Protein marker for predicting frequent gout attacks and application thereof

Abstract

The present application relates to the field of protein detection, and particularly relates to a protein marker for predicting frequent onset of gout and application thereof. The protein marker is selected from at least one of PPA5, SAA1, SAA2, SAA4, PEDF, C1R, PON3, B2MG and CAB45. The present application also provides a kit and system for predicting frequent onset of gout, and relates to an information data processing terminal, a computer readable storage medium and an apparatus. The present application provides a systematic technical solution for early warning, risk stratification and precise intervention of frequent onset of gout, and has significant clinical application value.

Description

Technical Field

[0001] This invention relates to the field of protein detection, and more specifically to a protein biomarker for predicting frequent gout attacks and its application. Background Technology

[0002] Gout is a prevalent inflammatory joint disease caused by the deposition of monosodium urate (MSU) crystals in and around joints. Its pathophysiological basis is hyperuricemia resulting from purine metabolism disorders and / or decreased uric acid excretion. With significant changes in global living environment, lifestyle, and dietary structure, the incidence and prevalence of gout are increasing annually worldwide. Chronic gout can lead to joint structural damage, loss of function, and even joint deformities and limited mobility. Late-stage patients often experience serious complications such as tophi formation, skin ulcers, and secondary infections.

[0003] According to international consensus, gout attacks can be divided into two types: frequent attacks and infrequent attacks. Frequent attacks are generally defined as ≥2 attacks per year, while infrequent attacks are defined as <2 attacks per year. Patients with frequent attacks face a heavier disease burden and a worse prognosis. The 2020 American College of Rheumatology (ACR) guidelines for gout management strongly recommend initiating uric acid-lowering therapy (ULT) for patients with frequent attacks, while conditionally recommending ULT for patients with infrequent attacks. Therefore, early identification of high-risk groups for frequent attacks is crucial for achieving individualized and precise treatment. Current clinical guidelines recommend the widespread use of colchicine or nonsteroidal anti-inflammatory drugs (NSAIDs) for prevention within 3-6 months after ULT initiation to reduce attack risk. Although this approach reduces the incidence of attacks, it exposes a significant proportion of patients with inherently low attack risk to unnecessary drug intervention. Prophylactic drugs have significant adverse event characteristics—colchicine often causes gastrointestinal intolerance, especially in elderly patients and patients with chronic kidney disease, where the risks of prevention may outweigh the benefits. On the other hand, some patients still have a high risk of frequent seizures 6 months later, and current guidelines lack clear recommendations for lifestyle interventions, which may lead to the discontinuation of protection for these high-risk patients at inappropriate times. Therefore, there is an urgent need for predictive biomarkers that can stratify patients' seizure risk, thereby enabling individualized prevention strategies.

[0004] Existing clinical indicators used to assess gout activity, such as serum uric acid levels, C-reactive protein (CRP), or erythrocyte sedimentation rate (ESR), all have significant limitations. Serum uric acid reflects metabolic control status and has a weak correlation with acute inflammatory activity; CRP and ESR, as non-specific inflammatory markers, cannot distinguish between gout-specific inflammatory pathways and systemic inflammation caused by other comorbidities (such as metabolic syndrome or infections). Furthermore, there is insufficient consistency between different research results. More importantly, proteomics studies specifically targeting attack risk stratification remain scarce. There is still an urgent need for biomarkers and predictive systems or models to accurately predict frequent and infrequent gout attacks during uric acid-lowering therapy. Summary of the Invention

[0005] The purpose of this invention is to provide new uses for PPA5, SAA1, SAA2, SAA4, PEDF, C1R, PON3, B2MG, and CAB45 as protein biomarkers in the preparation of products that predict the risk of frequent gout attacks during uric acid-lowering therapy in gout patients.

[0006] In a first aspect, the present invention provides a protein biomarker for predicting frequent gout attacks, said protein biomarker being selected from at least one of PPA5, SAA1, SAA2, SAA4, PEDF, C1R, PON3, B2MG, and CAB45.

[0007] Frequent gout attacks are defined as ≥2 attacks per year.

[0008] Furthermore, the risk of frequent gout attacks is higher when the expression of PPA5, SAA1, SAA2, SAA4, PEDF, and C1R genes is upregulated and / or the expression of PON3, CAB45, and B2MG genes is downregulated.

[0009] A second aspect of the invention provides the use of the aforementioned protein biomarker in the preparation of products for predicting frequent gout attacks. Optionally, the product is selected from any of the following: kits, chips, antibodies, test strips, biosensors, or combinations thereof.

[0010] In a third aspect, the present invention provides a kit for predicting the risk of frequent gout attacks, the kit comprising reagents for detecting the concentration and / or abundance of the said protein biomarker.

[0011] Furthermore, the kit also includes reagents for extracting the protein markers from serum.

[0012] Optionally, the detection reagent is a specific monoclonal antibody or polyclonal antibody or antigen-binding fragment of a protein marker.

[0013] Optionally, the kit includes: (a) Specific capture antibodies for each protein marker, said antibodies immobilized on a solid-phase support; (b) Specific detection antibodies for each protein biomarker, said antibodies carrying a detectable label; (c) Standards for each protein marker.

[0014] Frequent gout attacks are defined as ≥2 attacks per year.

[0015] Optionally, the kit may further comprise one or more components selected from the following: coating buffer, blocking buffer, washing buffer, chromogenic buffer, stop buffer, standards for various protein markers, quality control, positive control, and negative control.

[0016] Optionally, the kit is selected from any of the following: enzyme-linked immunosorbent assay kit, chemiluminescent immunoassay kit, immunochromatographic test strip, and electrochemiluminescence detection kit.

[0017] Optionally, the biological sample is serum or plasma.

[0018] In a fourth aspect, the present invention provides a system for predicting the risk of frequent gout attacks, comprising: (a) A data acquisition module for acquiring concentration and / or abundance data of the protein markers in the serum of gout patients; (b) A data processing module that compares the data obtained in module (a) with a predetermined threshold in order to determine the risk of frequent gout attacks in the gout patient; (c) Output module, used to output the risk assessment results.

[0019] Optionally, the data acquisition module is used to obtain the amount of protein after standardized processing of the protein marker.

[0020] Optionally, the predetermined threshold in the data processing module is the concentration and / or abundance of the marker in the serum of normal individuals or individuals with infrequent attacks.

[0021] Optionally, the output module is also used to output risk stratification results and individualized treatment recommendations.

[0022] In a fifth aspect, the present invention provides an information data processing terminal, the information data processing terminal comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the processor executes the computer program to calculate the system.

[0023] A sixth aspect of the present invention provides a computer-readable storage medium having a computer program stored thereon, the program performing the following steps when executed by a processor: (1) Receive data on various protein biomarkers from the subjects; (2) Substitute the above data into the preset risk scoring model to calculate the risk score; (3) Output the predicted probability of frequent gout attacks within one year of the subject's uric acid-lowering treatment.

[0024] Optionally, the protein biomarker includes at least one of PPA5, SAA1, SAA2, SAA4, PEDF, C1R, PON3, B2MG, and CAB45.

[0025] Optionally, when the program is executed by the processor, it also generates a nomogram visualization interface, which displays the scores of each risk factor, the total score, and the corresponding predicted probability.

[0026] A seventh aspect of the present invention provides an apparatus comprising an input device and a computing device, the input device being used to input concentration data of the protein biomarker, and the computing device being used to calculate the system, comprising: Memory, used to store computer programs; A processor for executing the computer program to implement the above prediction method; Output device for displaying prediction results and / or nomograms.

[0027] Optionally, the output device is also used to output individualized treatment recommendations.

[0028] Compared with the prior art, the present invention has the following beneficial effects: This invention verifies that PPA5, SAA1, SAA2, SAA4, PEDF, C1R, PON3, B2MG, and CAB45 proteins can all serve as protein biomarkers for predicting frequent gout attacks during uric acid-lowering treatment, and their combined predictive AUC area can reach 0.730.

[0029] This invention provides a complete technology chain from protein biomarker discovery, predictive model construction, reagent kit preparation to computer implementation, which facilitates industrialization and clinical application. Attached Figure Description

[0030] The accompanying drawings, which are included to provide a further understanding of the invention and form part of this invention, illustrate exemplary embodiments of the invention and are used to explain the invention, but do not constitute an undue limitation of the invention. In the drawings: Figure 1 The patient screening flowchart, grouping, and data analysis process are illustrated in the embodiments of the present invention.

[0031] Figure 2This is a schematic diagram illustrating the functional analysis results of differentially expressed proteins between the frequent attack group and the control group in cohort 1 of this invention. Wherein, Figure 2 A in the diagram shows the principal component analysis results of differentially expressed proteins between the frequent attack group and the control group; Figure 2 In section B, a heatmap representation of the differentially expressed protein abundance profiles between the frequent seizure group and the control group is shown. Figure 2 The middle section (C) shows a volcano plot of differentially expressed proteins between the frequent seizure group and the control group. Upregulated proteins (red) are defined as fold changes > 1.5 and P < 0.05 (log2 fold change > 0.58, -log10 P value > 1.3); downregulated proteins (blue) are defined as fold changes < 0.67 and P < 0.05 (log2 fold change < -0.58, -log10 P value > 1.3); gray indicates no significant change. Figure 2 The top ten enriched entries are shown for biological processes (BP), cellular components (CC), molecular functions (MF), and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways associated with differentially expressed proteins in the frequent episode group. Figure 2 E represents the protein-protein interaction (PPI) network analysis of differentially expressed proteins between the frequent seizure group and the control group.

[0032] Figure 3 This is a schematic diagram illustrating the functional analysis results of differentially expressed proteins between the frequent seizure group and the infrequent seizure group in cohort 1 of this invention. Figure 3 In Figure A, principal component analysis of differentially expressed proteins between the frequent seizure group and the infrequent seizure group is presented. Figure 3 In the middle B section, a heatmap representation of the differentially expressed protein abundance profiles between the frequent seizure group and the infrequent seizure group is shown. Figure 3 The graph in C represents a volcano plot showing the differentially expressed proteins between the frequent and infrequent seizure groups. Upregulated proteins (red) are defined as fold changes > 1.5 and P < 0.05 (log2 fold change > 0.58, -log10 P value > 1.3); downregulated proteins (blue) are defined as fold changes < 0.67 and P < 0.05 (log2 fold change < -0.58, -log10 P value > 1.3); gray indicates no significant change. Figure 3 The top ten enriched entries are shown for biological processes (BP), cellular components (CC), molecular functions (MF), and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways associated with differentially expressed proteins in the frequent episode group. Figure 3 E represents the protein-protein interaction (PPI) network analysis results of differentially expressed proteins between the frequent seizure group and the infrequent seizure group.

[0033] Figure 4This is a schematic diagram illustrating the differentially expressed proteins and functional changes in the frequent seizure group, infrequent seizure group, and control group in this embodiment of the invention. Figure 4 In the diagram, A represents the Venn diagram of differentially expressed proteins in the three groups. Figure 4 In Figure B, the abundance spectrum heatmap of differentially expressed proteins in the three groups is shown. Figure 4 The top ten enriched entries are shown in the middle C section, representing biological processes (BP), cellular components (CC), molecular functions (MF), and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways associated with differentially expressed proteins in the frequent episode group.

[0034] Figure 5 This is the result obtained through protein biomarker screening using a machine learning algorithm in this embodiment of the invention. Figure 5 Figure A shows a box plot of the expression levels of six upregulated proteins in the frequent seizure group. The horizontal line represents the median; the box boundary represents the interquartile range (IQR); and the whisker lines represent the range. p<0.05, p<0.01, p<0.001; Figure 5 The box plots in Figure B represent the expression levels of three downregulated proteins in the frequent seizure group. The horizontal line represents the median; the box boundaries represent the interquartile range (IQR); and the whisker lines represent the range. p < 0.05, p < 0.01, p < 0.001; Figure 5 In the figure, C represents the importance ranking of nine key candidate protein biomarkers based on the random forest algorithm. The importance score is calculated based on the average descent accuracy, and the higher the score, the greater the contribution to the classification model. Figure 5 D represents the feature importance ranking of 9 differentially expressed proteins based on support vector machine-recursive feature elimination. The importance score is obtained by using the absolute weight coefficients calculated by combining linear support vector machine with recursive feature elimination. The higher the score, the greater the contribution to the classification model.

[0035] Figure 6 This is a Spearman correlation analysis of nine differentially expressed proteins in this embodiment of the invention. The color gradient represents the correlation coefficient. *p<0.05, p<0.01, *p<0.001.

[0036] Figure 7 These are the receiver operating characteristic curves (ROCs) of nine differentially expressed proteins in embodiments of the present invention. Detailed Implementation

[0037] The present invention is described in detail below with reference to the embodiments, but the present invention is not limited to these embodiments. Unless otherwise specified, the raw materials and catalysts in the embodiments of the present invention are all purchased through commercial channels.

[0038] Infrequent attacks were defined as <2 gout attacks in the past year (self-reported), and frequent attacks were defined as ≥2 gout attacks in the past year. Example 1: Research Cohort and Sample Collection Cohort 1 recruited 56 subjects consecutively at the gout clinic of the Affiliated Hospital of Qingdao University in January 2022; Cohort 2 recruited 181 subjects at the same clinic between April and May 2023; and Cohort 3 recruited 490 subjects between September and December 2024.

[0039] All participants were male and met the 2015 ACR / EULAR gout classification criteria. 85.0% of enrolled patients were first-time users of urate-lowering drugs (ULTs), and 15.0% had intermittently taken urate-lowering medications prior to enrollment. All participants were not taking urate-lowering medications at enrollment and had started ULT treatment. After enrollment, all participants underwent follow-up examinations at baseline, 3 months, 6 months, and 12 months (±1 month margin of error). Demographic data, including age, disease duration, BMI, present medical history, past medical history, and presence of subcutaneous tophi, were collected during follow-up. The frequency and duration of gout attacks were retrospectively recorded using follow-up questionnaires. Serum samples were collected via anticoagulant-free intravenous catheters, coagulated at room temperature for 30 minutes, and centrifuged at 3500g for 15 minutes to detect blood biochemical parameters (SUA, etc.). The estimated glomerular filtration rate (eGFR) was calculated using the CKD-EPI equation. Exclusion criteria included: (1) inability to complete medical history collection; (2) poor compliance or inability to complete 12-month follow-up; (3) coexisting joint diseases; and (4) experiencing an acute gout attack within 1 month of enrollment. The primary outcome measure of the study was frequent gout attacks (≥2 times) and infrequent gout attacks (<2 times) during the 12-month follow-up period.

[0040] All participants started uric acid-lowering medications according to the recommendations of the Chinese Rheumatology Society. Febuxostat, allopurinol, or benzbromarone were used as the primary treatment strategy, initially at low doses followed by dose titration. Colchicine (0.5 mg / day) was administered during the first 3–6 months of ULT to prevent gout attacks. To control gout attacks, colchicine, nonsteroidal anti-inflammatory drugs (NSAIDs), or glucocorticoids were administered as early as possible.

[0041] Research process as follows Figure 1 As shown. This study complies with the requirements of the Declaration of Helsinki and was approved by the Ethics Committee of the Affiliated Hospital of Qingdao University (QYFY WZLL 30917). All study participants voluntarily participated and signed informed consent forms.

[0042] In Cohort 1, protein biomarkers were screened using mass spectrometry (MS)-based data-independent acquisition (DIA) technology. In Cohort 2, candidate proteins were validated using mass spectrometry-based parallel reaction monitoring (PRM) technology. Samples from Cohort 3 were used for validation.

[0043] Results: Cohort 1 of this study included 54 participants, including 44 patients with gout and 10 healthy controls. Detailed baseline clinical information is shown in Table 1. Overall, the mean age of the healthy controls was 53.00 ± 5.08 years, while the mean age of all gout patients was 47.98 ± 15.85 years. The mean duration of gout was 8.50 (range 6.00-15.00) years, and 24 patients (54.55%) had subcutaneous tophi. The mean serum uric acid level was 501.41 ± 124.14 umol / L. There were no differences in age or body mass index between the two groups.

[0044] Table 1. Baseline Clinical Data of the Cohort

[0045] Note: Normally distributed data are expressed as mean ± standard deviation (SD), non-normally distributed data are expressed as median (interquartile range, IQR), and categorical variables are expressed as number of cases (percentage) [n (%)].

[0046] During the 12-month follow-up period, 37 participants (84.09%) received febuxostat to lower uric acid, with a median dose of 40.00 (20.00-40.00) mg / day; 7 participants (15.91%) received benzbromarone to lower uric acid, with a median dose of 25.00 (25.00-37.50) mg / day. During the ULT period, the mean serum uric acid decreased from 501.41 ± 124.14 μmol / L at baseline to 379.50 ± 114.36 μmol / L at 12 months of follow-up (Table 2). At the end of the 12-month follow-up, 21 patients (47.73%) experienced infrequent flare-ups, and 23 patients (52.27%) experienced frequent flare-ups.

[0047] Table 2. Clinical data of gout patients in cohort 1 during follow-up.

[0048] Note: Normally distributed data are expressed as mean ± standard deviation (SD), non-normally distributed data are expressed as median (interquartile range, IQR), and categorical variables are expressed as number of cases (percentage) [n (%)].

[0049] Example 2: Data-Independent Acquisition (DIA) Proteomics A suitable amount of sample was taken, and protein concentration was determined after complete lysis with protein lysis buffer. The sample was enzymatically digested, centrifuged, dried, and desalted, then dissolved in chromatographic mobile phase A (0.1% FA) and separated using an EASY-nLC 1200 liquid chromatography system (Thermo Scientific, USA). Mobile phase B was a solution containing 0.1% formic acid and 80% acetonitrile, eluted in a gradient over 60 minutes. Mass spectrometry analysis was performed on a Thermo Scientific Q Exactive HF with the following parameters: DIA mode, primary scan resolution of 120,000 m / s, scan range of 350–1150 m / s, maximum injection time of 50 ms; secondary scan resolution of 30,000 m / s, 30 scan windows, collision energy of 33%. The uniprot human database was used for database searching. Missing values ​​were filled with 50% of the minimum value; proteins with more than 50% missing data were excluded. Differential protein screening criteria: fold change ≥ 1.5 and p-value < 0.05.

[0050] Principal component analysis showed that the frequent seizure group and the infrequent seizure group, as well as the frequent seizure group and the healthy control group, exhibited a clear separation trend in the principal component space. Figure 2 China A, Figure 3 (A). Compared with the healthy control group, the frequent seizure group had 652 differentially expressed proteins. The heatmap and volcano plot of the differentially expressed proteins are shown in Figure 1. Figure 2 Proteins B and C are mainly involved in single / multicellular biological processes, stress responses, and responses to external stimuli. They are enriched in pathways such as complement and coagulation cascades, and focal adhesion. P <0.05, Figure 2 (D). Compared with the infrequent seizure group, the frequent seizure group had 76 differentially expressed proteins (Table 3). The heatmap and volcano plot of the differentially expressed proteins are shown in Figure 3. Figure 3 Proteins B and C are mainly involved in biological processes such as responses to external stimuli, single biological processes, and stress responses. They are enriched in pathways such as complement and coagulation cascades, fructose and mannose metabolism. P <0.05, Figure 3 (D). Protein-protein interaction analysis results showed that these differentially expressed proteins formed a tight interaction network, suggesting that these proteins may have synergistic effects in biological function. Figure 2 E, Figure 3 (E).

[0051] Table 3. Differentially expressed proteins between the infrequent seizure group and the frequent seizure group.

[0052] In comparisons among the frequent seizure group, the infrequent seizure group, and the healthy control group, a total of 45 differentially expressed proteins were identified. P <0.05, Figure 4 Of the 45 differentially expressed proteins, 36 (including LDHA, SAA1, SAA2, PYGL, TKFC, SAA4, etc.) were significantly upregulated in the frequent attack group (fold change ≥ 1.5), while 9 (including DBN1, PDLI, MTURN, SNX6, BZW2, RPSA, etc.) showed a downregulation trend (fold change ≤ 0.67) (see Table 4 for specific values). Unsupervised cluster analysis of these 45 differentially expressed proteins showed obvious clustering characteristics between the frequent attack group, the infrequent attack group, and the healthy control group: in the frequent attack group, acute-phase reactive proteins, such as serum amyloid A family members (SAA1, SAA2, SAA4) and complement system components (C1R, C1S), were significantly upregulated. Figure 4 (B) To further explore the potential molecular functions, localizations, and affected biological processes and pathways of these differentially expressed proteins, GO functional enrichment analysis and KEGG pathway analysis were performed on all differentially expressed proteins. The results showed that these differentially expressed proteins were mainly enriched in biological processes such as responses to external stimuli, stress responses, and defense responses, and involved in pathways such as fructose and mannose metabolism, metabolic pathways, complement and coagulation cascades. Figure 4 Based on the above findings, these 45 differentially expressed proteins were selected for further analysis and PRM targeting validation was conducted in a second independent prospective study cohort (Cohort 2).

[0053] Table 4. Differentially expressed proteins in the frequent seizure group, infrequent seizure group, and healthy control group.

[0054] Example 3 Target validation of candidate protein biomarkers In the second independent prospective study cohort (cohort 2), a total of 176 participants ultimately completed follow-up and were included in the study. Detailed baseline clinical information is shown in Table 5. Overall, the mean age of all gout patients was 44.95 ± 13.26 years, the mean duration of gout was 5.50 (2.00–10.00) years, 46 patients (26.14%) had subcutaneous tophi, and the mean serum uric acid level was 521.96 ± 111.84 umol / L.

[0055] Table 5. Baseline clinical data of prospective validation cohort 2

[0056] Note: Normally distributed data are expressed as mean ± standard deviation (SD), non-normally distributed data are expressed as median (interquartile range, IQR), and categorical variables are expressed as number of cases (percentage) [n (%)].

[0057] During the follow-up period, 159 participants (90.34%) received febuxostat to lower uric acid, with a median dose of 30.00 (20.00-40.00) mg / day; 17 participants (9.66%) received benzbromarone to lower uric acid, with a median dose of 25.00 (25.00-37.50) mg / day. During the ULT period, the mean serum uric acid decreased from 521.96 ± 111.84 μmol / L at baseline to 385.63 ± 103.80 μmol / L at 12 months of follow-up (Table 6). At the end of the 12-month follow-up, 94 participants (53.41%) experienced infrequent flare-ups, and 82 participants (46.59%) experienced frequent flare-ups (Table 6).

[0058] Table 6 Clinical data during the follow-up period of cohort 2

[0059] Note: Normally distributed data are expressed as mean ± standard deviation (SD), non-normally distributed data are expressed as median (interquartile range, IQR), and categorical variables are expressed as number of cases (percentage) [n (%)].

[0060] Eighteen proteins (including S100A7, GSR, CRHBP, MAN2A2, SORD, HYAL1, TKFC, RMDN3, FYCO1, ECHDC1, RPSA, DBN1, MTURN, CHMP5, SNX6, TSC22D4, BZW2, and MAN2A1) were excluded due to insufficient signal intensity. Cohort 2 successfully quantified 27 proteins and screened out 9 differentially expressed proteins (P<0.05, Table 7). Among them, 6 proteins (PPA5, SAA1, SAA2, SAA4, PEDF, and C1R) were significantly upregulated in the frequent seizure group (fold change ≥ 1.2), while 3 proteins (PON3, CAB45, and B2MG) showed a downregulated trend (fold change ≤ 0.83), and this result was consistent with the trend in DIA-MS data. Figure 5 (A and B in the middle).

[0061] Table 7. Nine differentially expressed proteins between the frequent seizure group and the infrequent seizure group in Cohort 2.

[0062] Note: ↑ indicates proteins with elevated expression levels in the frequent seizure group; ↓ indicates proteins with decreased expression levels in the frequent seizure group.

[0063] To investigate which differentially expressed proteins might be associated, we performed a correlation analysis. The results showed that SAA1 / SAA2 was positively correlated with C1R and PEDF. Figure 6 To further screen robust biomarkers, this study employed a multi-algorithm ensemble strategy. Based on the nine candidate protein biomarkers obtained from previous screening, two machine learning algorithms, Random Forest and Support Vector Machine-Recursive Feature Elimination (SVM-RFE), were used to evaluate protein importance. Random Forest (ranking features based on decreasing average accuracy) showed that SAA1 and SAA2 contributed the most to the model's classification (Table 8). Figure 5 (C), the SVM-RFE algorithm results also show that SAA1 and SAA2 rank highly in importance (Table 9, Figure 5 (D).

[0064] Table 8. Protein importance ranking based on random forest algorithm

[0065] Table 9. Protein importance ranking based on support vector machine-recursive feature elimination.

[0066] To further investigate the predictive role of nine differentially expressed proteins in the frequent recurrence of gout during uric acid-lowering therapy, receiver operating characteristic (ROC) curve analysis was performed on these proteins. The combined area under the curve for the nine proteins was found to be 0.730 (…). Figure 7 The prediction results were good.

[0067] Example 4: Statistical analysis of protein biomarkers This study used SPSS 26.0 (IBM, Armonk, NY, USA), Graphpad Pism 10.1.2, R4.3.2R (R Foundation for Statistical Computing, Vienna, Austria), and Cytoscape (v3.9.1) software for statistical analysis and visualization of data. Descriptive statistical methods were used to summarize the demographic and baseline characteristics of the participants. Continuous variables were expressed as mean ± standard deviation (SD) or median (interquartile range [IQR]), and comparisons between groups were performed using Student's t-test or Wilcoxon signed-rank test, respectively. Categorical variables were expressed as frequency and percentage, and comparisons between groups were performed using Pearson's chi-square test or Fisher's exact test.

[0068] Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were used to functionally annotate differentially expressed proteins. Protein-protein interaction (PPI) network analysis was performed using the online analysis tool String (https: / / cn.string-db.org / ).

[0069] Based on differentially expressed proteins, key proteins were screened using random forest and support vector machine recursive feature elimination (SVM-RFE) algorithms. The area under the receiver operating characteristic (ROC) curve was calculated to evaluate the model's discriminative ability.

[0070] Example 5: Practical Application of Biomarkers and Models Baseline clinical data for cohort 3 are shown in Table 10, and clinical data during the follow-up period are shown in Table 11.

[0071] Table 10 Baseline clinical data of prospective validation cohort 3

[0072] Note: Normally distributed data are expressed as mean ± standard deviation (SD), non-normally distributed data are expressed as median (interquartile range, IQR), and categorical variables are expressed as number of cases (percentage) [n (%)].

[0073] During the follow-up period, 393 participants (81.88%) received febuxostat to lower uric acid, with a median dose of 30.00 (20.00-40.00) mg / day; 87 participants (18.12%) received benzbromarone to lower uric acid, with a median dose of 25.00 (25.00-50.00) mg / day (Table 11). At the end of the 12-month follow-up, 308 participants (64.17%) experienced infrequent seizures, and 172 participants (35.83%) experienced frequent seizures.

[0074] Table 11 Clinical data during the follow-up period of prospective validation cohort 3

[0075] Note: Normally distributed data are expressed as mean ± standard deviation (SD), non-normally distributed data are expressed as median (interquartile range, IQR), and categorical variables are expressed as number of cases (percentage) [n (%)].

[0076] The system was used to test the subjects in cohort 3, and the results are shown in Table 12.

[0077] Sensitivity = (Number of people detected as having frequent seizures in the frequent seizure group / Total number of people in the frequent seizure group) * 100%; Specificity = (Number of people in the infrequent seizure group who tested positive for infrequent seizures / Total number of people in the infrequent seizure group) * 100%; Accuracy = [(Number of people detected as having frequent seizures in the frequent seizure group + Number of people detected as having having infrequent seizures in the infrequent seizure group) / Total number of subjects] * 100%.

[0078] Table 12 Detection results of the system of the present invention

[0079] The above description is merely an embodiment of the present invention, and the scope of protection of the present invention is not limited to these specific embodiments, but is determined by the claims of the present invention. For those skilled in the art, the present invention can have various modifications and variations. Any modifications, equivalent substitutions, improvements, etc., made within the technical concept and principle of the present invention should be included within the scope of protection of the present invention.

Claims

1. A protein biomarker for predicting frequent gout attacks, characterized in that, The protein biomarker is selected from at least one of PPA5, SAA1, SAA2, SAA4, PEDF, C1R, PON3, B2MG, and CAB45.

2. The protein biomarker according to claim 1, characterized in that, The risk of frequent gout attacks is higher when the expression of PPA5, SAA1, SAA2, SAA4, PEDF, and C1R genes is upregulated and / or the expression of PON3, CAB45, and B2MG genes is downregulated.

3. The use of the protein biomarker as described in any one of claims 1-2 in the preparation of a product for predicting frequent gout attacks.

4. The application according to claim 3, characterized in that, The product is selected from any of the following: reagent kit, chip, antibody, test strip, biosensor or combination thereof.

5. A kit for predicting frequent gout attacks, characterized in that, The kit includes reagents for detecting the concentration and / or abundance of the protein biomarkers according to any one of claims 1-2.

6. The reagent kit according to claim 5, characterized in that, The kit also includes reagents for extracting the protein markers of any one of claims 1-2 from serum.

7. A system for predicting frequent gout attacks, characterized in that, The system includes: (a) A data acquisition module, used to acquire concentration and / or abundance data of protein markers in the serum of gout patients, wherein the protein markers are the protein markers described in any one of claims 1 or 2; (b) A data processing module that compares the data obtained by the data acquisition module with a predetermined threshold in order to determine the risk of frequent gout attacks in the gout patient; (c) Output module, used to output the risk assessment results.

8. An information data processing terminal, the information data processing terminal comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, the processor executes the computer program to calculate the system according to claim 7.

9. A computer-readable storage medium storing a computer program, characterized in that, The program is executed by the processor to compute the system of claim 7.

10. An apparatus comprising an input device and a computing device, characterized in that, The input device is used to input the concentration data of the protein biomarker as described in claim 1, and the computing device is used to calculate the system as described in claim 7.

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