%. Figure 5 The results indicate that exosome C4BPA has excellent diagnostic performance (as shown in Figure A). This result demonstrates that exosome C4BPA has extremely high diagnostic reference value, can effectively reduce the false positive rate, and meets the high specificity requirements for accurate clinical diagnosis.

[0081] Example 2: ROC evaluation of plasma DAO

[0082] 1. Sample Information

[0083] The sample used in Example 2 is plasma, and the rest of the information is the same as in Example 1.

[0084] 2. DAO detection and ROC evaluation

[0085] (1) Plasma DAO levels were measured according to the ELISA kit instructions. A standard curve was established and sample concentrations were calculated. Due to the large sample size, single-well assays were performed in this ELISA test, and the concentrations were obtained by conversion from the standard curve. The results are shown in […]. Figure 4 B in the middle.

[0086] (2) ROC curve analysis was performed using plasma DAO concentration as the discriminant variable. Figure 5The results showed that DAO (diethyltoluamide) has good auxiliary diagnostic value. The area under the curve (AUC) of this indicator was 0.808 (95% CI: 0.700-0.892). When the cutoff value was set at 84.821 pg / mL, its sensitivity was as high as 92.3% and its specificity was 76.5%. This high sensitivity indicates that plasma DAO can effectively identify the vast majority of RA patients and plays an important role in early clinical screening, helping to reduce the missed diagnosis rate.

[0087] Example 3: ROC evaluation of plasma DAO and C4BPA in independent samples

[0088] 1. Sample Information

[0089] The sample in this embodiment is independent of those in Examples 1 and 2, but the inclusion and exclusion criteria, plasma collection and separation methods are the same as in Example 1. This embodiment includes 40 cases in the RA validation group and 40 cases in the healthy validation group; the RA validation group is 45.8±14.9 years old, and the healthy validation group is 65.3±0.9 years old. To control for potential confounding factors of age, age was included as a covariate in the subsequent model analysis and correction.

[0090] 2. Single-index detection and ROC evaluation

[0091] (1) Detection of plasma biomarker levels

[0092] Strictly following the ELISA kit instructions, the levels of C4BPA and DAO in plasma samples from each group were measured. Sample concentrations were calculated using a standard curve. The results showed that the plasma C4BPA and DAO levels in the RA validation group were significantly higher than those in the healthy validation group (e.g., ...). Figure 6 (As shown).

[0093] (2) ROC curve evaluation: Receiver operating characteristic curves (ROC curves) were plotted using plasma C4BPA and DAO concentrations as discriminant variables to assess their independent diagnostic value. The analysis results showed that plasma C4BPA had an area under the curve (AUC) of 0.844 (95% CI: 0.756-0.921), exhibiting good specificity (see [link to analysis]). Figure 7 Plasma DAO: demonstrated excellent diagnostic performance, with an AUC as high as 0.946 (95% CI: 0.887-0.990), and both sensitivity and specificity were at a high level (see...). Figure 7 ).

[0094] (3) C4BPA and DAO joint prediction model

[0095] A combined diagnostic model based on plasma C4BPA and DAO concentrations was constructed using logistic regression analysis, and the corresponding ROC curves were plotted. The results showed that the combined model (C4BPA+DAO) achieved an AUC of 0.965 (95% CI: 0.918-0.997), significantly superior to either single-indicator detection (see [link to relevant documentation]). Figure 7 This combined approach, while maintaining high specificity, further enhances diagnostic sensitivity, demonstrating the synergistic effect of dual biomarker combined detection in the accurate diagnosis of RA.

[0096] Obviously, the above embodiments are merely illustrative examples for clear explanation and are not intended to limit the implementation. Those skilled in the art will recognize that other variations or modifications can be made based on the above description. It is neither necessary nor possible to exhaustively list all possible implementations here. However, obvious variations or modifications derived therefrom are still within the scope of protection of this invention.

Claims

1. The application of reagents for detecting protein biomarker content in the preparation of diagnostic kits for rheumatoid arthritis, characterized in that, The protein markers include complement-binding protein α chain, or a combination of complement-binding protein α chain and diamine oxidase; When the protein marker is a complement-binding protein α chain, the protein marker is derived from plasma or plasma exosomes; When the protein biomarker is a combination of complement-binding protein α chain and diamine oxidase, the protein biomarker is derived from plasma.

2. A diagnostic kit for rheumatoid arthritis, characterized in that, The diagnostic kit contains reagents for detecting the levels of the protein biomarkers described in claim 1.

3. The diagnostic kit according to claim 2, characterized in that, The diagnostic kit also includes reagents for extracting exosomes from plasma.

4. The diagnostic kit according to claim 2, characterized in that, The reagents include those for detecting the concentration or content of the protein markers in the sample to be tested by nuclear magnetic resonance, chromatography, spectroscopy, mass spectrometry, or a combination thereof.

5. A reagent for detecting the concentration or content of protein markers in a sample, characterized in that, The protein biomarker is the protein biomarker described in claim 1.

6. The application of protein biomarkers in the preparation of diagnostic products for rheumatoid arthritis, characterized in that, The protein biomarker is the protein biomarker described in claim 1.

7. The application according to claim 6, characterized in that, The diagnostic product includes equipment for analyzing the levels of the protein markers.

8. The application according to claim 6, characterized in that, The rheumatoid arthritis diagnostic products include antibodies, probes, or aptamers that can specifically recognize and bind to the protein markers.

9. The application of protein biomarkers in constructing predictive models for rheumatoid arthritis, characterized in that, The protein biomarker is the protein biomarker described in claim 1.

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