Application and kit of differential tissue proteins and / or metabolites in pigmented villonodular synovitis

Abstract

The present invention discloses an application and a kit for differential histoproteins and / or metabolites of pigmented villonodular synovitis; the differential histoproteins include at least one of the following proteins: TNFSF11, cathepsin K, and adhesion G protein-coupled receptor E5; the differential metabolites include at least one of the following products: 13-hydroxyperoxylinoleic acid and octadecadienoic acid; the present invention provides a new screening marker for pigmented villonodular synovitis (PVNS) through proteomics and metabolomics, which can achieve effective screening for pigmented villonodular synovitis; the obtained screening marker is used in a screening kit, which only needs synovial fluid as a test sample, can quickly perform screening and detection, and has the advantages of high efficiency and simple detection.

Description

Technical Field

[0001] The present invention relates to the field of biomedical technology, and in particular to an application of differential tissue proteins and / or metabolites of pigmented villonodular synovitis and a kit. Background Art

[0002] PVNS is an aggressive soft tissue tumor that is considered benign in its cellular proliferation. Despite clinical and radiographic differences, the two forms of PVNS share similar histologic features, including histiocytic proliferation and hemosiderin deposition within the synovium. The localized synovial form typically presents as a palpable intrasynovial mass that may be locally invasive and prone to local recurrence. In contrast, the diffuse form of PVNS involves the entire synovium, both intra- and extra-articular, and can gradually progress from focal to diffuse disease, affecting bones, muscles, and tendons, leading to arthritis. The more diffuse form is more aggressive and recurrent, often resulting in severe, extensive joint disease and deformity despite treatment attempts. Recurrence may manifest as recurrent effusions, hemarthrosis, arthrosis, the appearance of a new palpable mass, or worsening pain with limited range of motion. Previous studies have focused on the ongoing proliferation of the synovium, with less emphasis on synovial fluid analysis.

[0003] Currently, biopsy remains the gold standard for diagnosis as it demonstrates synovial tissue proliferation and the formation of brown, villous, nodular lesions. Although surgical resection is the primary treatment for almost all localized variants and most diffuse variants, local recurrence is common after simple synovectomy, and the efficacy and potential side effects of external-beam radiotherapy as an adjuvant treatment are uncertain. Due to the atypical clinical symptoms of PVNS, diagnosis and treatment are often delayed, leading to functional impairment. Current diagnostic methods for PVNS include synovial fluid (SF) analysis, radiology, arthroscopy, and pathology. The lack of in-depth biological information on the pathogenic mechanisms of PVNS poses a challenge to the development of treatments.

[0004] The diagnosis is based on radiological diagnosis and clinical manifestations, and the most critical is intraoperative pathological biopsy, but it has not been widely carried out due to its invasiveness and professionalism. The popularization of these technologies has not only improved the sensitivity and specificity of diagnosis, but also can effectively assess the severity of the disease. For many years, in addition to clinical manifestations, joint MRI examination has been the main auxiliary means of diagnosing PVNS, but due to its poor sensitivity, it is not conducive to early diagnosis. Whether oxidized lipid substances can be used as biomarkers for pigmented villonodular synovitis, what kind of oxidized lipid substances and proteins can be used as biomarkers for pigmented villonodular synovitis to achieve the diagnosis of pigmented villonodular synovitis, there is currently a lack of relevant reports. Summary of the Invention

[0005] Aiming at the problems existing in the prior art, the present invention provides an application of differential tissue proteins and / or metabolites of pigmented villonodular synovitis and a kit.

[0006] The technical solution adopted by the present invention is: an application of differential tissue proteins and / or metabolites of pigmented villonodular synovitis, wherein the differential tissue proteins include at least one of the following proteins: TNFSF11, cathepsin K, adhesion G protein-coupled receptor E5;

[0007] The differential metabolites include at least one of the following products: 13-hydroxyperoxylinoleic acid and octadecadienoic acid.

[0008] Furthermore, the differential tissue proteins and / or metabolites are used as screening markers for pigmented villonodular synovitis and / or are used to prepare a screening kit for pigmented villonodular synovitis.

[0009] Furthermore, the pigmented villonodular synovitis screening kit contains reagents for detecting tissue proteins and / or metabolites in joint fluid.

[0010] Furthermore, the database number of the tissue protein TNFSF11 is P014788; the database number of the cathepsin K is P43235; and the database number of the adhesion G protein-coupled receptor is P48960.

[0011] A pigmented villonodular synovitis screening kit comprising reagents for detecting tissue proteins and / or metabolites;

[0012] The tissue proteins include at least one of the following proteins: TNFSF11, cathepsin K, adhesion G protein-coupled receptor E5;

[0013] The metabolites include at least one of the following products: 13-hydroxyperoxylinoleic acid, octadecadienoic acid;

[0014] The reagent for detecting tissue protein and / or metabolites is used as a reagent for detecting tissue protein and / or metabolites in joint fluid.

[0015] The beneficial effects of the present invention are:

[0016] (1) The present invention provides a new screening marker for pigmented villonodular synovitis (PVNS) through proteomics and metabolomics, which can achieve effective screening for pigmented villonodular synovitis.

[0017] (2) The screening markers obtained in the present invention are used in a screening kit, which only requires synovial fluid as a test sample, and can be quickly screened and tested, with the advantages of high efficiency and simple detection. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 Schematic diagram of the marker screening process of the present invention.

[0019] Figure 2 Schematic diagram of the analysis results of differential proteins in the embodiment of the present invention, A is a schematic diagram of the principal component analysis results, B is a heat map of cluster analysis of differential protein expression levels, and C is the statistical results of differential proteins.

[0020] Figure 3 Schematic diagram of the analysis results of differential proteins in the examples of the present invention, A is the KEGG pathway enrichment analysis result, and B is the PPI analysis result of up-regulated DEPs.

[0021] Figure 4 Schematic diagram of the relationship between gene modules and disease phenotypes in an embodiment of the present invention, A is a characteristic module association heat map, and B is the KEGG enrichment analysis of genes in the up-regulated difference bubble map.

[0022] Figure 5 The metabolite analysis results obtained by principal component analysis of QC samples in the embodiment of the present invention are shown in Figure 1. A is the PCA model diagram, B is the PLS-DA model diagram, and C is the visualization result of the differential metabolite expression.

[0023] Figure 6 This is the KEGG enrichment analysis result of the isometabolites in the examples of the present invention.

[0024] Figure 7 This is the correlation loading diagram between proteomics and metabolomics of the present invention.

[0025] Figure 8 Schematic diagram of the verification results of differential proteins and differential metabolites in the examples of the present invention. DETAILED DESCRIPTION

[0026] The present invention will be further described below with reference to the accompanying drawings and specific embodiments.

[0027] An application of differential tissue proteins and / or metabolites of pigmented villonodular synovitis, wherein the differential tissue proteins include at least one of the following proteins: TNFSF11, cathepsin K, adhesion G protein-coupled receptor E5;

[0028] The differential metabolites include at least one of the following products: 13-hydroxyperoxylinoleic acid and octadecadienoic acid.

[0029] The differentially expressed tissue proteins and / or metabolites are used as screening markers for pigmented villonodular synovitis and / or for preparing a screening kit for pigmented villonodular synovitis. The screening kit for pigmented villonodular synovitis contains reagents for detecting tissue proteins and / or metabolites in joint fluid.

[0030] The database number of the tissue protein TNFSF11 is P014788; the database number of the cathepsin K is P43235; and the database number of the adhesion G protein-coupled receptor is P48960.

[0031] A pigmented villonodular synovitis screening kit comprising reagents for detecting tissue proteins and / or metabolites;

[0032] The tissue proteins include at least one of the following proteins: TNFSF11, cathepsin K, adhesion G protein-coupled receptor E5;

[0033] The metabolites include at least one of the following products: 13-hydroxyperoxylinoleic acid, octadecadienoic acid;

[0034] The reagent for detecting tissue protein and / or metabolites is used as a reagent for detecting tissue protein and / or metabolites in joint fluid.

[0035] Screening process of tissue proteins and metabolites Figure 1 As shown in the figure, proteomic analysis of synovial fluid from patients with pigmented villonodular synovitis (P) and normal controls (C) was performed to screen for differentially expressed proteins. Multi-omics combined analysis of expressed proteins and metabolites in synovial fluid from pigmented villonodular synovitis (PVNS) refines screening criteria, reduces false positives and negatives, and provides more reliable results.

[0036] The specific proteomic analysis steps are as follows:

[0037] Joint fluid was extracted, and 40 μL of joint fluid was taken from each sample and diluted with 400 μL of binding buffer (1.25 mL of 5 mM Tris-HCl (pH = 6.8), 2 mL of glycerol, 4 mL of 10% SDS, 1 g of DTT, 500 μL of 0.1% bromophenol blue, and diluted to 20 mL with deionized water). The two sets of samples were removed from a -80°C freezer and reconstituted at 4°C. The samples were then vortexed for 30 seconds. A 50 μL sample was added to 250 μL of spiked methanol, vortexed, and placed at -20°C for 20 minutes. After ice-bath sonication for 15 minutes, the samples were centrifuged at 13,300 rpm in a 4°C centrifuge for 15 minutes. 200 μL of the supernatant was collected and dried in a vacuum concentrator at 30°C for 2 hours. After evaporation of all solvents, the samples were reconstituted in 0.1 mL of Hilic reconstitution solution. After ice-bath sonication, vortexing, and centrifugation, 80 μL of the supernatant was transferred to a sample vial and subsequently analyzed by the analyzer. For quality control, 20 μL of the supernatant from each sample was extracted and mixed into a quality control sample, which was then aliquoted according to sample size. The peptides after enzymatic hydrolysis of each sample were collected separately on the machine, and the LC-MS analyzed all peptides after enzymatic hydrolysis of synovial fluid and synovium in data independent acquisition (DIA) mode. The detection time for sample separation was 78 minutes, the m / z range was 350-1550, each cycle contained 40 timetables, the separation degree of MS2 was 30,000, and secondary fragment high energy collision induced dissociation (HCD) was selected. The IRT kit (biognosys) was used to calibrate the retention time. After searching the database, the qualitative and quantitative information of the protein was obtained, and then the KNN algorithm was used to estimate the missing data. If the protein was missing in more than half of the samples, the protein would be eliminated, and proteins with a coefficient of variation (CV) greater than 30% would also be deleted. ANOVA was performed to screen differential proteins with p < 0.05. To find out the functions of the differentially expressed proteins involved. KEGG pathway enrichment analysis and network protein interaction analysis were performed to determine the specific pathways and functions involved in the differentially expressed proteins. The data and results are shown in Tables 1 and 2. Figure 2 and Figure 3 shown.

[0038] Table 1. Proteomic analysis results

[0039]

[0040] *P values indicate the differences between groups compared by Student's t test.

[0041] **p-values indicate differences between groups compared by Wilcoxon signed-rank test.

[0042] ***p values indicate differences between groups, and comparisons were made using the chi-square test.

[0043] Table 2. Changes in synovial fluid protein abundance in each group

[0044]

[0045] FC, short for fold change, refers to the ratio of the concentration of a specific metabolite in a patient's serum to that in normal human serum. P value is a statistical measure used to determine whether a hypothesis is valid.

[0046] Proteomic analysis such as Figure 2 As shown, Figure 2 In Figure A, we can see that in PVNS patients, DEP is mainly related to the differential expression of proteins related to the damage caused by the disease: Principal component analysis (PCA) was performed to illustrate the relationship between PVNS and control samples in different dimensions. Each point represents a repetition in the group experiment. Different colors distinguish the groups ( Figure 2 A). PCA highlights the spatial distribution of samples within the same group. The following criteria were used for differential screening: P-value < 0.05, FC ≥ 1.5, or FC ≤ 1 / 1.5.

[0047] Figure 2 Figure B shows the cluster analysis results. Cluster analysis of differential protein expression levels. Cluster heatmaps can be used to control the quality of standardized experimental data and customize data presentation after differential data enrichment. Data and samples can be clustered to assess sample quality. Hierarchical cluster heatmaps demonstrate the consistency of DEP changes between groups.

[0048] Figure 2 Middle C shows the results of 156 DEPs, among which 70 proteins were upregulated and 86 were downregulated in the PVNS group compared with the control group.

[0049] Differential protein analysis revealed significant changes in the expression and abundance of TNFSF11, CTSK, and ADGRE5 in synovial fluid, as shown in Table 2. These proteins may be involved in multiple immune responses and metabolites in the joint cavity and may serve as biomarkers for the differential diagnosis of PVNS, as well as providing new targets for subsequent disease treatment.

[0050] KEGG pathway analysis results Figure 3 As shown, from Figure 3 As can be seen in Figure A, "Hsa04380: osteoclast differentiation" was significantly enriched. The key proteins involved were TNFSF11 and CTSK, which were significantly upregulated. PPI analysis of upregulated DEPs showed a significant correlation between TNFSF11, CTSK, and ADGRE5, as shown in Figure 5. Figure 3 As shown in B.

[0051] The analysis was performed using a weighted gene co-expression network. The analysis process was as follows:

[0052] A total of 376 genes and 20 samples were analyzed. Genes with low expression variability (standard deviation ≤ 0.5) were filtered out, leaving 208 genes and 20 samples for further analysis. A weighted co-expression network model was constructed using a power value of 22, and the remaining 208 genes were divided into 3 modules. Data analysis and visualization were performed using the WGCNA package in R, and data visualization was performed using R and Python. The Pearson correlation algorithm was used to calculate the correlation coefficient and p-value between the module characteristic genes and traits. Modules with an absolute correlation coefficient ≥ 0.3 and a p-value < 0.05 were considered significant. For each significant module, the correlation between module gene expression and trait gene significance (GS) was calculated, and the correlation between module gene expression and Eigengene was analyzed to construct a module-trait correlation analysis. The results are shown in Figure 2. Figure 4 shown.

[0053] Figure 4 A in the figure is the feature module association heat map, where the absolute value of the correlation coefficient greater than or equal to 0.3 and pValue less than 0.05 is the threshold for screening modules associated with each feature.

[0054] The metabolomics analysis process is as follows:

[0055] Joint fluid was drawn, and 50 μL of each sample was mixed with 250 μL of labeled methanol, vortexed, and stored at -20°C for 20 minutes. After ultrasonic extraction on ice for 15 minutes, the samples were centrifuged at 13,300 rpm for 15 minutes at 4°C. 200 μL of supernatant was collected, dried under vacuum at 30°C for 2 hours, and reconstituted in 0.1 mL of Hilic reconstitution solution. After ultrasonication, vortexing, and centrifugation on ice, 80 μL of supernatant was transferred to a vial for component analysis using a liquid chromatography-mass spectrometry (LC-MS) system consisting of an ACQUITY UPLC coupled to an AB Triple TOF 5600 high-resolution mass spectrometer. For quality control, 20 μL of supernatant was extracted from each sample, pooled to create a quality control sample, and aliquoted to the same volume as the test sample. Metabolomics analysis was performed using Progenesis QI (Waters) for peak selection, alignment, and annotation. When the total metabolomics score was ≥35 and the intensity was >1000, the data were imported into R software (version 3.5.1) for subsequent statistical analysis. After data cleaning and missing value interpolation (KNN algorithm), median normalization was applied, and PCA was used to evaluate the experimental quality. Compounds with p < 0.05 and VIP value ≥ 1 in the t-test were selected as differential markers. Differential metabolites were screened and functional analysis was performed on the differential metabolites. The results are shown in Figure 2. Figure 5 and Figure 6 The changes in metabolite abundance are shown in Table 3.

[0056] Figure 5 Figure A shows the PCA model for all samples obtained through 7-fold cross-validation. Figure B shows the partial least squares discriminant analysis (PLS-DA) method. Figure C shows the heat map of the top 50 differential metabolites. The figure shows the top 50 differential metabolite expression visualized using VIP (the horizontal axis represents the sample name, and the vertical axis represents the differential metabolite. The color ranges from green to red, indicating the expression abundance of the metabolite from low to high, i.e., the redder the color, the higher the expression abundance of the differential metabolite).

[0057] Table 3. Changes in the abundance of metabolites in the synovial fluid of each group

[0058]

[0059] FC, short for foot change, refers to the ratio of the concentration of a specific metabolite in a patient's serum to that in normal human serum. P value is used in statistics to determine whether a hypothesis is valid.

[0060] Figure 6 It is the KEGG TOP enrichment pathway of differential metabolites.

[0061] Figure 7 This is a loading plot of the top 30 proteome-metabolite associations. The horizontal axis represents the first dimension, and the vertical axis represents the second dimension. The dots in the figure represent metabolites, and the triangles represent proteins. The larger the absolute value in the coordinates, the greater the degree of association between the element and the other omics.

[0062] The Western blot analysis process is as follows:

[0063] Synovial fluid samples were lysed at 4°C and centrifuged at 12,000 g for 25 minutes, and the supernatant was collected as the sample protein solution. Protein concentration was measured using a BCA kit. Equal amounts of protein (40 μg) were separated on 8%-15% SDS-PAGE gels, transferred to polyvinylidene fluoride (PVDF) membranes, and incubated overnight with the appropriate primary antibodies. The primary antibodies used were TNFSF11 (1:1000, ABcloal, A2550); CTSK (1:500, ABcloal, A5871); ADGRE5 (1:2000, ABcloal, A22218); and β-tublin (1:1000, ABcloal, AC008). Immunoblots were visualized using a BeyoECL Plus (Beyotime, Beijing, China), and protein bands were photographed and stored using a Tanon 2500R gel imaging system (Tanon, Shanghai, China). The intensity of the bands was quantified using ImageJ 1.39V software (n=3). Figure 8 shown.

[0064] Figure 8 A, Western blotting analysis of TNFSF11, CTSK, and ADGRE5 expression. B, TNFSF11, CTSK, and ADGRE5 (all data are expressed as mean ± SD, analyzed by two-way ANOVA followed by Tukey's test). C, D, Quantification of relative expression of the metabolite 13-L-Hydroperoxylinoleic acid (13-OxoODE). * indicates P < 0.05, ** indicates P < 0.01, and *** means P < 0.001.

[0065] The statistical analysis method is as follows:

[0066] All data are expressed as mean ± SEM. Statistical significance between groups was analyzed using t-test or one-way ANOVA, followed by Tukey post hoc test to correct for multiple comparisons in GraphPad Prism 8. A p-value of <0.05 was considered statistically significant. O2PLS (Two-Way Orthogonal PLS) is a two-way orthogonal partial least squares method. The O2PLS method takes into account factors such as size, scale, distribution, and experimental error in data sets under different scenarios. The modeling process takes into account the three parts of joint, specific, and residual between different data sets. The results are shown in Figure 2. Figure 7 shown.

[0067] This study used high-throughput proteomics and metabolomics analysis of synovial fluid from patients with PVNS. A total of 156 DEPs and 62 differentially expressed metabolites were identified. Enrichment analysis revealed that osteoclast differentiation signaling plays a key role in PVNS progression, with significantly increased expression of TNFSF11, cathepsin K (CTSKCTSK), and ADGRE5 in lesional tissue from PVNS patients. Correlating these findings with the patients' clinical presentation, WGCNA analysis revealed a positive correlation between CRP, ESR, and WBC and a gene module characterized by the blue module. Further analysis of the blue module genes revealed a link to osteoclast differentiation signaling.

[0068] PPI analysis revealed a strong interaction between TNFSF11, cathepsin K:CTSK, and ADGRE5. ADGRE5 was found to be closely associated with TNFSF11 and CTSK. ADGRE5's role in cellular metabolism may complement its previously implicated role in cell migration. ADGRE5 may have similar functions in activated immune cells and cancer cells, both of which exhibit a tendency toward glycolytic metabolism and enhanced migration and proliferation.

[0069] Metabolomic analysis identified significant enhancements in the linoleic acid metabolism and unsaturated fatty acid biosynthesis pathways in PVNS samples, with differentially expressed metabolites such as 13-hydroxyperoxylinoleic acid (13-L-Hydroperoxylinoleic acid) and 13-OxoODE showing significant increases. Further analysis of proliferation and differentiation within the diseased sites using O2PLS (Two-way Orthogonal Partial Least Squares) analysis revealed a close relationship between ADGRE5, TNFSF11, CTSK, 13-L-hydroperoxylinoleic acid, and 13-OxoODE. Western blotting confirmed that ADGRE5, TNFSF11, and CTSK expression were significantly elevated in PVNS compared to controls. These findings suggest that enhanced energy metabolism leads to increased lipid oxidation, which in turn regulates the increased ADGRE5 expression and exacerbates osteoclast differentiation, as evidenced by the increased expression of TNFSF11 and CTSK. This ultimately leads to damage to the normal synovium and cartilage.

[0070] The present invention uses a method with little damage and the ability to quickly extract joint fluid as a detection means to diagnose pigmented villonodular synovitis. The detection method causes little harm to patients and has few interfering factors.

Claims

1. An application of differential tissue proteins and / or metabolites in pigmented villonodular synovitis, characterized in that: The differential tissue proteins include at least one of the following proteins: TNFSF11, cathepsin K, adhesion G protein-coupled receptor E5; The differential metabolites include at least one of the following products: 13-hydroxyperoxylinoleic acid and octadecadienoic acid; the differential tissue proteins and / or metabolites are used to prepare a screening kit for pigmented villonodular synovitis.

2. The use of the differential tissue proteins and / or metabolites of pigmented villonodular synovitis according to claim 1, characterized in that: The pigmented villonodular synovitis screening kit contains reagents for detecting differential tissue proteins and / or metabolites in joint fluid.

3. The use of the differential tissue proteins and / or metabolites of pigmented villonodular synovitis according to claim 1, characterized in that: The database number of the tissue protein TNFSF11 is P014788; the database number of the cathepsin K is P43235; and the database number of the adhesion G protein-coupled receptor is P48960.

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