Osteoarthritis diagnostic marker screening method based on proteomics technology

Abstract

The invention provides an osteoarthritis diagnostic marker screening method based on a proteomics technology, and relates to the technical field of medical treatment. The method comprises the following steps: acquiring a cartilage tissue sample and a normal cartilage tissue sample of a patient with osteoarthritis, performing proteomic detection on the samples by adopting a data independent acquisition technology to obtain protein expression data, screening out differential expression proteins from the protein expression data, and determining whether the differential expression proteins are different or not. And determining the function and pathway characteristics of the differential expression protein through bioinformatics analysis, and verifying the application of the differential expression protein as the diagnostic marker according to the function and pathway characteristics.

Description

Technical Field

[0001] The present invention relates to the field of medical technology, and in particular to a method for screening osteoarthritis diagnostic markers based on proteomics technology. Background Art

[0002] Osteoarthritis (OA) is a common chronic joint disease characterized by joint pain, swelling, limited mobility, and morning stiffness, particularly affecting large joints such as the knees, hips, and spine. The pathogenesis of OA is complex, involving degenerative changes in articular cartilage, synovial inflammation, and bone hyperplasia. With aging, the prevalence of OA increases year by year, seriously affecting patients' quality of life and social productivity. Currently, the diagnosis of OA mainly relies on X-ray examinations and clinical symptoms, but the detection of early joint lesions remains challenging, and conventional imaging cannot accurately reflect subtle changes in cartilage. Therefore, the development of more sensitive biomarkers has become an important issue for the early diagnosis and treatment of OA.

[0003] Traditional proteomics methods, such as ELISA and Western blotting, have limited sensitivity and coverage in complex biological samples. In recent years, liquid chromatography-tandem mass spectrometry (LC-MS / MS), particularly data-independent acquisition (DIA), has become a core tool in proteomics research due to its significant advantages in quantitative accuracy and reproducibility. DIA can effectively detect low-abundance proteins in complex samples, providing a more precise molecular basis for screening osteoarthritis biomarkers. Summary of the Invention

[0004] The present invention provides a method for screening osteoarthritis diagnostic markers based on proteomics technology, which mainly includes:

[0005] Cartilage tissue samples from osteoarthritis patients and normal cartilage tissue samples are obtained, and proteomic testing of the samples is performed using data-independent acquisition technology to obtain protein expression data. Differentially expressed proteins are screened from the protein expression data, and the functions and pathway characteristics of the differentially expressed proteins are determined through bioinformatics analysis. The use of the differentially expressed proteins as diagnostic markers is verified based on the functions and pathway characteristics.

[0006] Furthermore, obtaining cartilage tissue samples and normal cartilage tissue samples from osteoarthritis patients includes: collecting diseased cartilage tissue from osteoarthritis patients and normal cartilage tissue from healthy controls from clinical specimens, performing sample processing on the diseased cartilage tissue and the normal cartilage tissue, obtaining protein samples by protein extraction, and determining the concentration of the protein samples according to a protein concentration quantification method.

[0007] Furthermore, the proteomic detection of the sample using data-independent acquisition technology includes: proteolysis of the sample to obtain a polypeptide sample, separation of the polypeptide sample by liquid chromatography, detection of the separated polypeptide sample using nano-liquid chromatography-mass spectrometry technology, acquisition of mass spectrometry data of the polypeptide sample, and determination of protein expression data by database search based on the mass spectrometry data.

[0008] Furthermore, the method of screening out differentially expressed proteins from the protein expression data includes: comparing the protein expression data according to a preset difference fold threshold and a statistical significance threshold, determining upregulated and downregulated differentially expressed proteins, performing cluster analysis on the differentially expressed proteins to obtain expression patterns of the differentially expressed proteins, and extracting key differentially expressed proteins based on the expression patterns.

[0009] Furthermore, the functions and pathway characteristics of the differentially expressed proteins are determined by bioinformatics analysis, including: performing signal pathway enrichment analysis on the differentially expressed proteins to determine pathways related to inflammatory response and cartilage degradation, using subcellular localization analysis to determine the distribution characteristics of the differentially expressed proteins, and determining the molecular functional characteristics of the differentially expressed proteins by domain enrichment analysis.

[0010] Furthermore, the verification of the application of the differentially expressed protein as a diagnostic marker based on the functional and pathway characteristics includes: detecting the expression level of the differentially expressed protein through experimental methods, judging the diagnostic ability of the differentially expressed protein based on the correlation between the expression level and the disease status, developing a detection method for the differentially expressed protein, and determining the application value of the detection method in the early diagnosis of the disease.

[0011] Furthermore, the comparing the protein expression data according to a preset difference fold threshold and a statistical significance threshold includes: setting the difference fold threshold to be greater than or equal to a preset up-regulation value or less than or equal to a preset down-regulation value, setting the statistical significance threshold to be less than a preset probability value, and screening out proteins with significant differences from the protein expression data according to the difference fold threshold and the statistical significance threshold.

[0012] Furthermore, the signal pathway enrichment analysis of the differentially expressed proteins includes: using a pathway database to perform enrichment calculation on the differentially expressed proteins, determining the distribution of the differentially expressed proteins in the cell signal transduction pathway, obtaining pathway data related to extracellular matrix interaction and cell apoptosis, and analyzing the role of the differentially expressed proteins in the disease pathology mechanism based on the pathway data.

[0013] The technical solution provided by the embodiment of the present invention may have the following beneficial effects:

[0014] The present invention discloses a method for screening diagnostic markers for osteoarthritis. The method screens differentially expressed proteins by performing proteomic analysis on cartilage tissue samples from osteoarthritis patients. The differential protein expression patterns are visualized using principal component analysis and cluster heat maps, and the key protein expression characteristics are quantified using box plots. Subcellular localization and functional enrichment analysis are further combined to determine the cellular distribution and related signaling pathways of the differential proteins. Finally, disease ontology analysis is used to verify the correlation between the screened protein markers and osteoarthritis and their diagnostic application value. The present invention integrates multidimensional proteomic data to systematically identify and verify osteoarthritis-specific diagnostic markers, providing new molecular targets for early diagnosis of the disease and personalized treatment. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 This is the differential protein volcano map provided by the present invention.

[0016] Figure 2 This is the principal component analysis diagram provided by the present invention.

[0017] Figure 3 This is the clustering heat map of the Top 100 differentially expressed proteins provided by the present invention.

[0018] Figure 4 This is a box plot of the key proteins provided by the present invention.

[0019] Figure 5 This is a subcellular localization analysis diagram provided by the present invention.

[0020] Figure 6 The KEGG pathway enrichment bar chart provided by the present invention.

[0021] Figure 7 Reactome pathway enrichment bar chart provided by the present invention.

[0022] Figure 8 Bar chart of InterPro domain enrichment provided by the present invention.

[0023] Figure 9 Disease ontology (DO) enrichment scatter plot. DETAILED DESCRIPTION

[0024] To help those skilled in the art better understand the technical solutions in this specification, the following will provide a clear and complete description of the technical solutions in the embodiments of this specification, in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of this specification, not all of them. All other embodiments derived by those skilled in the art based on the embodiments in this specification without creative effort shall fall within the scope of protection of this specification.

[0025] In this embodiment, a method for screening osteoarthritis diagnostic markers based on proteomics technology may specifically include:

[0026] S101. Obtain a cartilage tissue sample set from an osteoarthritis patient, wherein the sample set includes diseased tissue and normal tissue, and perform proteomic detection on the samples using a data-independent acquisition technology.

[0027] Clinical cartilage tissue specimens from 12 patients with osteoarthritis were obtained to confirm that the specimens included both normal and diseased tissue. The specimen data were processed using liquid chromatography-tandem mass spectrometry, and a data-independent acquisition method was used to generate a raw proteome dataset. High-throughput proteomics analysis was performed on this raw dataset to obtain protein expression data. Bioinformatics tools were used to screen this expression data and identify a list of differentially expressed proteins associated with the pathological process of osteoarthritis. Functional information was obtained for proteins in this list to determine their association with known biological functions. Based on this functional information, the differentially expressed proteins were classified to generate functional groupings. Pathway analysis tools were used to analyze this functional grouping data to identify the biological pathways in which the proteins participate. If pathway signature data were insufficient, proteomics databases were searched to supplement relevant pathway information and generate a complete pathway signature dataset. Systematic analysis tools were used to integrate this pathway signature dataset to identify key features of protein regulation during the pathological process of osteoarthritis.

[0028] S102 , extracting protein expression data from the sample set, and screening differentially expressed proteins using a difference fold and a statistical significance threshold to obtain a differential protein set.

[0029] Protein expression data were extracted from the sample set to obtain the original expression quantity dataset. Based on the extracted protein expression data, the differential fold change (FC) value of each protein was calculated. The calculation results were used to determine the list of proteins with differential fold change FC ≥ 1.2 or FC ≤ 0.83. The log2FC calculation method was used to screen a subset of proteins with an absolute value ≥ 0.263. The filtered protein subset was obtained and the P value of each protein was calculated. Based on the P value calculation results, the protein set with a P value < 0.05 was determined. The differentially expressed protein set was obtained by dual threshold screening of differential fold change and P value. The disease ontology DO enrichment analysis method was used to determine the correlation data between differentially expressed proteins and skeletal system diseases. Based on the enrichment analysis results, the association set of differentially expressed proteins with osteoarthritis, rheumatoid arthritis, and osteoporosis was obtained.

[0030] Specifically, protein expression data is extracted from the sample collection to obtain a raw expression dataset. For example, quantitative protein information is obtained through mass spectrometry analysis and recorded as the expression level of each protein in each sample. Based on the extracted protein expression data, the fold difference (FC) value of each protein is calculated using the formula FC = experimental group mean / control group mean to obtain the fold difference value for each protein. Based on the calculation results, a list of proteins with a fold difference (FC) ≥ 1.2 or FC ≤ 0.83 is determined. For example, proteins meeting the FC criteria of 1.5 and 0.8 are screened. Using the log2FC calculation method, a subset of proteins with an absolute value ≥ 0.263 is screened, for example, log2(1.5) ≈ 0.585 and log2(0.8) ≈ -0.322 are calculated, and proteins meeting the criteria are retained. After obtaining the screened protein subset, the P value for each protein is calculated, for example, using a t-test or rank sum test to obtain the significance level for each protein. Based on the P-value calculation results, the protein set with a P-value < 0.05 was determined, for example, proteins with significant differences of 0.03, 0.01, etc. were retained. A set of differentially expressed proteins was obtained by dual threshold screening using both the fold difference and the P-value, for example, 100 significantly differentially expressed proteins were ultimately screened. The disease ontology DO enrichment analysis method was used to determine the correlation data between differentially expressed proteins and skeletal system diseases, for example, by calculating enrichment significance using a hypergeometric test. Based on the enrichment analysis results, a set of differentially expressed proteins associated with osteoarthritis, rheumatoid arthritis, and osteoporosis was obtained. For example, 20 differentially expressed proteins were found to be significantly associated with osteoarthritis.

[0031] S103 , performing principal component analysis on the differential protein set to determine the distribution difference in protein expression profiles between the diseased tissue and the normal tissue, and obtaining principal component analysis results.

[0032] High-throughput proteomics data were obtained, and protein expression profile data of OA samples and normal samples were screened. The protein expression profile data were processed by principal component analysis, and the principal component score of each sample was calculated. The PCA1 feature was extracted based on the principal component score, and its explained variation ratio was determined to be 69.38%. A two-dimensional coordinate system was used to draw the distribution map of OA samples and normal samples to obtain the principal component analysis results. The expression data of the top 100 differentially expressed proteins were obtained to generate a differential protein set. The differentially expressed protein set was processed by clustering heat map analysis to determine its expression pattern under OA conditions. Based on the clustering results, differentially expressed proteins related to the pathological process of osteoarthritis were extracted to obtain a functional feature data set. If the functional feature data set contains pathway information, the protein pathway characteristics were analyzed in combination with bioinformatics tools to determine the pathway distribution pattern. By integrating the principal component analysis results with the pathway feature data, the distribution differences in protein expression profiles between diseased tissues and normal tissues were determined.

[0033] S104. Constructing a cluster heat map based on the differential protein set, analyzing the expression patterns of the differential proteins under osteoarthritis conditions, and obtaining cluster heat map data.

[0034] High-throughput proteomics analysis was used to obtain raw protein expression data, and a set of differentially expressed proteins associated with the pathological process of osteoarthritis was screened. Based on this screened set of differentially expressed proteins, the top 100 differentially expressed proteins were extracted, and their expression levels in osteoarthritis were determined. The expression data of the top 100 differentially expressed proteins were preprocessed using a normalization method to generate a normalized expression matrix. Hierarchical clustering analysis was then performed on the normalized expression matrix using a clustering algorithm to obtain similarity distance data between proteins. A cluster tree structure was constructed based on the similarity distance data to determine the grouping pattern of the top 100 differentially expressed proteins. The cluster tree structure and the normalized expression matrix data were combined to generate preliminary cluster heatmap data. The cluster heatmap data were visualized using bioinformatics tools to generate color-coded heatmap images. If the heatmap data had unclear groupings, adjustments were made based on protein functional annotation information to determine the optimized cluster heatmap data. Expression pattern features were extracted from the optimized cluster heatmap data to determine the clustered expression patterns of the top 100 differentially expressed proteins in osteoarthritis.

[0035] S105. Analyze the expression changes of key proteins in the differential protein set using a box plot, determine the quantitative expression characteristics of the key proteins, and obtain key protein expression data.

[0036] High-throughput proteomics analysis was used to obtain differentially expressed protein data, and a set of proteins closely related to the pathological process of osteoarthritis was screened. Based on the screened differential protein set, the top 100 differentially expressed proteins were extracted to obtain a preliminary dataset of key proteins. Principal component analysis was used to process the preliminary dataset of key proteins to determine significant differences in protein expression profiles between OA samples and normal samples. Cluster heat map analysis was performed on the top 100 differentially expressed proteins to obtain their expression pattern data under OA conditions. Based on the expression pattern data, a subset of key proteins with the highest correlation with the pathological process was screened to identify the key protein set. Boxplots were used to analyze expression changes in the key protein subset to obtain quantitative expression signature data for the key proteins. The expression range of key proteins was calculated using the quantitative expression signature data to obtain statistical distribution parameters for key protein expression. If the statistical distribution parameters of key protein expression showed significant differences, bioinformatics tools were used to analyze their functional and pathway characteristics to obtain functional annotation data for the key proteins. Based on the functional annotation data, the expression changes of key proteins and pathway information were integrated to obtain a dataset of key protein role characteristics in osteoarthritis.

[0037] S106. Processing the differential protein set by subcellular localization analysis to determine the subcellular distribution characteristics of the differential proteins and obtain subcellular localization analysis results.

[0038] High-throughput proteomics analysis was used to obtain sample data and identify a set of differentially expressed proteins associated with osteoarthritis. Principal component analysis (PCA) was used to analyze the differentially expressed protein set and identify significant differences in protein expression profiles between OA and normal samples. PCA1-explained variation was extracted based on the PCA1 results, resulting in 69.38% of protein expression variation signatures. Raw data for the differentially expressed protein set were obtained and functional annotation was performed using bioinformatics tools. Subcellular localization analysis was performed to analyze the differentially expressed protein set and determine their distribution within the nucleus, cytoplasm, or mitochondria. Based on the distribution ratios of the subcellular localization analysis results, enrichment signatures of the differentially expressed proteins in specific subcellular structures were determined. Pathway analysis tools were used to analyze the enrichment signature data and identify pathways associated with the pathological process of osteoarthritis. Correlations between the pathway analysis results and the subcellular localization data were obtained to determine the subcellular distribution and functional characteristics of the differentially expressed proteins. Data integration and functional analysis of the subcellular distribution and functional characteristics yielded systematic subcellular localization analysis results.

[0039] S107. Perform pathway analysis on the differential protein set using a functional enrichment analysis method to determine the signaling pathways in which the differential proteins are enriched, and obtain pathway enrichment data.

[0040] The differential protein set data was obtained and preliminarily processed using functional enrichment analysis methods. The KEGG database was used to retrieve information on differential protein-related pathways to obtain a preliminary dataset of pathway enrichment. Based on the characteristics of the PI3K-Akt signaling pathway, the preliminary dataset was screened to determine the enrichment level of the PI3K-Akt pathway. Based on the characteristics of the MAPK signaling pathway, the screened dataset was reanalyzed to determine the enrichment data of the MAPK pathway. Based on the characteristics of the extracellular matrix (ECM) receptor interaction pathway, the analysis results were compared to determine the enrichment of the ECM pathway. Statistical methods were used to integrate the enrichment data of the three pathways to obtain a comprehensive pathway enrichment dataset. Through pathway interaction analysis, correlation calculations were performed on the comprehensive dataset to determine the cross-influence between pathways. Significance indicators in the pathway enrichment data were obtained to determine the enrichment reliability of the differential proteins in each pathway. Through data normalization processing, the enrichment reliability results were formatted to obtain the final pathway enrichment data.

[0041] S108. Perform disease ontology analysis based on the pathway enrichment data to determine the correlation between the differentially expressed proteins and osteoarthritis-related diseases, and obtain disease ontology enrichment results.

[0042] Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis data were obtained to extract information related to the PI3K-Akt signaling pathway, MAPK signaling pathway, and extracellular matrix receptor interaction pathway, where differentially expressed proteins were significantly enriched. Based on the extracted pathway data, a list of associated differentially expressed proteins was searched to identify protein sets involved in the PI3K-Akt, MAPK, and ECM receptor interaction pathways. Disease ontology databases were queried to obtain disease ontology entries related to osteoarthritis, rheumatoid arthritis, and osteoporosis, and a standard set of disease ontology identifiers was obtained. A cross-analysis was performed between the differentially expressed protein set and the disease ontology identifier set to determine the preliminary association of the differentially expressed proteins with osteoarthritis-related diseases. Based on the cross-analysis results, the enrichment significance of the differentially expressed proteins in each disease ontology entry was calculated to obtain the enrichment analysis statistic. The statistical value was used to filter the significance threshold and identify a subset of differentially expressed proteins significantly associated with osteoarthritis, rheumatoid arthritis, and osteoporosis. Disease ontology enrichment analysis data from the technical briefing document were obtained and the association of the differentially expressed protein subset with OA, rheumatoid arthritis, and osteoporosis was compared to determine data consistency. Using the consistency judgment results, the association weights of differentially expressed proteins with osteoarthritis-related diseases were updated to obtain weighted disease ontology enrichment results. Based on the weighted enrichment results, the differentially expressed proteins and pathway information significantly associated with osteoarthritis were extracted to determine the final disease ontology enrichment analysis conclusions.

[0043] S109. Extract target diagnostic markers from the key protein expression data, and verify their application value in the diagnosis of osteoarthritis in combination with pathway enrichment data to obtain a diagnostic marker set.

[0044] Proteomic data based on data-independent acquisition technology were acquired, and key protein expression data were extracted. Key protein expression data were processed using functional enrichment analysis to obtain a protein function correlation set. Candidate markers associated with the pathological mechanism of osteoarthritis were screened based on the protein function correlation set to determine a preliminary diagnostic marker list. Pathway enrichment analysis was used to process the preliminary diagnostic marker list to obtain data on the association between markers and biological pathways. The biological significance of the preliminary diagnostic markers in osteoarthritis was verified through association data analysis to obtain a subset of markers that had been validated by pathways. Expression level data of the subset of pathway-validated markers was obtained to extract target diagnostic markers associated with early disease characteristics. Cluster analysis was performed based on the expression level data of the target diagnostic markers to determine the synergistic relationship between the markers. The target diagnostic markers were processed based on the synergistic relationship combined with the pathway enrichment data to obtain a diagnostic marker set. Statistical analysis methods were used to verify the stability of the diagnostic marker set to obtain the final osteoarthritis diagnostic marker set.

[0045] The specific experimental contents are as follows:

[0046] 1. Sample collection and processing:

[0047] 1.1 Clinical cartilage tissue specimens were collected from 12 OA patients, including normal tissue (NT) and pathological tissue (IT).

[0048] 1.2 The samples were tested using DIA quantitative proteomics technology.

[0049] 2. Differential protein screening:

[0050] Differentially expressed proteins were screened using a fold difference FC ≥ 1.2 or FC ≤ 0.83 (i.e., the absolute value of log2FC ≥ 0.263) and a P value < 0.05 as the threshold criteria. A total of 2,695 expressed proteins were identified, of which 485 were significantly upregulated and 157 were significantly downregulated.

[0051] 3. Analysis of differential protein data (see Figure 1-Figure 5 ):

[0052] 3.1 Principal component analysis (PCA) showed significant differences in protein expression profiles between OA samples and normal samples, and PCA1 explained 69.38% of the variation.

[0053] 3.2 Cluster heat map analysis of the top 100 differentially expressed proteins was performed to reveal their expression patterns under OA conditions. 3.3 Boxplots were used to quantitatively analyze the expression changes of key proteins (such as BASP1, RRBP1, PDXDC1, SMNDC1, CALD1, APOBR, FKBP14, DBN1, IFT27, etc.).

[0054] 3.4 Subcellular localization analysis showed that the differentially expressed proteins were significantly enriched in specific subcellular structures such as the nucleus, cytoplasm or mitochondria.

[0055] 4. Function and pathway enrichment analysis (see Figure 6-Figure 9 ):

[0056] 4.1 KEGG pathway enrichment analysis showed that differentially expressed proteins were significantly enriched in the PI3K-Akt signaling pathway, MAPK signaling pathway, and extracellular matrix (ECM) receptor interaction pathway, which play a key role in the inflammatory response, cartilage degradation, and cell apoptosis of OA.

[0057] 4.2 Reactome analysis further revealed the extracellular matrix organization, cytoskeleton regulation and immune regulation pathways in which the differentially expressed proteins were involved.

[0058] 4.3 InterPro domain enrichment analysis showed that the differentially expressed proteins were significantly enriched in immunoglobulin-like domains, fibronectin type III domains, and kinase domains, which are closely related to cell-cell interactions and signal transduction. 4.4 Disease ontology (DO) enrichment analysis showed that the differentially expressed proteins were significantly associated with skeletal diseases such as OA, rheumatoid arthritis, and osteoporosis.

[0059] Experimental methods

[0060] 1. Sample preparation

[0061] 1.1 Protein extraction

[0062] Transfer all samples to new EP tubes, add 300 µL of RIPA working solution, mix thoroughly, grind with steel beads at 70 Hz for 4 minutes, and centrifuge at 12,000 rpm at 4°C for 10 minutes. Transfer all samples to new EP tubes again, sonicate in an ice-water bath with a cell disruptor for 20 minutes to fully lyse the samples. Centrifuge at 12,000 rpm at 4°C for 10 minutes, and transfer the supernatant to a new EP tube.

[0063] 1.2 BCA quantification (BCA assay)

[0064] Pipette BCA working solution into a 96-well plate, 200 μL per well, with seven standard spots and one blank. Add 20 μL of sample (diluted accordingly) or standard protein (BSA). Incubate at 37°C with shaking for 30 minutes. Measure absorbance at 562 nm. Fit a standard curve using the standard protein and calculate the protein concentration of the corresponding sample.

[0065] 1.3 Acetone precipitation

[0066] For each sample, 100 μL of total protein was diluted to 1 mg / mL using RIPA lysis buffer. Acetone, pre-chilled to -20°C, was added to the sample in a 5-fold volume of acetone. Mix thoroughly and allow to precipitate overnight at -20°C. Centrifuge at 12,000 rpm for 10 minutes at 4°C and carefully remove the supernatant. Rinse the pellet twice with 200 μL of pre-chilled 80% acetone and centrifuge at 12,000 rpm. Remove the supernatant carefully.

[0067] 1.4 Protein Redissolve & Reduction & Alkylation

[0068] Add 100 µL of protein reconstitution solution and sonicate in a waterbath for 3 minutes to dissolve the protein precipitate. Add DTT to 5 mM and incubate at 55°C with shaking for 20 minutes to reduce disulfide bonds. Cool the sample to room temperature and add IAA to 15 mM in the dark for 30 minutes to alkylate the reduced disulfide bonds.

[0069] 1.5 Protein digestion

[0070] Dissolve trypsin in resuspension buffer to 0.5 g / L and incubate at room temperature for 5 minutes. Mix the sample thoroughly at a ratio of 1:50. Centrifuge briefly and incubate at 37°C with shaking at 1000 rpm overnight.

[0071] 1.6 SDC cleanup

[0072] Take 50 µL of sample and add TFA to the mixed sample (final concentration 2%). Mix thoroughly to precipitate SDC. Centrifuge at high speed for 10 minutes and transfer the supernatant to a new EP tube. Add 300 µL of 2% TFA and mix thoroughly. Centrifuge at 12,000 rpm for 10 minutes to extract the co-precipitated peptides (repeat extraction twice). Combine the supernatant fractions, centrifuge at high speed for 10 minutes, and transfer the supernatant to a new EP tube to obtain the peptide sample.

[0073] 1.7 Peptide desalting

[0074] Add 400L of Buffer B to a C18 desalted centrifuge tube for activation, centrifuge at 1000g for 1 min, and allow all the solution to slowly flow into the centrifuge tube. Add 400L of Buffer A for equilibration, centrifuge at 1000g for 1 min, and allow all the solution to slowly flow into the centrifuge tube. Add the sample supernatant, centrifuge at 1000g for 1 min, and allow all the solution to slowly flow into the centrifuge tube. Add 400L of Buffer A to rinse twice, centrifuge at 1000g for 1 min, and allow all the solution to slowly flow into the centrifuge tube. Add 200L of Buffer B to elute and collect in a new EP tube. Dry under vacuum at 4°C overnight. Add 100L of mobile phase A to reconstitute, centrifuge, and obtain the supernatant for mass spectrometry detection.

[0075] 2. nanoLC-MS / MS analysis

[0076] 200 ng of total peptide per sample was separated using a nano-UPLC system (nanoElute2) and then coupled to a mass spectrometer (timsTOF Pro2) equipped with a nanoliter ion source for data acquisition. Chromatographic separation was performed on a 75 μm ID × 15 cm reversed-phase column (PePSep C18, 1.9 L, 75 L × 15 cm, Bruker, Germany). The mobile phase was an acetonitrile-water-formic acid system, with mobile phase A consisting of 0.1% formic acid in water and phase B consisting of 0.1% formic acid in acetonitrile. After the column was equilibrated with 100% phase A, the sample was loaded directly onto the column via an autosampler and separated by a gradient flow at a flow rate of 300 nL / min over 60 minutes. The mobile phase B ratio was: 2% for 0 minutes, 2-22% for 45 minutes, 22-37% for 5 minutes, 37-80% for 5 minutes, and 80% for 5 minutes. The mass spectrometer was operated in DDA PaSEF mode for DDA data acquisition, with a scan range of 100–1700 m / z. During the PASEF MS / MS scan, the collision energy increased linearly with ion mobility, from 20 eV (1 / K0 = 0.6 Vs / cm2) to 59 eV (1 / K0 = 1.6 Vs / cm2).

[0077] 3. Library Search and Protein Quantification

[0078] The raw data files were searched using SpectroMine (4.2.230428.52329; Biognosys AG) software with the Pulsar search engine, and qualitative analysis was performed after the search.

[0079] Analysis: Through high-throughput proteomic analysis combined with advanced bioinformatics tools, differentially expressed proteins closely related to the pathological process of osteoarthritis were screened out, and the functions and pathway characteristics of these proteins were systematically revealed. Specifically, the present invention identified 2,695 proteins from the cartilage tissues of 12 OA patients, of which 485 were significantly upregulated and 157 were significantly downregulated. Through principal component analysis (PCA) and cluster heat map analysis, the present invention revealed significant differences in protein expression profiles between OA samples and normal samples, and further verified the expression changes of key proteins (such as BASP1, RRBP1, PDXDC1, SMNDC1, CALD1, APOBR, FKBP14, DBN1, IFT27, etc.) under OA conditions.

[0080] Functional enrichment analysis revealed that the differentially expressed proteins were significantly enriched in the PI3K-Akt signaling pathway, the MAPK signaling pathway, and the extracellular matrix (ECM) receptor interaction pathway, which play key roles in OA inflammation, cartilage degradation, and apoptosis. Furthermore, Reactome analysis revealed that the differentially expressed proteins were involved in extracellular matrix organization, cytoskeleton regulation, and immune regulation pathways. InterPro domain enrichment analysis revealed that the differentially expressed proteins were significantly enriched in immunoglobulin-like domains, fibronectin type III domains, and kinase domains, which are closely related to intercellular interactions and signal transduction. Disease ontology (DO) enrichment analysis further revealed that the differentially expressed proteins were significantly associated with skeletal diseases such as osteoarthritis, rheumatoid arthritis, and osteoporosis.

[0081] The present invention discloses a method for screening diagnostic markers for osteoarthritis. The method screens differentially expressed proteins by performing proteomic analysis on cartilage tissue samples from osteoarthritis patients. The differential protein expression patterns are visualized using principal component analysis and cluster heat maps, and the key protein expression characteristics are quantified using box plots. Subcellular localization and functional enrichment analysis are further combined to determine the cellular distribution and related signaling pathways of the differential proteins. Finally, disease ontology analysis is used to verify the correlation between the screened protein markers and osteoarthritis and their diagnostic application value. The present invention integrates multidimensional proteomic data to systematically identify and verify osteoarthritis-specific diagnostic markers, providing new molecular targets for early diagnosis of the disease and personalized treatment.

[0082] With the above embodiments of the present invention as inspiration, and through the above description, relevant personnel can make various changes and modifications without departing from the technical scope of this invention. The technical scope of this invention is not limited to the contents of the specification, but must be determined according to the scope of the claims.

Claims

1. A method for screening osteoarthritis diagnostic markers based on proteomics technology, characterized in that: include: Cartilage tissue samples from osteoarthritis patients and normal cartilage tissue samples are obtained, and proteomic testing of the samples is performed using data-independent acquisition technology to obtain protein expression data. Differentially expressed proteins are screened from the protein expression data, and the functions and pathway characteristics of the differentially expressed proteins are determined through bioinformatics analysis. The use of the differentially expressed proteins as diagnostic markers is verified based on the functions and pathway characteristics.

2. The method for screening osteoarthritis diagnostic markers based on proteomics technology according to claim 1, wherein: The method of obtaining cartilage tissue samples from osteoarthritis patients and normal cartilage tissue samples comprises: Diseased cartilage tissue from osteoarthritis patients and normal cartilage tissue from healthy controls are collected from clinical specimens. Samples of the diseased cartilage tissue and the normal cartilage tissue are processed, protein samples are obtained by protein extraction, and the concentration of the protein samples is determined according to a protein concentration quantification method.

3. The method for screening osteoarthritis diagnostic markers based on proteomics technology according to claim 1, wherein: The method of performing proteomic detection on the sample using a data-independent acquisition technology comprises: The sample is subjected to proteolysis to obtain a polypeptide sample, the polypeptide sample is separated by liquid chromatography, and the separated polypeptide sample is detected using nano-liquid chromatography-mass spectrometry technology to obtain mass spectrometry data of the polypeptide sample, and protein expression data is determined by database search based on the mass spectrometry data.

4. The method for screening osteoarthritis diagnostic markers based on proteomics technology according to claim 1, wherein: The step of screening the differentially expressed proteins from the protein expression data comprises: The protein expression data are compared according to a preset difference fold threshold and a statistical significance threshold to determine the upregulated and downregulated differentially expressed proteins, cluster analysis is performed on the differentially expressed proteins to obtain the expression patterns of the differentially expressed proteins, and key differentially expressed proteins are extracted based on the expression patterns.

5. The method for screening osteoarthritis diagnostic markers based on proteomics technology according to claim 1, wherein: Determining the functions and pathway characteristics of the differentially expressed proteins by bioinformatics analysis includes: Signal pathway enrichment analysis was performed on the differentially expressed proteins to determine the pathways related to inflammatory response and cartilage degradation, subcellular localization analysis was used to determine the distribution characteristics of the differentially expressed proteins, and domain enrichment analysis was performed to determine the molecular functional characteristics of the differentially expressed proteins.

6. The method for screening osteoarthritis diagnostic markers based on proteomics technology according to claim 1, wherein: The method of verifying the use of the differentially expressed protein as a diagnostic marker based on the functional and pathway characteristics includes: The expression levels of the differentially expressed proteins are detected by experimental methods, the diagnostic ability of the differentially expressed proteins is determined based on the correlation between the expression levels and the disease status, a detection method is developed for the differentially expressed proteins, and the application value of the detection method in the early diagnosis of the disease is determined.

7. The method for screening osteoarthritis diagnostic markers based on proteomics technology according to claim 4, characterized in that: The comparing the protein expression data according to a preset difference fold threshold and a statistical significance threshold comprises: The difference fold threshold is set to be greater than or equal to a preset up-regulation value or less than or equal to a preset down-regulation value, and the statistical significance threshold is set to be less than a preset probability value. Proteins with significant differences are screened from the protein expression data based on the difference fold threshold and the statistical significance threshold.

8. The method for screening osteoarthritis diagnostic markers based on proteomics technology according to claim 5, wherein: The signal pathway enrichment analysis of the differentially expressed proteins comprises: The pathway database is used to perform enrichment calculation on the differentially expressed proteins, determine the distribution of the differentially expressed proteins in the cell signaling pathway, obtain pathway data related to extracellular matrix interaction and cell apoptosis, and analyze the role of the differentially expressed proteins in the disease pathology mechanism based on the pathway data.

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