Nucleic acid combination product for detecting SIAH2, diagnostic kit for osteoarthritis and application thereof

Abstract

The invention belongs to the technical field of biomedicine, and particularly relates to a nucleic acid combination product for detecting SIAH2, a diagnostic kit for osteoarthritis and application of the diagnostic kit. According to the application, the models are established by using machine learning to analyze various cell death modes, and the application finds that in the models of the four cell death modes, the ferroptosis model has the highest diagnosis efficiency on the OA and identifies the corresponding biomarkers. It is found that SIAH2 is used as a biomarker to regulate occurrence and development of OA through cartilage cells, and a new diagnosis and treatment strategy is provided for OA.

Description

Technical Field

[0001] The present application belongs to the field of biomedical technology, and specifically relates to a nucleic acid combination product for detecting SIAH2, a diagnostic kit for osteoarthritis and applications thereof. Background Art

[0002] Osteoarthritis (OA) is a common degenerative disease characterized by symptoms such as discomfort, pain, and joint dysfunction. Current treatments, including symptomatic treatment and joint replacement surgery, cannot effectively prevent the pathological progression of OA or meet people's demand for a higher quality of life. Therefore, it is particularly important to further explore the pathogenesis of OA and its corresponding diagnosis and treatment methods.

[0003] Articular cartilage is a connective tissue lacking blood vessels and nerves, composed of chondrocytes and their synthesized extracellular matrix (ECM). Under normal physiological conditions, chondrocytes maintain cartilage homeostasis by regulating the synthesis and degradation of ECM and controlling cell differentiation and death. Cartilage degeneration is a core component of the pathogenesis of OA. In OA, cartilage homeostasis is disrupted, leading to massive chondrocyte death caused by inflammation and oxidative stress, which in turn causes ECM degradation, aggravates cartilage degeneration, and ultimately causes loss of joint function. Therefore, chondrocyte death plays a key role in the development of OA. In recent decades, multiple forms of chondrocyte death have been found in cartilage tissue of OA patients and animal models, including apoptosis, ferroptosis, necroptosis, autophagy, pyroptosis, and the recently discovered copper death. Although inhibition of apoptosis, necroptosis, or pyroptosis has shown potential to improve OA in animal models, no single cell death inhibitor has been successful in clinical trials. This suggests that multiple forms of cell death are jointly involved in the occurrence and development of OA and jointly promote cartilage degeneration.

[0004] Biomarkers reflect structural or functional changes in cells, tissues, and organs, and are essential for the early diagnosis and treatment of OA. However, biomarkers of cell death in OA still need to be further explored. Sevenin absentia homolog 2 (SIAH2) is an E3 ubiquitin ligase that plays a key role in regulating metabolism, stress signaling, and cell cycle. Under hypoxic conditions, SIAH2 promotes the degradation of hypoxia-inducible factor (HIF) prolyl hydroxylase domain protein 3 (PHD3), leading to upregulation of HIF-1α, thereby helping cells adapt to hypoxia. The specific role of SIAH2 in OA is still unknown.

[0005] Therefore, systematic and in-depth exploration of biomarkers related to cell death in OA is essential for a comprehensive understanding of the disease process and the appropriate development of effective diagnostic, therapeutic, and prognostic methods. In addition, the markers for the diagnosis of osteoarthritis in traditional technologies still have the problem of low sensitivity. Summary of the invention

[0006] Based on this, one embodiment of the present application provides a nucleic acid combination product for detecting SIAH2, a diagnostic kit for osteoarthritis and applications thereof.

[0007] In one aspect, the present application provides a nucleic acid combination product, which includes a reagent for detecting the expression level of the SIAH2 gene.

[0008] In one embodiment, the reagent for detecting the expression level of the SIAH2 gene includes detection primers as shown in SEQ ID NO.1 to SEQ ID NO.2.

[0009] Another aspect of the present application provides a diagnostic kit for osteoarthritis, wherein the kit comprises the nucleic acid combination product.

[0010] In one embodiment, the osteoarthritis is primary osteoarthritis or secondary osteoarthritis.

[0011] In one embodiment, the kit further includes one or more of a negative control, a positive control and a quality control product.

[0012] In one embodiment, the kit further includes: one or more of a nucleic acid releasing reagent, a nucleic acid extracting reagent, and a nucleic acid amplification reagent.

[0013] In one embodiment, the nucleic acid amplification reagent includes DNA polymerase, dNTP, UNG enzyme, PCR buffer and Mg 2+ One or more of .

[0014] Another aspect of the present application provides the use of the nucleic acid combination product or the kit in the preparation of an osteoarthritis diagnostic product.

[0015] On the other hand, the present application provides the use of a SIAH2 gene expression promoter, SIAH2 gene mRNA and SIAH2 protein in the preparation of a drug for treating osteoarthritis.

[0016] On the other hand, the present application provides a drug for treating osteoarthritis, which includes an agent for promoting the expression of gene SIAH2, a transcription product of gene SIAH2 or an expression product of gene SIAH2, and pharmaceutically acceptable excipients.

[0017] The details of one or more embodiments of the present application are set forth in the description which follows, and other features, objects, and advantages of the present application will be apparent from the description and its claims. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions in the embodiments of the present application and to more completely understand the present application and its beneficial effects, the following is a brief introduction to the drawings required for the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present application, and those skilled in the art can obtain other drawings based on these drawings without creative work.

[0019] Figure 1 This is the big data verification flow chart for this application;

[0020] Figure 2 for data processing and identification of DEGs and OA-related module genes;

[0021] Figure 3 for the identification and functional analysis of DCDEGs;

[0022] Figure 4 The ScRNA-seq data show the expression of DCDEGs at different stages;

[0023] Figure 5 To screen biomarkers associated with cell death using machine learning algorithms;

[0024] Figure 6 To evaluate the diagnostic performance of the logistic regression model in OA cartilage by ROC and DCA;

[0025] Figure 7 To evaluate the diagnostic performance of the logistic regression model in OA synovium and subchondral bone by ROC and DCA;

[0026] Figure 8 The expression of SIAH2, GDF15, and PPARG in the database;

[0027] Fig. 9 Validation of RNA expression of SIAH2, GDF15, and PPARG;

[0028] Fig.10 To verify the expression of SIAH2;

[0029] Fig.11 To inhibit the effect of SIAH2 on chondrocytes. DETAILED DESCRIPTION

[0030] Below in conjunction with embodiment and example, the application is further described in detail.It should be understood that these embodiments and examples are only used to illustrate the application and are not used to limit the scope of the application, and the purpose of providing these embodiments and examples is to make the understanding of the disclosure of the application more thorough and comprehensive.It should also be understood that the application can be implemented in many different forms, is not limited to the embodiment and example described herein, and those skilled in the art can make various changes or modifications without violating the connotation of the application, and the equivalent form obtained also falls within the protection scope of the application.In addition, in the description below, a large number of specific details are given in order to provide a more comprehensive understanding of the application, and it should be understood that the application can be implemented without one or more of these details.

[0031] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.

[0032] the term

[0033] Unless otherwise specified or incompatible herewith, the terms and phrases used herein shall have the following meanings:

[0034] The terms "and / or", "or / and", and "and / or" used in this article include any one of two or more related listed items, and also include any and all combinations of related listed items, and the arbitrary and all combinations include any two related listed items, any more related listed items, or a combination of all related listed items. It should be noted that when at least three items are connected by at least two conjunctions selected from "and / or", "or / and", and "and / or", it should be understood that in this application, the technical solution undoubtedly includes technical solutions that are all connected by "logical and", and undoubtedly includes technical solutions that are all connected by "logical or". For example, "A and / or B" includes three parallel solutions of A, B and A+B. For example, the technical solution of "A, and / or, B, and / or, C, and / or, D" includes any one of A, B, C, and D (that is, the technical solution that is all connected by "logical OR"), and also includes any and all combinations of A, B, C, and D, that is, the combination of any two or any three of A, B, C, and D, and also includes the combination of four of A, B, C, and D (that is, the technical solution that is all connected by "logical AND").

[0035] All documents mentioned in this application are cited as references in this application, just as each document is cited as reference separately. Unless they conflict with the invention purpose and / or technical solution of this application, the cited documents involved in this application are cited with all contents and all purposes. When the cited documents are involved in this application, the definitions of relevant technical features, terms, nouns, phrases, etc. in the cited documents are also cited. When the cited documents are involved in this application, the examples and preferred methods of the cited relevant technical features can also be incorporated into this application as references, but are limited to the implementation of this application. It should be understood that when the cited content conflicts with the description in this application, the present application shall prevail or be modified adaptively according to the description of this application.

[0036] "Enrichment analysis" is a method for analyzing high-throughput experimental data, which is often used to understand the enrichment of gene sets or other biological entities under given experimental conditions, pathways, or specific biological processes. Through enrichment analysis, we can understand the importance of these genes in specific biological processes and infer the roles they may play. After screening 26 DCDEGs, this study used GeneMANIA, GO, and Proteomaps for enrichment analysis. These DCDEGs were significantly enriched in responses to oxidative stress and reactive oxygen species in GO analysis, and were also enriched in oxidative stress functions in GeneMANIA. In the Proteomaps scree map, DCDEGs were enriched in metabolism, including biosynthesis, immune system, lipid, and steroid metabolism.

[0037] "ScRNA-seq" is a new technology that can sequence transcriptomes at the single-cell level. It can be used to analyze the gene expression, functional enrichment, and metabolic pathways of mRNA in single cells. This study used pseudo-time series analysis to show the reliability of chondrocyte samples of various OA stages in scRNA data (GSE104782). The "AddModuleScore" function is often used to quantify the expression level of gene sets.

[0038] The protein encoded by SIAH2 is a member of the Seven in Absentia Homolog (SIAH) family. The protein functions as an E3 ligase and is involved in ubiquitination and proteasome-mediated degradation. However, the role of SIAH2 in OA has not been reported. This study showed downregulation of SIAH2 after chondrocytes were stimulated with IL-1β, which was consistent with the database prediction results.

[0039] SIAH2 plays a key role in regulating metabolism, stress signal transduction and cell cycle. Under hypoxic conditions, SIAH2 promotes the degradation of hypoxia-inducible factor (HIF) prolyl hydroxylase domain protein 3 (PHD3), leading to upregulation of HIF-1α, thereby helping cells adapt to hypoxia. SIAH2 can also ubiquitinate and degrade LATS2, allowing YAP to enter the nucleus and activate the Hippo signaling pathway. The specific role of SIAH2 in OA is still unknown. This study found that SLAH2 protein expression was significantly downregulated in OA cartilage through in vivo experiments, and reduced SIAH2 protein expression in chondrocytes after IL-1β stimulation. This suggests that SIAH2 may be one of the biomarkers of OA.

[0040] In one aspect, the present application provides a nucleic acid combination product, which includes a reagent for detecting the expression level of the SIAH2 gene.

[0041] In a specific example, the reagent for detecting the expression level of the SIAH2 gene includes detection primers as shown in SEQ ID NO.1 to SEQ ID NO.2.

[0042] Another aspect of the present application provides a diagnostic kit for osteoarthritis, wherein the kit comprises the nucleic acid combination product.

[0043] In a specific example, the kit further includes one or more of a negative control, a positive control, and a quality control product.

[0044] Optionally, the kit further comprises: one or more of a nucleic acid releasing reagent, a nucleic acid extracting reagent, and a nucleic acid amplification reagent.

[0045] Further optionally, the nucleic acid amplification reagent includes DNA polymerase, dNTP, UNG enzyme, PCR buffer and Mg 2+ One or more of .

[0046] Another aspect of the present application provides the use of the nucleic acid combination product or the kit in the preparation of an osteoarthritis diagnostic product.

[0047] On the other hand, the present application provides the use of a SIAH2 gene expression promoter, SIAH2 gene mRNA and SIAH2 protein in the preparation of a drug for treating osteoarthritis.

[0048] In one embodiment, the osteoarthritis is primary osteoarthritis or secondary osteoarthritis.

[0049] On the other hand, the present application provides a drug for treating osteoarthritis, which includes an agent for promoting the expression of gene SIAH2, a transcription product of gene SIAH2 or an expression product of gene SIAH2, and pharmaceutically acceptable excipients.

[0050] In a specific example, pharmaceutically acceptable excipients are also included;

[0051] Optionally, the auxiliary material is selected from one or more of a diluent, a binder, a disintegrant, a lubricant and a wetting agent.

[0052] Optionally, the dosage form of the drug is one or more of tablets, capsules, granules, pills, injections and sustained-release preparations.

[0053] Further optionally, the pharmaceutically acceptable excipient is selected from one or more of a diluent, a binder, a disintegrant, a lubricant and a wetting agent.

[0054] Wherein, the diluent is selected from at least one of starch, dextrin, sucrose, glucose, lactose, mannitol, sorbitol, xylitol, microcrystalline cellulose, calcium sulfate, calcium hydrogen phosphate and calcium carbonate.

[0055] The adhesive is selected from at least one of starch slurry, dextrin, syrup, honey, glucose solution, microcrystalline cellulose, acacia slurry, gelatin slurry, sodium carboxymethyl cellulose, methyl cellulose, hydroxypropyl methyl cellulose, ethyl cellulose, acrylic resin, carbomer, polyvinyl pyrrolidone and polyethylene glycol.

[0056] The disintegrant is selected from at least one of starch, microcrystalline cellulose, low-substituted hydroxypropyl cellulose, cross-linked polyvinyl pyrrolidone, cross-linked sodium carboxymethyl cellulose, sodium carboxymethyl starch, polyoxyethylene, sorbitol, fatty acid ester and sodium lauryl sulfate. The lubricant is selected from at least one of talc, silicon dioxide, stearate, tartaric acid, liquid paraffin and polyethylene glycol.

[0057] Wherein, the wetting agent is selected from at least one of water, ethanol and isopropanol.

[0058] Optionally, the drug is an injection, and the pharmaceutically acceptable excipient is selected from at least one of a solubilizer, a pH regulator and an osmotic pressure regulator.

[0059] Further optionally, the solubilizing agent is selected from at least one of ethanol, isopropanol, propylene glycol, polyethylene glycol, poloxamer, lecithin and hydroxypropyl-β-cyclodextrin.

[0060] Optionally, the pH regulator is selected from at least one of citrate, phosphate, carbonate, acetate, hydrochloric acid and hydroxide. Wherein, the osmotic pressure regulator is selected from at least one of sodium chloride, mannitol, glucose, phosphate, citrate and acetate.

[0061] This application uses machine learning to establish a model to analyze various types of cell death. This application finds that among the four cell death models, the ferroptosis model has the highest diagnostic efficacy for OA, showing the outstanding diagnostic efficacy of ferroptosis in OA, and identifying the corresponding biomarkers. At the same time, it was found that SIAH2, as a biomarker, regulates the occurrence and development of OA through chondrocytes, provides a new diagnosis and treatment strategy for OA, and provides a reagent for detecting the expression of gene SIAH2 in the preparation of osteoarthritis diagnostic products. Further, this application focuses on screening and preliminary verification of three ferroptosis-related biomarkers (SIAH2, GDF15 and PPARG), among which SIAH2 has the highest diagnostic efficacy for OA. In vitro and in vivo experiments have shown that SIAH2 expression is significantly downregulated in the OA model, and inhibition of SIAH2 will cause chondrocyte ECM metabolism imbalance, which indicates that SIAH2 may be a potential biomarker related to OA cartilage. These findings provide new ideas for the clinical diagnosis and targeted treatment of OA.

[0062] The embodiments of the present application will be described in detail below in conjunction with examples. It should be understood that these examples are only used to illustrate the present application and are not intended to limit the scope of the present application. The experimental methods for which specific conditions are not specified in the following examples are preferably referred to the guidance provided in the present application, and can also be based on the experimental manual or normal conditions in this area, can also be based on the conditions recommended by the manufacturer, or refer to experimental methods known in the art.

[0063] In the following specific embodiments, the measured parameters of raw material components may have slight deviations within the range of weighing accuracy unless otherwise specified. For temperature and time parameters, acceptable deviations caused by instrument test accuracy or operation accuracy are allowed.

[0064] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0065] Example 1

[0066] 1. Database Analysis

[0067] 1. Collection and preprocessing of bulk RNA sequencing data and microarray data

[0068] This application downloaded the gene expression profiles of cartilage tissues of OA patients and trauma individuals as controls (GSE114007, GSE57218, and GSE169077) from the NCBI GEO database (https: / / www.ncbi.nlm.nih.gov / geo / ) (Table 1). The original matrix and platform annotation files were pre-processed by Perl scripts and R packages of R language software to generate the gene expression matrix. The specific design flow chart is shown in Figure 1 shown.

[0069] GSE114007 was used as a training set. GSE57218 and GSE169077 were used as validation sets. In addition, two microarray datasets (GSE46750 and GSE55235) containing gene expression profiles of OA and control synovial tissues, and a microarray dataset (GSE51588) containing gene expression profiles of OA and control subchondral bone tissues were obtained as validation sets. The R package “sva” was used to correct batch effects caused by different platforms and obtain the expression of intersection genes, including the two platforms within GSE114007, the merge of GSE57218 and GSE169077, and the merge of GSE46750 and GSE55235.

[0070] Table 1 HC: healthy control group; OA: osteoarthritis; S0: refers to cartilage close to normal; S1: slightly softened and swollen cartilage surface; S2: cartilage surface diameter less than 1

[0071]

[0072] 1 cm roughness and fibrosis; S3: cartilage surface loss with a diameter greater than 1 cm, but the underlying cartilage is not exposed; S4: full-thickness tear and partial exposure of the underlying cartilage; oLT: mildly damaged lateral tibial plateau cartilage; MT: damaged medial tibial plateau cartilage.

[0073] 2. Analysis of differentially expressed genes (DEGs)

[0074] The raw data of the two platforms of GSE114007 were normalized using R language software, including calculating the logarithm of the larger value matrix to eliminate batch effects. Principal component analysis (PCA) was used to evaluate the effectiveness of batch effect correction. The R package "DESeq2" was then used to screen differentially expressed genes between the OA group and the control group in the normalized data, with a filtering threshold of |log2(foldchange)|>1 and an adjusted P value (adj.P)<0.05. The volcano plot of differentially expressed genes was completed by the R package "ggplot2", and the box plot was drawn by the R package "ggpubr".

[0075] 3. Establish a co-expression network to identify OA-related module genes

[0076] The WGCNA method is helpful to study genome expression. Its basic principle is to cluster highly correlated genes into modules, and then cluster these characteristic genes or hub genes in the modules to form modules, measure their relationship with genes, explore their relationship, and sort them or genes. The R package "WGCNA" is used to construct gene co-expression networks. By drawing a clustering tree, observe whether there are abnormal samples in the data set. In order to determine whether there is a correlation between two genes, a threshold needs to be set for screening, and those above the threshold are considered to be correlated. In order to avoid the error of the hard threshold, a soft threshold (soft-thresholding power, i.e., β value) is set to perform a power operation on the correlation coefficient, thereby strengthening strong correlations and weakening weak correlations, so that the connection network between genes obeys a scale-free network distribution. By constructing an adjacency matrix, calculating the topological overlap matrix (TOM), and using average hierarchical clustering and dynamic tree-cutting algorithms to construct a clustering tree to detect gene modules. Module membership (MM) and gene importance (GS) are calculated to connect modules with clinical characteristics. A heat map of the correlation between the modules and the two clinical traits "Health" and "OA" was drawn, and the modules with higher Pearson MM correlation and P < 0.05 were defined as key modules. The genes of the corresponding modules were used for further analysis.

[0077] 4. Selection of cell death-related OA-related module differentially expressed genes (DCDEGs)

[0078] 431 ferroptosis-related genes were downloaded from the FerroDb v2 database. 273 apoptosis-related genes were collected from the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway database (https: / / www.kegg.jp / entry / hsa04210) and the REACTOME database (https: / / www.reactome.org / content / detail / R-HSA-109581). 207 pyroptosis-related genes were searched in the NCBI database. 378 necroptosis-related genes were searched in the NCBI database and the KEGG pathway database (https: / / www.kegg.jp / entry / hsa04217). The OA-related module differentially expressed genes obtained from the GSE114007 dataset were intersected with these cell death-related genes to obtain DCDEGs, which were displayed using Venn diagrams and Upset diagrams.

[0079] 5. Functional enrichment analysis of DCDEGs

[0080] The correlation analysis of DCDEGs was performed using the R package "corrplot". The interaction network of DCDEGs and their co-expression was analyzed using the GeneMANIA online website to explore the potential biological functions and pathways of DCDEGs. GeneMANIA will use rich genomics and proteomics data to find genes with similar functions, form an interaction network containing elements such as physical effects, co-expression, predicted interactions, co-localization, genetic associations, and protein domain sharing, and display the enriched biological terms and pathways and the weight of the predicted value of each gene. In addition, the R package "clusterProfiler" was used to perform gene ontology (GO) enrichment analysis, and entries with P < 0.05 were considered statistically significant.

[0081] Proteomics technology can obtain a lot of information. This application uses the Proteomaps online website to visualize differential protein data in differential analysis. This is a tool that displays protein composition as well as protein abundance and function, and is used to cluster DCDEGs according to their KEGG pathway annotations. In the created Proteomaps visualization rectangle, the entire area is divided into color-coded polygons representing top categories, and the top category areas are divided into disease-related subcategories, common region subcategories shared by functionally related proteins, and common region subcategories shared with related genes. Functionally related proteins are assigned to adjacent regions and used to observe the dynamic changes in the proportion of KEGG pathways between different groups.

[0082] 6. ScRNA sequencing data processing

[0083] In the ScRNA sequencing dataset (GSE104782), the original authors divided the knee chondrocyte regions into five levels: S0, S1, S2, S3, and S4, from "normal" to "severely abnormal", according to the ICRS cartilage injury classification system. ScRNA sequencing data provides an unbiased assessment of many heterogeneous cells at the single-cell level, revealing the complexity of molecular composition and differences from corresponding cells in cartilage tissue. The data were processed as follows: the R package "Seurat" v4.3 was used for quality control, normalization, integration, batch correction, principal component analysis, cell clustering, and unified manifold approximation and projection (UMAP) dimensionality reduction.

[0084] During the quality control process, cells with less than 200 genes, more than 10,000 genes, or mitochondrial genes accounting for more than 20% were screened out. The gene expression of each cell was normalized using the “LogNormalize” method. After data normalization, 3,000 highly differentially expressed genes were identified by the “FindVariableFeatures” function, and the 3,000 differentially expressed genes were normalized by the “ScaleData” function and then subjected to principal component analysis (PCA) dimensionality reduction. The obtained principal components (PCs) were visualized by UMAP dimensionality reduction. The “DoHeatmap” function in the R package “Seurat” was used to create a heat map of the expression of DCDEGs in various OA cartilage lesion stages. The R package “monocle” is a tool for time series analysis based on the biological processes of individual cells, which can simulate the dynamic changes of chondrocytes. The R package “monocle” v2.24.1 was used for pseudo-time series analysis, also known as single-cell trajectory analysis. The expression abundance scores of DCDEGs in individual cells were calculated by the “AddModuleScore” function in the R package “Seurat”.

[0085] 7. Using machine learning technology to screen biomarkers of cell death

[0086] DCDEGs were further used to identify significant biomarkers. Feature identification methods of machine learning are a process that limits the number of factors and are particularly suitable for building predictive models. This study included least absolute shrinkage and selection algorithm (LASSO) regression, random forest (RF) algorithm, and support vector machine recursive feature elimination (SVM-RFE) to explore feature genes. LASSO regression is a dimensionality reduction method that has shown superior performance to regression analysis when evaluating high-dimensional data and uses regularization to improve prediction accuracy, thereby selecting linear models and retaining reliable feature genes. Binomial distribution variables were further presented in LASSO classification, with standard error values ​​as the minimum parameter. LASSO regression was used to screen feature genes by applying the R package “glmnet”. RF is a supervised machine learning algorithm based on the decision tree algorithm, which is used to solve regression and classification problems. Feature importance was determined by the average reduced Gini index calculated by RF. RF was used to screen feature genes by applying the R package “randomForest”. SVM-RFE, a machine learning method based on support vector machines that can recursively rank features to avoid overfitting, was used to find the best feature genes by removing the feature vectors produced by SVM. SVM-RFE was used to screen the characteristic genes by applying the R package "e1071". The most significant characteristic genes among the three algorithms were intersected and used as biomarkers for subsequent analysis.

[0087] 8. Establishment and validation of cell death biomarker diagnostic model

[0088] A logistic regression model was constructed using biomarkers of each cell death mode to effectively test the diagnostic efficacy of each cell death mode. The logistic regression model was made using the R package "rms". Two methods were then used to evaluate the performance of the logistic regression model in predicting the occurrence of OA: the receiver operating characteristic curve (ROC) and decision curve analysis (DCA). In ROC, the closer the area under the curve AUC is to 1, the better the diagnostic efficacy. DCA is an analytical method that estimates the clinical usefulness of a prediction model by calculating the net benefit within a threshold probability range. ROC and DCA curves were made using the R package "pROC" and the R package "ggDCA", respectively.

[0089] 9. Detection of differential expression of SIAH2, GDF15 and PPARG using GEO dataset

[0090] The box plots of R package "ggpubr" were used to show the expression differences of SIAH2, GDF15 and PPARG between the control group and the OA group in bulk RNA data (GSE114007 and GSE169077). The "DotPlot" function in R package "Seurat" was used to analyze the expression differences of SIAH2, GDF15 and PPARG between cartilage of different OA grades in scRNA sequencing dataset (GSE104782) and between mildly damaged lateral tibial plateau cartilage and damaged medial tibial plateau cartilage in scRNA sequencing dataset (GSE152805).

[0091] 2. Analysis of SIAH2 by in vitro cell experiments

[0092] 1. Cell culture

[0093] (1) Subculturing of SW1353 cells

[0094] Human chondrocyte cell line SW1353 (CL-0447) was obtained from Wuhan Punosai Life Science Co., Ltd., China. When the cell coverage area in the T25 cell culture flask reached 80%, the original culture medium was replaced and 2 mL of PBS liquid was carefully injected. The cells were carefully shaken to wash without damaging the cell layer, and this step was repeated 3 times. Then 1 mL of 0.25% trypsin was added and digested for 1 min in a cell culture incubator with 5% CO2 and 37°C. After 70-80% cell morphology shrinkage was observed, the wall of the box was tapped to make the cells fall off from the side. Then 2 mL of culture medium was added to interrupt the digestion process. Use a pipette to blow gently to help the cells separate from the bottom, and check under a microscope to ensure that there are free single cells in the solution, and then transfer them to a 15 mL centrifuge tube. Centrifuge at 1000 rpm for 5 min at room temperature, discard the supernatant, and then resuspend the cells with 1 mL of complete culture medium and plate at a density of 6×10 5 / cm2 and subcultured into a new T25 cell culture flask. Observe the growth status of the cells under an inverted microscope, put the culture flask into the incubator, and wait for the cells to adhere to the wall and grow.

[0095] (2) Cryopreservation of SW1353 cells

[0096] When the cells in the T25 culture flask grow to 80%-90% confluence, first remove the original culture medium, and then use 2mL PBS to wash three times. Afterwards, add 1mL of 0.25% trypsin solution to separate the cells, and continue to observe their digestion. When the cell volume is observed to shrink into a round shape and reach 70%-80%, tapping the culture flask can cause the cells to fall off from the flat bottom, and quickly add 2mL of fresh culture medium to stop digestion. Then use a pipette to tap the cell suspension at the bottom of the T25 culture flask, collect it in a 15mL centrifuge tube, and centrifuge it at 1000rpm for 5min at room temperature. After centrifugation, remove the supernatant, resuspend the cells with a solution containing 1mL of serum, and then put them into a specific cell cryopreservation tube and store them in a -80℃ refrigerator.

[0097] (3) Recovery of SW1353 cells

[0098] Take out the cell cryopreservation tube from the -80℃ freezer and immediately put it into a heated water bath to thaw quickly, while shaking it constantly to melt the liquid in it as quickly as possible. After about 1 minute, the liquid in the test tube melted, and it was taken into the clean bench. The cell liquid was transferred into a 15mL centrifuge tube containing 6mL of complete culture medium using a pipette, and then centrifuged at 1000rpm for 5 minutes. Then discard the supernatant, and resuspend the cell clumps with 1mL of complete culture medium to make a cell suspension. The number of cells was counted using a cell counter according to 2×10 5 / cm2 were inoculated into a T25 cell culture flask, and the flask was sealed and mixed. 2 and incubated at 37°C in a cell culture incubator.

[0099] (4) Drug induction of SW1353 cells

[0100] Blank control group: no drug treatment. IL-1β group: 10 ng / mL IL-1β was added to SW1353 cells and incubated for 48 hours to establish the OA cell model.

[0101] 2. RNA Extraction

[0102] SW1353 cells were cultured at 2×10 5 The cells were seeded at a density of 100 μg / well in a 6-well plate and placed in a 37°C, 5% CO 2 The cells were incubated for 24 hours under the environment of 40 °C. Then, the corresponding intervention treatments were carried out for each group according to the experimental design: blank control group, IL-1β group. After a certain period of drug action, the culture medium was removed and the cells were washed with PBS twice. Next, RNA samples were extracted using the EZ-press RNApurification Kit (EZMED Yingze Biological Company, B0004D), and the specific steps followed the instructions for use of the product. 500 μL / well of lysis buffer was added to the 6-well plate and shaken at 150 rpm for 5 minutes on the shaking table to ensure that the cells were completely lysed. The next step was to inject 500 μL / well of anhydrous ethanol into each well and blow gently with a pipette until no precipitation was visible. Finally, the mixture was transferred to a centrifuge tube, left at 4000g for one minute at room temperature, the supernatant was removed, and then centrifuged at 12000g for 1 minute. Remove the supernatant, add 500 μL of elution buffer heated to 95°C to the centrifuge tube, let stand for 2 minutes at room temperature, centrifuge at 12000g for 1 minute, and place the collected filtrate on ice for subsequent detection of RNA content. Finally, store the sample in a -80°C refrigerator.

[0103] 3. Determination of RNA concentration

[0104] The NanoDrop2000 ultra-micro-volume UV spectrophotometer software was used to determine the purity and concentration of RNA. Before measurement, the sample loading window must be cleaned with distilled water. 1 μL of enzyme-free water was added to set the blank calibration to eliminate background interference. After that, the sample loading window was wiped again with dust-free paper and 1 μL of RNA sample was added for measurement. Record the measured concentration and related data. Ideally, the 260 / 280 ratio should be between 1.8 and 2.0, and the closer to 2.0, the higher the purity of the RNA.

[0105] 4. RNA Reverse Transcription

[0106] Use the Evo M-MLV reverse transcription premix (designed for qPCR) provided by AG. The specific operation steps follow the instructions in the product manual. Prepare the reverse transcription reaction mixture according to the instructions in Table 3. Mix the reaction solution on ice, followed by a brief low-speed centrifugation. Set the PCR reverse transcription reaction conditions as follows: the volume is 10 μL, the reaction temperature and time are set to 37°C and 15min; 85°C for 5s; then keep warm at 4°C. After the reaction is completed, store the obtained cDNA in a refrigerator at -20°C.

[0107] Table 2: Reagent components of the reverse transcription reaction system

[0108]

[0109]

[0110] 5. Real-time fluorescence quantitative PCR

[0111] Primers were designed based on the sequence information retrieved from the GenBank database and related literature, and the sequences of the synthesized primers are shown in Table 3. The SYBR Green Premix Pro Taq HS qPCR Kit from AG was used in the experiment, and the experiment was carried out in accordance with the guidance of the product manual. According to the components shown in Table 4, the PCR reaction mixture was prepared on ice, and the cDNA was diluted 10 times before adding it. The PCR reaction mixture was thoroughly mixed on ice and centrifuged at a low speed, and then added to the PCR reaction plate. The reaction volume of each reaction well was set to 20 μL. The PCR amplification program was set as follows: first, the temperature was raised to 95°C and maintained for 3s, then dropped to 60°C for 30s, and cycled 40 times in total. GAPDH was used as the internal reference gene, and the relative gene expression level was normalized by GAPDH, and 2 -ΔΔCt Methods The results were calculated with three technical replicates for each sample.

[0112] Table 3: Primer sequences

[0113]

[0114] Table 4: qRT-PCR reverse transcription reaction system

[0115] Reagents volume 2X SYBR Green Pro Taq HS Premix 10μL Primer F (10 μM) 0.4μL Primer R (10μM) 0.4μL Template(<100ng) 2μL ROX Reference Dye (4μM) 0.4μL RNase free water 6.8μL Total volume 20μL

[0116] 3. Clinical Samples and Cell and Animal Model Validation

[0117] 1. Clinical sample collection

[0118] Human articular cartilage samples were obtained from patients with knee OA (64-75 years old, female, Kellgren–Lawrence grade 4) who underwent knee replacement in Guangdong Provincial People's Hospital and were divided into non-damaged (lateral condyle or lateral plateau) and damaged (medial condyle or medial plateau) areas. Safranin fast green (S&F) staining and immunohistochemical staining for SIAH2 levels were performed on five pairs of cartilage samples. This clinical trial was approved by the Ethics Committee of Guangdong Provincial General Hospital (XJS2023-021-01).

[0119] 2. Animal model design

[0120] Six 6-week-old male C57BL / 6 (20-30g) were provided by Guangdong HUAWEI Testing Co., Ltd. The six mice were randomly divided into a control group and an MIA-induced mouse knee OA model group, with 3 mice in each group, and all were anesthetized by intraperitoneal injection of pentobarbital hydrochloride (35mg / kg). In the MIA group, each mouse received a single injection of MIA (0.6mg / 20μL saline) into the right knee joint cavity in the first week. In the control group, the same volume of 20μL saline was injected at the same injection time point as the MIA injection group. When the mice were 14 weeks old, the mice in both groups were euthanized at the same time. The knee joints were collected and fixed in 4% paraformaldehyde for the next experiment. This study was approved by the Ethics Committee of the Huawei Microbiology Testing Center (202307003) and complied with the ARRIVE guidelines. All mice had animal quality certification.

[0121] 3. Histological analysis

[0122] (1) Fixation, decalcification, embedding and sectioning of knee joint tissue

[0123] A. Fixation: Immerse the isolated human knee joint and mouse knee joint tissue in 4% paraformaldehyde, fix for 48 hours, and then rinse with running water.

[0124] B. Decalcification: The samples were decalcified using a 10% EDTA decalcification solution and kept on a shaker at 37°C for 8 weeks, during which the EDTA decalcification solution needed to be changed every two days. When a 1mL syringe can easily penetrate the bone cortex and produce a clear sense of emptying, decalcification is considered complete and proceed to the next step. Then, the cartilage samples were trimmed into appropriate sizes using surgical scissors and placed in a specimen fixing container, followed by washing under running water for 30 minutes.

[0125] C. Dehydration: Place the dehydration box into the hanging basket, and then dehydrate and melt the wax in a gradually increasing concentration manner, using 75% alcohol - 85% alcohol - 90% alcohol - 95% alcohol - anhydrous ethanol I - anhydrous ethanol II - alcohol benzene - xylene I - xylene II - 65℃ to melt paraffin I - 65℃ to melt paraffin II - 65℃ to melt paraffin III.

[0126] D. Embedding: First, place the melted wax in the embedding frame, and before it solidifies, place the knee joint tissue in it, ensuring that the sagittal plane of the knee joint is parallel to the embedding surface. After embedding, store it at room temperature for 20 minutes, wait for the wax block to completely solidify, and then cool it to -20℃ for stabilization.

[0127] E. Sectioning: Fix the wax block in a rotary microtome, trim it according to the thickness of 4μm / sheet until the joint area is exposed, and then cut it into sections continuously. Use a charged anti-stripping sheet to pick it up, flatten the section at 40℃ water temperature, and then place it in a 60℃ oven to dry.

[0128] (2) Specific steps of S&F staining:

[0129] A. Tissue fixation, decalcification, dehydration embedding, and paraffin sectioning are performed as described above. The sections are inserted into a slide rack and baked in a drying oven at 60°C for 2 hours.

[0130] B. Dewax the paraffin sections to water: soak the sections in two bottles of xylene for 20 minutes each, then put them in the first bottle of anhydrous ethanol for 15 minutes, and the second bottle of anhydrous ethanol for 5 minutes, then soak them in 95% alcohol for 10 minutes, 85% alcohol for 10 minutes, and 75% alcohol for 10 minutes, and finally rinse with distilled water.

[0131] C. Fast green staining: Immerse the sections in fast green stain for 5 to 10 minutes, wash off excess stain with tap water until the cartilage becomes colorless, soak in differentiation solution for a while, and rinse with tap water.

[0132] D. Safranin staining: The sections were immersed in safranin stain for 15-30 seconds and then rapidly dehydrated with three cylinders of anhydrous ethanol.

[0133] E. Dehydration and transparent sealing: Rapidly immerse in anhydrous ethanol for 5s, 2s, and 10s in sequence for dehydration, then transparentize in clean xylene for 5min, and finally seal with neutral gum and wait to dry.

[0134] F. Observation under an optical microscope and image acquisition under a digital pathology slide scanner showed that the articular cartilage was red or dark red, while the cartilage fibrous layer, subchondral bone and meniscus were blue-green. The OA Research Society International (OARSI) scoring system was used to evaluate the severity of cartilage degeneration in the OA model. The specific grading criteria are as follows

[0135] Normal cartilage surface, graded as 0; S&F absent but no structural changes, graded as 0.5;

[0136] A small amount of fibrosis but no cartilage loss is graded as 1; vertical cracks extend to the layer below the surface with some loss of the surface layer, graded as 2;

[0137] Vertical cracks / erosions into calcified cartilage extending to <25% of the articular surface are graded as 3;

[0138] Vertical cracks / erosions into calcified cartilage extending to 25-50% of the joint surface are graded as 4;

[0139] Vertical cracks / erosions into calcified cartilage extending to 50-75% of the joint surface are graded as 5;

[0140] Vertical cracks / erosions into calcified cartilage extending to >75% of the articular surface are graded as 6.

[0141] (3) Immunohistochemistry

[0142] A. Tissue fixation, decalcification, dehydration embedding, and paraffin sectioning are performed as described above. The sections are inserted into a slide rack and baked in a drying oven at 60°C for 2 hours.

[0143] B. Dewaxing as above steps.

[0144] C. Place the slices in a staining jar and wash them three times with PBS solution on a shaker, each time for 5 minutes.

[0145] D. Repair antigens with pepsin antigen repair solution (Sevier, China). Use a 100 μL pipette tip to absorb the antigen repair solution, drop it on the tissue surface, and incubate it in a 37°C incubator for 40 minutes. Place the slices in a staining jar and wash them three times on a shaker with PBS solution, each time for 5 minutes.

[0146] E. Take out the washed slices and shake off the excess water on the tissue slices, and remove the excess water around them through filter paper. Next, use an immunohistochemical pen to mark the tissue (make sure the ends are closed and avoid touching the tissue itself), then add an endogenous peroxidase inhibitor (3% hydrogen peroxide) and let it stand for 30 minutes in a dark environment. Put the slices in a staining jar and wash them three times with PBS solution on a shaker, each time for 5 minutes.

[0147] F. Add 3% BSA for blocking and incubate at room temperature for 1 hour.

[0148] G. Discard the blocking solution, add the prepared primary antibody SIAH2 (1:50, Proteintech, 12651-1-AP), cover the wet box, and place in a 4°C refrigerator overnight.

[0149] H. The next day, take the slides out of the incubation box and place them at room temperature for 30 minutes, then wash them three times in a stream of PBS, then add HRP-labeled secondary antibodies of specific animal species corresponding to the primary antibody to the histochemical circle and place them at room temperature for 1 hour. Place the sections in a staining jar and wash them three times with PBS solution, shaking on a shaker, each time for 5 minutes.

[0150] 1. After mixing the DAB stock solution with the diluent at a ratio of 1:20, apply the washed and dried sections to ensure that the DAB color mixture completely covers the tissue. Observe under a microscope and observe a brown-brown stained area, indicating positive expression. The color development process should be controlled within 2 minutes to avoid excessive color development. After color development is completed, immediately rinse the DAB color development solution on the slide with water, immerse the section in tap water to stop the color development reaction, and then rinse with running water for 2-5 minutes.

[0151] J. Place the tissue sections that have been stained in hematoxylin for 30 seconds to 3 minutes and rinse with running water.

[0152] K. Differentiation with differentiation solution, blueing solution turns blue. Cell nuclei are blue under microscope. Rinse with running water.

[0153] L. Gradient dehydration: Soak in 75% ethanol, 85% ethanol, anhydrous ethanol I, anhydrous ethanol II, n-butanol, and xylene for 5 min each, seal the slides with neutral gum and wait to dry.

[0154] 4. Cell culture

[0155] Passaging, recovery, subculturing and drug induction of SW1353 cells:

[0156] Blank control group: no drug treatment. VitK3 group: 50 μM VitK3 was added to SW1353 cells and incubated for 1 hour to target SIAH2.

[0157] 5. qRT-PCR

[0158] SW1353 cells were cultured at 2×10 5 The cells were inoculated into 6-well plates at a density of 1:1 / well, and the corresponding induction drugs were added according to the groups: blank control group and VitK3 group. After a certain period of drug action, RNA extraction, RNA concentration determination, RNA reverse transcription and real-time fluorescence quantitative PCR were performed. The specific steps were the same as those in the experiments related to "Second point: in vitro analysis of SIAH2". The synthesized primer sequences are shown in Table 5.

[0159] Table 5: Primer sequences

[0160]

[0161] 6. Western Blot

[0162] (1) SW1353 cell protein extraction

[0163] SW1353 cells were cultured at 3 × 10 5 / well in a 6-well plate, and the corresponding induction drugs were added according to the grouping: (1) blank control group, IL-1β group; (2) blank control group, VitK3 group. After treatment, the cell culture plate was placed on ice, the culture medium was aspirated, and the cells were washed with pre-cooled PBS, and each well was gently washed twice with 1 mL PBS.

[0164] Then add 100 μL of a mixture containing RIPA lysis buffer and PMSF protease inhibitor (1:100 ratio) to each well, and place the well plate on ice for 30 minutes to lyse the cells. After lysis, use a cell scraper to scrape all the cells in a single direction, collect them in a 1.5mL EP tube, and then centrifuge at 12000rpm and 4℃ for 20 minutes. After centrifugation, carefully aspirate the supernatant to avoid aspirating the precipitate, and place it in a new sterile 1.5mL EP tube. The resulting supernatant is the total cell protein solution. Reserve 2μL of protein sample and transfer it to a new EP tube for protein quantitative analysis. Add the remaining protein sample to a quarter of the sample volume of 5×SDS-PAGE protein loading buffer, boil it at 100℃ for 10 minutes, and finally store the sample in a -20℃ refrigerator for use.

[0165] (2) BCA determination of protein concentration

[0166] A. Use the Bio-Tech Enhanced BCA Assay Kit to determine the protein concentration. According to the instructions, add 0, 1, 2, 4, 8, 12, 16, and 20 μL of 0.5 mg / mL standard to the standard wells of the 96-well plate, and make up 20 μL of standard diluent to each well.

[0167] B. Add the reserved 2 μL protein sample to the sample wells of the 96-well plate, and then add 20 μL of standard diluent.

[0168] C. According to the number of samples, prepare an appropriate amount of BCA working solution by mixing BCA reagent A and B in a volume ratio of 50:1. Mix thoroughly and use immediately. Add 200 μL of BCA working solution to each well of the ELISA plate and incubate at 37°C for 30 minutes.

[0169] D. Use an enzyme-labeled instrument to record its absorbance at 562 nm, draw a standard curve with protein content as the horizontal axis and absorbance value as the vertical axis, and calculate the protein concentration.

[0170] (3) Gel configuration

[0171] Combine the gel-making racks together as a gel-making frame. The gel-making frame has two grooves, with a long and short glass plate embedded in the front and back. After installation and alignment, inject water into the gap between the glass plates until the water level is almost the same height as the glass plate, then keep it still and check whether there is leakage. If there is no leakage, suck out the water. Select appropriate separation gel and concentrated gel (10%, 12.5%) to match the relative molecular mass of the target protein. The prepared separation gel should be quickly added to the glass plate with a pipette, and the gel should be gently injected from left to right along the upper edge of the glass plate with a 1mL gun tip to avoid uneven gel concentration and avoid bubbles. Then add anhydrous ethanol from left to right to make the liquid surface above the separation gel flat, wait for 15 minutes, and when a clear interface appears between the gel and the water layer, it means that the separation gel has been configured, and pour out the anhydrous ethanol. As above, use a 1mL gun tip to gently add concentrated gel from left to right, then insert a 10-hole comb and let it stand for solidification. After preparing the gel, place it in a box of suitable size, add an appropriate amount of electrophoresis solution to soak it, and store it in a refrigerator at 4°C.

[0172] (4) Electrophoresis

[0173] Select a suitable ratio of separation gel according to the molecular weight of the protein, place it in the tank of the electrophoresis instrument and pour in the electrophoresis buffer solution, pull out the comb vertically, add the protein sample and marker in sequence according to the grouping order, and ensure that the protein content of each well is 20μg. Plug in the positive and negative electrodes, set to 150V, and the electrophoresis time is 50min. When the marker is clearly separated and a blue buffer strip appears at the bottom of the gel, the electrophoresis is completed.

[0174] (5) Transfer

[0175] After completing the electrophoresis operation, take out the glass slide from the container and slightly separate it. According to the position of the marker and the size of the target protein, cut the gel part on the glass slide and move it onto the filter paper. After eliminating the bubbles, use a PVDF membrane that has been pre-activated with methanol for 1 minute to cover the gel layer. Next, place the filter pad, filter paper, gel layer, PVDF membrane, filter paper and filter pad in order to form a "sandwich"-like sponge device. After firmly fixing this device, put it in the transfer tank with the black side facing the black area in the tank and the white side facing the red area, and add an appropriate amount of transfer solution. Determine the transfer conditions according to the molecular weight of the protein.

[0176] (6) Closed

[0177] After the transfer process was completed, the PVDF membrane was immersed in blocking solution (10×) and blocked on a shaker at room temperature for 1-2 h.

[0178] (7) Incubation with secondary antibody

[0179] The primary antibody was recovered and washed three times on a TBST shaker, each time for 5 min. Then, the secondary antibody (1:2000) bound to it was added for incubation for 1 h, the secondary antibody was recovered, and the wash was performed three times on a TBST shaker, each time for 5 min.

[0180] (8) Development

[0181] Prepare ECL developing solution A and solution B in a 1:1 ratio and mix them evenly before use. Then drop them onto the surface of the PVDF film, wait for 2 minutes, and then put them into the chemiluminescence imaging analysis device for detection, and finally obtain the experimental data. At the same time, save the image so that the gray value of the band can be analyzed using Image J software.

[0182] 7. Immunofluorescence

[0183] SW1353 cells were cultured at 2×10 5 The cells were seeded at a density of 1 mL / well in a 24-well plate, and different induction drugs were added according to the grouping: blank control group, IL-1β group. After a period of time, the old culture medium was discarded and the well plate was washed twice with 1 mL / well of PBS. The cells were then fixed with 1 mL / well of 4% paraformaldehyde for 15 minutes, the formaldehyde was removed and washed three times with PBS at a low speed on a shaker for 5 minutes each time. 1 mL / well of 0.5% Triton X-100 was added for treatment for 10 minutes, and then washed three times with PBS at a low speed for 5 minutes each time. 1 mL / well of immunostaining blocking solution was added, blocked for 1 hour, and after discarding the blocking solution, the diluted primary antibody SIAH2 (1:50, Proteintech, 12651-1-AP) was added and incubated for 18 hours. The primary antibody was removed, washed three times with PBST at a low speed on a shaker for 5 minutes each time, and then added. 488-labeled secondary antibody (1:200) was used for 1 h. After removing the secondary antibody, the cells were washed three times with PBST on a shaker for 5 min each time. Hoechst33258 was added to stain the cell nucleus for 15 min. The slides in the well plate were removed and inverted on a glass slide, and the cells were imaged using a confocal fluorescence microscope (Leica Microsystems, Wetzlar, Germany). Finally, the fluorescence intensity was quantified by ImageJ software, and the average optical density (AOD) method was used to quantify protein expression.

[0184] 8. CCK-8

[0185] SW1353 cells grown to 80% were digested with trypsin containing 0.25% EDTA, and then resuspended after centrifugation. Next, the number was determined using a cell counting plate, and 1000 cells were placed in each well and inoculated into a 96-well plate. After 24 hours of culture, different concentrations of 50μM VitK3 were added for intervention, and the treatment time was 0, 15, 30, 60 and 120min, respectively. After the treatment, the culture medium was aspirated and rinsed twice with an equal volume of PBS. 10μL CCK-8 solution and 90μL serum-containing culture medium were added to each well and incubated at 37°C in the dark for 2h. During the period, avoid the generation of bubbles when adding CCK-8 solution. Use an enzyme reader to detect the absorbance value (OD value) at a wavelength of 450nm and record it.

[0186] 9. Statistical analysis

[0187] Statistical analysis was performed using SPSS software (version 25.0) and GraphPad Prism software (version 9.0). Quantitative data represent at least three independent experiments. No samples were excluded during the analysis. The Shapiro-Wilk test and Levene's method were used to estimate the normal distribution and homogeneity of variance of the data, respectively. Two-independent sample t-test was selected for the analysis of differences between two independent sample groups. One-way or two-way analysis of variance (ANOVA) followed by Tukey post hoc test was used to assess the statistical significance of means exceeding two groups. Data are expressed as mean ± standard deviation. The statistical significance level was set at P < 0.05.

[0188] 4. Result verification:

[0189] 1. Data processing and identification of DEGs and OA-related module genes

[0190] DEGs were identified using the GSE114007 dataset, which included 18 control groups and 20 OA cartilage tissues. The PCA analysis results of the two platforms of GSE114007 after batch removal showed that the samples of the control group and the OA group were distributed in relatively independent quadrants, indicating that the batch effect caused by different platforms was successfully eliminated ( Figure 2 A- Figure 2 B). DEGs between the control group and the OA group were identified by using the R package “DESeq2”. According to the preset filtering criteria, a total of 2351 DEGs ( Figure 2 C).

[0191] WGCNA analysis was performed using the expression profile of GSE114007. The clustering tree diagram showed that there were no obvious outliers in all samples ( Figure 2D in ). Before constructing the gene co-expression network, the soft threshold, β value, was calculated. The scale-free topological threshold of the network was 0.9. When the soft threshold power was 16, the average connectivity was close to 0. Therefore, β = 16 was selected to construct the hierarchical clustering tree ( Figure 2 E in Figure 3). Nine modules were identified based on the average hierarchical clustering and dynamic shear tree, and the distribution of these modules was displayed by cluster dendrogram ( Figure 2 The heat map of the correlation between modules and clinical traits showed that the green (r = -0.83, p = 1e-10) and cyan (r = -0.7, p = 8e-07) modules were highly negatively correlated with OA, and the black (r = 0.85, p = 1e-11) and blue (r = 0.89, p = 4e-14) modules were highly positively correlated with OA ( Figure 2 G in the above four modules), indicating that the genes in the above four modules may play an important role in OA. Finally, 3433 OA-related module genes in the above four modules were screened for subsequent research.

[0192] 2. Enrichment analysis showed that cell death-related functions were significantly enriched in DCDEGs

[0193] By intersecting the DEGs in GSE114007 with the OA-related module genes, 571 OA-related module DEGs were obtained ( Figure 3 After the intersection of cell death-related genes and OA-related module DEGs, 26 overlapping genes were obtained as DCDEGs, as shown in the Upset figure ( Figure 3 B), including 9 genes related to ferroptosis (MYB, ALOX5, SAT1, MT1G, PARP15, GDF15, NOS2, PPARG, SIAH2), 4 genes related to apoptosis (GADD45B, FOS, SPTA1, CLSPN), 7 genes related to pyroptosis (TRIM31, APOE, KIF23, MPEG1, SEZ6L2, PPARG, PCSK9), and 8 genes related to necroptosis (ARC, IGF1, RIPK3, PPARG, NAT2, TNFRSF1B, CAMK2A, NOD2). The box plot of the differential expression of DCDEGs is shown in Figure 3 The correlation heat map results show that there is a strong correlation between most DCDEGs ( Figure 3 D).

[0194] To further understand the biological functions of these 26 DCDEGs identified in OA, the molecular interaction network created by the GeneMANIA online program showed that DCDEGs were significantly enriched in "positive regulation of lipid metabolism", "regulation of inflammatory response", "response to oxidative stress" and "positive regulation of apoptosis" ( Figure 3 E in ). GO enrichment analysis results showed that DCDEGs were mainly enriched in biological processes such as inflammatory response regulation, response to oxidative stress, positive regulation of lipid metabolism, and response to reactive oxygen species ( Figure 3 F in Figure 3). Proteomaps analysis showed that DCDEGs were enriched in metabolic pathways such as biosynthesis, immune system, and lipid and steroid metabolism ( Figure 3 G in the figure). These results indicate that DCDEGs are closely related to each other and that DCDEGs are involved in biological functions such as oxidative stress, reactive oxygen species, lipid metabolism, and immune inflammation. Among them, the reactive oxygen species and oxidative stress functions significantly enriched in DCDEGs are closely related to ferroptosis.

[0195] 3. ScRNA-seq data revealed that ferroptosis-related genes have a higher correlation with the severity of OA lesions

[0196] 1600 chondrocytes were obtained by analyzing the ScRNA-seq dataset (GSE104782). Quality control showed that a total of 1464 chondrocytes were suitable for subsequent analysis. Based on UMAP analysis, the cell distribution of five OA cartilage lesion stages was shown ( Figure 4 A in Figure ). The heat map shows the expression of 26 DCDEGs in five OA cartilage lesion stages ( Figure 4 (B). The pseudo-time analysis model reflects the dynamic gene expression changes between chondrocytes at different pathological stages under histology. The results show that the more severe the cartilage pathological stage of the chondrocytes, the longer the pseudo-time allocated to the chondrocytes ( Figure 4 C to D in the figure, which shows the reliability of the sample. The results of calculating the expression levels of each cell death mode by the "AddModuleScore" function in the R package "Seurat" showed that the scores related to ferroptosis genes and apoptosis were higher in chondrocytes than those related to pyroptosis and necroptosis. In addition, unlike apoptosis, pyroptosis and necroptosis, the ferroptosis genes in S0 and S4 stages were significantly different from those in other lesion stages ( Figure 4 These results suggest that ferroptosis-related genes are more closely related to the severity of OA cartilage lesions.

[0197] 4. Machine learning algorithms screen biomarkers related to cell death

[0198] A machine learning algorithm was used to screen biomarkers of ferroptosis and other death modes in DCDEGs. Among the ferroptosis-related genes, four OA characteristic genes were screened using the LASSO algorithm ( Figure 5 A~B in the figure). According to the SVM-RFE feature selection, 8 OA characteristic genes were determined ( Figure 5 C in ). Random forest is used to identify feature importance and the top 5 genes are selected as feature genes ( Figure 5 The characteristic genes obtained from SVM-RFE, LASSO and RF algorithms were intersected to obtain four ferroptosis genes. Among these four genes, PARP15 was excluded because it was not detected in the subsequent validation set. Finally, three genes were obtained as ferroptosis-related biomarkers (SIAH2, GDF15 and PPARG) ( Figure 5 F in ). At the same time, four apoptosis-related biomarkers (GADD45B, FOS, SPTA1 and CLSPN) were obtained by the same algorithm ( Figure 5 G in the figure), 3 pyroptosis-related biomarkers (KIF23, SEZ6L2 and TRIM31. PCSK9 was not detected in the validation set and was therefore excluded) Figure 5 H) and three necroptosis-related biomarkers (CAMK2A, NAT2 and NOD2. RIPK3 was not detected in the validation set and was therefore excluded) ( Figure 5 The above biomarkers will be used for subsequent analysis.

[0199] 5. Ferroptosis biomarker model has the highest diagnostic efficacy for OA

[0200] After constructing corresponding logistic regression models using biomarkers of cell death modes, the performance of the models was evaluated by ROC and DCA. In the training set (GSE114007), ROC analysis showed that the AUC values ​​of the four cell death mode models were all 1 ( Figure 6 The DCA showed that these models also showed the same high clinical benefit. In order to confirm their clinical utility, the above models were further tested in the validation set. In the OA cartilage validation set (GSE57218-GSE169077), ROC analysis showed that the AUC values ​​of ferroptosis, apoptosis, pyroptosis and necroptosis models were 0.893, 0.684, 0.746 and 0.801, respectively. Figure 6The AUC value of the ferroptosis model was greater than 0.75, which was higher than other cell death modes and had the highest diagnostic efficacy. In the OA synovial validation set (GSE46750-GSE55235), ROC analysis showed that the AUC values ​​of the ferroptosis, apoptosis, pyroptosis, and necroptosis models were 0.841, 0.808, 0.721, and 0.729, respectively ( Figure 7 In the OA subchondral bone validation set (GSE51588), ROC analysis showed that the AUC values ​​of ferroptosis, apoptosis, pyroptosis, and necroptosis models were 1, 0.938, 0.797, and 0.93, respectively ( Figure 7 The synovial validation set and subchondral bone validation set also show that the ferroptosis model has better diagnostic efficacy than other cell death models. The ferroptosis model also has good benefits in the above validation set DCA.

[0201] In summary, among various death modes, the ferroptosis biomarker model has the highest diagnostic efficacy for OA, and three ferroptosis biomarkers (SIAH2, GDF15 and PPARG) were further analyzed. It is worth noting that in the data sets (GSE114007, GSE57218-GSE169077, GSE46750-GSE55235, GSE51588), ROC analysis showed that the AUC values ​​of the ferroptosis biomarker SIAH2 were 1, 0.694, 0.835 and 0.992, respectively, which were basically higher than the AUC values ​​of the other two ferroptosis biomarkers GDF15 and PPARG, indicating that SIAH2 has an outstanding high diagnostic efficacy among OA ferroptosis biomarkers.

[0202] 6. Detection of SIAH2, GDF15, and PPARG expression using GEO database

[0203] In the bulk RNA-seq cartilage training set (GSE114007), SIAH2 and GDF15 were significantly downregulated in OA, while PPARG was significantly upregulated in OA ( Figure 8 In the bulk RNA-seq cartilage validation set (GSE169077), SIAH2, GDF15, and PPARG were consistent with the above trend ( Figure 8 B). The ScRNA-seq cartilage dataset (GSE104782) showed that as the cartilage pathology stage worsened, the expression of SIAH2 and GDF15 gradually decreased, while PPARG gradually increased ( Figure 8C). The ScRNA-seq cartilage dataset (GSE152805) showed that the expression of SIAH2 and GDF15 was decreased, while the expression of PPARG was increased in the injured medial tibial plateau cartilage compared with the mildly injured lateral tibial plateau cartilage ( Figure 8 D).

[0204] 7. In vitro experiments preliminarily verified the expression of SIAH2, GDF15, and PPARG

[0205] After IL-1β was added to induce the OA model, the relative expression levels of SIAH2 and GDF15 in SW1353 cells were significantly decreased, and the expression level of PPARG was also significantly decreased (n=10) ( Fig. 9 AC in the control group and the IL-1β-treated group, the diagnostic efficacy of SIAH2 (AUC = 1) was higher than that of GDF15 (AUC = 0.83) and PPARG (AUC = 0.9) ( Fig. 9 D), with high sensitivity and specificity.

[0206] 8. SIAH2 expression is significantly downregulated in OA models

[0207] S&F staining of human KOA showed that the cartilage in the damaged area showed significant degeneration. Immunohistochemical staining showed that the level of SIAH2 in the cartilage in the damaged area was significantly reduced ( Fig.10 AC in the figure), the diagnostic results for SIAH2 are as follows Fig.10 As shown in D. MIA was used to induce a mouse knee OA model. S&F of mouse KOA showed that the articular cartilage showed obvious wear and degeneration after MIA injection. Immunohistochemical staining showed that the expression of SIAH2 protein in the mouse articular cartilage was significantly reduced after MIA injection ( Fig.10 Western Blot( Fig.10 H and I) and immunofluorescence ( Fig.10 The results of J and K in Figure 2 showed that the relative protein expression level of SIAH2 was significantly decreased after IL-1β stimulation of chondrocytes. The above results were statistically significant (P<0.05). In summary, the expression of SIAH2 was significantly downregulated in the OA model.

[0208] 9. Inhibition of SIAH2 causes dysregulation of chondrocyte ECM metabolism-related molecules and GPX4

[0209] The results of CCK-8 experiments showed that VitK3 inhibited the proliferation of chondrocytes in vitro ( Fig.11 A in Figure 1). qRT-PCR and WB experiments showed that VitK3 had a significant inhibitory efficiency on SIAH2 ( Fig.11BD in the figure). At the mRNA level, after using VitK3 to inhibit SIAH2, the expression of chondrogenic factors SRY-Box transcription factor 9 (SOX9), type II collagen α1 (COL2A1), and GPX4 was significantly reduced, and the expression of matrix metalloproteinase 13 (MMP13) was significantly increased. At the protein level, VitK3 also downregulated the expression of SOX9, MMP13, COL2A1, and GPX4 after inhibiting SIAH2 ( Fig.11 The results were statistically significant (P<0.05). The above findings indicate that SIAH2 regulates the expression of ECM metabolism-related molecules and GPX4 in chondrocytes.

Claims

1. A nucleic acid combination product, characterized in that: The nucleic acid combination product includes a reagent for detecting the expression level of the SIAH2 gene.

2. The nucleic acid combination product according to claim 1, characterized in that: The reagent for detecting the expression level of the SIAH2 gene includes detection primers as shown in SEQ ID NO.1 to SEQ ID NO.

2.

3. A diagnostic kit for osteoarthritis, characterized in that: The kit comprises the nucleic acid combination product according to any one of claims 1 to 2.

4. The kit according to claim 3, characterized in that The osteoarthritis is primary osteoarthritis or secondary osteoarthritis.

5. The kit according to claim 3, characterized in that The kit also includes one or more of a negative control, a positive control and a quality control product.

6. The kit according to claim 3, characterized in that The kit further comprises: one or more of a nucleic acid releasing reagent, a nucleic acid extracting reagent, and a nucleic acid amplifying reagent.

7. The kit according to claim 5, characterized in that The nucleic acid amplification reagent includes DNA polymerase, dNTP, UNG enzyme, PCR buffer and Mg 2+ One or more of .

8. Use of the nucleic acid combination product according to any one of claims 1 to 2 or the kit according to any one of claims 3 to 7 in the preparation of a product for diagnosing osteoarthritis.

9. Use of a SIAH2 gene expression promoter, SIAH2 gene mRNA and SIAH2 protein in the preparation of drugs for the treatment of osteoarthritis.

10. A drug for treating osteoarthritis, comprising an agent for promoting the expression of gene SIAH2, a transcription product of gene SIAH2 or an expression product of gene SIAH2, and pharmaceutically acceptable excipients.

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