Application of pigmented villus nodular synovitis differential tissue protein and / or metabolite and kit

Abstract

The invention discloses application of pigmented villus nodular synovitis differential tissue proteins and / or metabolites and a kit. The differential tissue protein at least comprises one of the following proteins: TNFSF11, cathepsin K and an adhesion G protein coupled receptor E5; the differential metabolites at least comprise one of the following products: 13-hydroxylinoleic acid peroxide and octadecadienoic acid; according to the invention, the novel PVNS screening marker for the pigmented villus nodular synovitis is provided through proteomics and metabonomics, so that the effective screening of the pigmented villus nodular synovitis can be realized; the obtained screening marker is used for a screening kit, screening and detection can be rapidly carried out only by taking joint synovial fluid as a detection sample, and the method has the advantages of high efficiency, simple detection and the like.

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 terms of cellular proliferation. Despite clinical and radiological differences, both 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 tends to recur locally. In contrast, diffuse 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 attempted treatment. Recurrences may present 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 continued proliferation of the synovium, with less emphasis on analysis of the synovial fluid.

[0003] Currently, biopsy remains the gold standard for diagnosis as it shows synovial tissue hyperplasia and the formation of brown, villous, nodular lesions. Although surgical resection is the main 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, resulting in 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 mechanism of PVNS poses a challenge to the development of treatment methods.

[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, and realize the diagnosis of pigmented villonodular synovitis, there is currently a lack of relevant reports. Summary of the invention

[0005] In view of 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 tissue proteinase 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 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 This is a 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 embodiments 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 a 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, 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 in conjunction with 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 differential 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 tissue protein TNFSF11 is P014788; the database number of the tissue proteinase 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 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 of patients with pigmented villonodular synovitis (P) and normal control group (C) was performed to screen differential proteins. Multi-omics combined analysis of expressed proteins and metabolites in synovial fluid of pigmented villonodular synovitis (PVNS) refined the screening conditions, reduced false positives or false negatives, and made the results more reliable.

[0036] The specific steps of proteomics analysis are as follows:

[0037] Joint fluid was extracted, and 40 μL of joint fluid was taken for each sample, and 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 dilute to 20 mL with deionized water) was added for dilution. The two groups of specimens were taken out of the -80℃ refrigerator and reconstituted at 4℃, then vortexed and mixed for 30s, 50μL of the sample was added with 250μL of spiked methanol, vortexed and mixed, placed at -20℃ for 20min, placed in a 4℃ centrifuge at 13300rpm for 15min after ice bath ultrasound for 15min, 200μL of supernatant was collected and placed in a vacuum concentrator at 30℃ for 2 hours, and after evaporating all the solvents, the sample was reconstituted in 0.1mL Hilic reconstitution solution, ice bath ultrasound, vortexing, centrifugation, and 80μL of supernatant was transferred to the injection bottle and then put on the machine for component analysis. For quality control, 20μL of supernatant was extracted from each sample and mixed into a quality control sample, which was then packaged according to the sample volume. The peptides after enzymatic hydrolysis of each sample were collected separately on the machine. 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 the 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 the 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, which were compared using the chi-square test.

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

[0044]

[0045] FC is the abbreviation of fold change, which refers to the concentration ratio of a specific metabolite in the patient's serum to that in the normal person's serum. P value is the basis for judging whether a hypothesis is established in statistics.

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

[0047] Figure 2 B in the middle is the cluster analysis result. Cluster analysis of differential protein expression levels. Cluster heat map can be used to control the quality of standardized experimental data and customized data display after differential data enrichment. Data and samples can be clustered to observe sample quality. Hierarchical cluster heat map shows 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] The differential protein analysis showed significant changes in the abundance of TNFSF11, CTSK, and ADGRE5 in the synovial fluid, as shown in Table 2. They may be involved in a variety of immune responses and metabolites in the joint cavity, and can be used as biomarkers for differential diagnosis of PVNS, as well as provide new targets for subsequent disease treatment.

[0050] KEGG pathway analysis results Figure 3 As shown, from Figure 3 As can be seen in 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, such as Figure 3 As shown in B.

[0051] The analysis was performed through 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 eigengene and the trait. Modules with an absolute correlation coefficient ≥ 0.3 and a p-value < 0.05 were considered significant. For each significant module, the correlation between the module gene expression and the trait gene significance (GS) was calculated, and the correlation between the module gene expression and the Eigengene was analyzed to construct a module-trait correlation analysis. The results are shown in 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 placed at -20°C for 20 min. After ultrasonic extraction on ice for 15 min, the samples were centrifuged at 13,300 rpm for 15 min at 4°C. 200 μL of supernatant was collected, dried under vacuum at 30°C for 2 h, and the samples were reconstituted in 0.1 mL of Hilic reconstitution solution. After ultrasonic treatment, vortexing, and centrifugation on ice, 80 μL of supernatant was transferred to injection vials for component analysis using a liquid chromatography-mass spectrometry (LC-MS) system consisting of an ACQUITY UPLC ultra-high performance liquid phase tandem AB Triple TOF 5600 high-resolution mass spectrometer. For quality control, 20 μL of supernatant was extracted from each sample, combined to make a quality control sample, and aliquoted into the same volume as the test sample. Metabolomics analysis was performed using Progenesis QI (Waters) for peak selection, alignment, and peak 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 A in the figure is the PCA model diagram of all samples obtained by 7-fold cross validation. B is the partial least squares discriminant analysis PLS-DA method diagram. C is the TOP-50 differential metabolite heat map. From the figure, it can be seen that the top 50 differential metabolite expressions are visualized based on VIP (the horizontal coordinate represents the sample name, and the vertical coordinate represents the differential metabolite. The color ranges from green to red, indicating that the expression abundance of the metabolite is from low to high, that is, 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 is the abbreviation of fo ld change, which refers to the concentration ratio of a specific metabolite in the patient's serum to that in the normal person's serum. P value is the basis for judging whether a hypothesis is established in statistics.

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

[0061] Figure 7 This is the loading diagram of the TOP30 associated with proteomes and metabolites. The horizontal axis is the first dimension coordinate and the vertical axis is the second dimension coordinate. The dots in the figure represent metabolites and the triangles represent proteins. The larger the absolute value in the coordinate, the greater the degree of association between this element and another group.

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

[0063] Synovial fluid samples were lysed at 4°C and centrifuged at 12,000 g for 25 min, and the supernatant was collected as the loading 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 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 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 blot was used to detect the expression of TNFSF11, CTSK, and ADGRE5. B, TNFSF11, CTSK, and ADGRE5 (all data are expressed as mean ± SD, and two-way analysis of variance was performed, followed by Tukey's test). C, D, quantification of the relative expression of metabolites 13-L-Hydroperoxylinoleic acid and 13-OxoODE. * means P < 0.05, ** means 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 considers the three parts of joint, specific, and residual between different data sets. The results are shown in Figure 7 shown.

[0067] The present invention performed high-throughput analysis of the synovial fluid of PVNS patients, including proteomics and metabolomics. A total of 156 DEPs and 62 differential metabolites were identified. Enrichment analysis showed that "osteoclast differentiation signaling" plays an important role in the progression of PVNS, and the expression of TNFSF11, cathepsin K: CTSKCTSK, and ADGRE5 in the lesion tissues of PVNS patients was significantly increased. By correlating these findings with the clinical manifestations of the patients, WGCNA analysis showed that CRP, ESR, and WBC were positively correlated with the gene module characterized by the blue module. Further analysis of the blue module genes showed an association with "osteoclast differentiation signaling".

[0068] PPI analysis revealed strong interactions between TNFSF11, cathepsin K:CTSK, and ADGRE5. ADGRE5 was found to have a close relationship with TNFSF11 and CTSK. ADGRE5's role in cell metabolism may complement its previous role in cell migration. ADGRE5 may have similar functions in activated immune cells and cancer cells, both of which have a tendency towards glycolytic metabolism and show enhanced migration and proliferation capabilities.

[0069] Through metabolomics analysis, it was determined that the "linoleic acid metabolism" and "biosynthesis of unsaturated fatty acids" pathways were significantly enhanced in PVNS samples, and differential metabolites such as 13-hydroxyperoxylinoleic acid: 13-L-Hydroperoxylinoleic acid and octadecadienoic acid: 13-OxoODE showed significantly increased expression. Proliferation and differentiation in the disease site were further analyzed using O2PLS (Two-way Orthogonal Partial Least Squares) analysis, revealing a close relationship between ADGRE5, TNFSF11, CTSK, 13-L-hydroperoxylinoleic acid, and 13-oxoODE. We then performed Western blot validation and confirmed that the expression of ADGRE5, TNFSF11, and CTSK was significantly increased in the PVNS group compared with the control group. These findings suggest that enhanced energy metabolism leads to increased lipid oxidation, which further regulates the increase in ADGRE5 expression and exacerbates osteoclast differentiation, as evidenced by the enhanced expression of TNFSF11 and CTSK. This ultimately leads to damage to 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 fewer interfering factors.

Claims

1. An application of differential tissue proteins and / or metabolites of 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.

2. The use of the differential tissue proteins and / or metabolites of pigmented villonodular synovitis according to claim 1, characterized in that: The differential tissue proteins and / or metabolites are used as screening markers for pigmented villonodular synovitis and / or are used for preparing a screening kit for pigmented villonodular synovitis.

3. 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.

4. 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 tissue proteinase K is P43235; and the database number of the adhesion G protein-coupled receptor is P48960.

5. A screening kit for pigmented villonodular synovitis, characterized in that: Includes reagents for detecting tissue proteins and / or metabolites; The tissue proteins include at least one of the following proteins: TNFSF11, cathepsin K, adhesion G protein-coupled receptor E5; The metabolites include at least one of the following products: 13-hydroxyperoxylinoleic acid, octadecadienoic acid; The reagent for detecting tissue protein and / or metabolites is used for detecting tissue protein and / or metabolites in joint fluid.

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