Method, equipment and device for identifying osteoarthritis characteristic gene by using ADCC (Adenosine Density Cellulose), and storage medium

Abstract

The invention discloses a method, equipment and device for identifying osteoarthritis characteristic genes by using ADCC related genes and a storage medium, belongs to the technical field of medical treatment, and solves the problem that the osteoarthritis characteristic genes cannot be constructed from the aspect of ADCC related genes in the prior art. A method for identifying osteoarthritis characteristic genes by using ADCC comprises the following steps: step S1, retrieving in a database to obtain ADCC related genes, identifying data in the database by using R software to obtain significantly differential genes, and screening out module genes related to osteoarthritis in the database according to a WGCNA network; s2, performing intersection operation on the significantly differential genes, module genes related to osteoarthritis and ADCC related genes to obtain intersection genes; s3, performing protein interaction network construction on the intersection genes by utilizing an STRING database, and obtaining key differential genes; and S4, combining the key differential genes obtained in the step S3 with a machine learning method to obtain feature genes. According to the invention, the effect of ADCC related genes in OA pathogenesis is analyzed.

Description

Technical Field

[0001] The present invention belongs to the field of medical technology, and in particular relates to a method, equipment, device and storage medium for identifying osteoarthritis characteristic genes by using ADCC. Background Art

[0002] Osteoarthritis (OA) is a highly prevalent, age-dependent joint disease. Due to the unprecedented aging of the global population, OA remains a major cause of disability, and treatment is often costly, resulting in a huge economic burden worldwide. However, as the etiology and pathological mechanisms remain unclear, there are currently limited clinical approaches to delay or reverse cartilage matrix degeneration. Antibody-dependent cellular cytotoxicity (ADCC) embodies a cellular immune response in which immune cells stimulate cell death through the binding of specific antibodies to the cell membrane. It is a key cytotoxic mechanism that is primarily mediated by natural killer (NK) cells and is activated when the Fc region of an antibody binds to Fcγ receptors (FcγRs) on effector cells, while the antibody binds to tumor antigens through the Fab segment of the target cell. This dual interaction mechanism coordinates the complex process of ADCC initiation. ADCC is the primary mechanism of action for many antibody-based therapeutic molecules and plays a role in mediating the clinical benefit of widely used cytotoxic monoclonal antibodies. ADCC is the primary mechanism of action for many antibody-based therapeutic molecules, and its therapeutic effects have been tested in preclinical models and validated in patients. However, the complexity of ADCC in OA remains unclear, and there is a lack of in-depth research on how ADCC-related genes regulate immune cells to cause the occurrence and progression of osteoarthritis. Summary of the invention

[0003] The purpose of the present invention is to provide a method that solves the existing problem of lack of constructing osteoarthritis characteristic genes from the perspective of ADCC-related genes.

[0004] In order to achieve the above object, the technical solution of the present invention is implemented as follows: a method for identifying osteoarthritis characteristic genes using ADCC, comprising:

[0005] Step S1, searching for ADCC-related genes in a database, using R software to identify data in the database to obtain significantly differentially expressed genes, and screening out module genes related to osteoarthritis in the database according to the WGCNA network;

[0006] Step S2, performing an intersection operation on the significantly differentially expressed genes, the osteoarthritis-related module genes, and the ADCC-related genes to obtain intersection genes;

[0007] Step S3, constructing a protein interaction network for the intersection genes using the STRING database, and obtaining key differentially expressed genes;

[0008] Step S4: combining the key differential genes obtained in step S3 with a machine learning method to obtain characteristic genes.

[0009] In some embodiments, step S1 specifically includes:

[0010] Step S11, downloading a data set from a database, processing the data set to obtain a training set and a validation set, and retrieving ADCC-related genes from the database;

[0011] Step S12, using R software to identify the data of the training set to obtain significantly differentially expressed genes;

[0012] Step S13, performing enrichment analysis on the significantly differentially expressed genes, and constructing a WGCNA network using the training set as the phenotype to screen out module genes associated with osteoarthritis.

[0013] In some embodiments, the step S3 further requires performing a functional enrichment analysis on the intersection genes before constructing a protein interaction network for the intersection genes using the STRING database.

[0014] In some embodiments, the specific method of step S3 is: using the four topological analysis algorithms MCC, MNC, Degree, and EPC in cytoHubba to predict the protein interaction network, and obtaining the top 30 intersection genes as key differential genes.

[0015] In some embodiments, step S4 further comprises combining the expression values ​​of the key differential genes in all samples with the sample grouping to perform LASSO logistic regression, SVM-RFE feature screening, random forest model construction and obtain characteristic genes.

[0016] In some embodiments, step S4 further includes taking the intersection of the characteristic genes to obtain key characteristic genes.

[0017] In some embodiments, step S4 further comprises extracting the expression levels of the key characteristic genes from the training set and the validation set, respectively, and performing inter-group difference analysis and ROC validation on each set.

[0018] Another technical solution of the present invention is achieved as follows: a device for identifying osteoarthritis characteristic genes using ADCC, comprising:

[0019] Data processing module: retrieve ADCC-related genes from the database, use R software to identify the data in the database to obtain significantly differentially expressed genes, and screen out module genes related to osteoarthritis from the database according to the WGCNA network;

[0020] Intersection gene processing module: performing intersection operation on the significantly different genes, the module genes related to osteoarthritis and the ADCC-related genes to obtain intersection genes;

[0021] Key differentially expressed genes processing module: using the STRING database to construct a protein interaction network for the intersection genes and obtain key differentially expressed genes;

[0022] Characteristic gene processing module: combining the key differential genes with machine learning methods to obtain characteristic genes.

[0023] Another technical solution of the present invention is implemented as follows: a device for identifying osteoarthritis characteristic genes using ADCC, the device for identifying osteoarthritis characteristic genes using ADCC: a memory, a processor, and a program for identifying osteoarthritis characteristic genes using ADCC stored in the memory and executable on the processor, the program for identifying osteoarthritis characteristic genes using ADCC being configured to implement a method for identifying osteoarthritis characteristic genes using ADCC.

[0024] Another technical solution of the present invention is implemented as follows: a computer-readable storage medium storing a computer program, which can implement the steps in the method of identifying osteoarthritis characteristic genes using ADCC when executed by a processor.

[0025] Compared with the prior art, the present invention screens out characteristic genes based on the database by analyzing the module genes, WGCNA and ADCC genes related to osteoarthritis, analyzes the role of ADCC-related genes in the pathogenesis of OA, and provides potential diagnostic markers and therapeutic targets for the treatment of OA. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 A schematic diagram of the structure of a device for identifying osteoarthritis characteristic genes using ADCC provided in an embodiment of the present invention;

[0027] Figure 2 A flow chart of a method for identifying osteoarthritis characteristic genes using ADCC provided in the first embodiment of the present invention;

[0028] Figure 3 A flow chart of a method for identifying osteoarthritis characteristic genes using ADCC provided in a second embodiment of the present invention;

[0029] Figure 4a is a boxplot diagram of the method for identifying osteoarthritis characteristic genes using ADCC before batch removal provided by the second embodiment of the present invention;

[0030] Figure 4 b is a boxplot diagram after batch removal of the method for identifying osteoarthritis characteristic genes using ADCC provided in the second embodiment of the present invention;

[0031] Figure 4 c is a Umap diagram before batch removal for the method for identifying osteoarthritis characteristic genes using ADCC provided in the second embodiment of the present invention;

[0032] Figure 4 d is a Umap diagram after batch removal of the method for identifying osteoarthritis characteristic genes using ADCC provided in the second embodiment of the present invention;

[0033] Figure 5 a is a differential gene volcano plot of the method for identifying osteoarthritis characteristic genes using ADCC provided in the second embodiment of the present invention;

[0034] Figure 5 b is a differential gene heat map of the method for identifying osteoarthritis characteristic genes using ADCC provided in the second embodiment of the present invention;

[0035] Figure 6 a is a bubble chart of KEGG function enrichment results of the method for identifying osteoarthritis characteristic genes using ADCC provided in the second embodiment of the present invention;

[0036] Figure 6 b is a bar graph of GO enrichment results of the method for identifying osteoarthritis characteristic genes using ADCC provided in the second embodiment of the present invention;

[0037] Figure 7 a is a diagram for selecting the best softPower value for the method for identifying osteoarthritis characteristic genes using ADCC provided by the second embodiment of the present invention;

[0038] Figure 7 b is a gene set merge diagram of the method for identifying osteoarthritis characteristic genes using ADCC provided by the second embodiment of the present invention;

[0039] Figure 7 c is a visualized gene network diagram of the method for identifying osteoarthritis characteristic genes using ADCC provided in the second embodiment of the present invention;

[0040] Figure 7 d is a heat map of the correlation between genes and traits in the module of the method for identifying osteoarthritis characteristic genes using ADCC provided in the second embodiment of the present invention;

[0041] Figure 8a is an intersection gene graph of a method for identifying osteoarthritis characteristic genes using ADCC provided in a second embodiment of the present invention;

[0042] Figure 8 b is a KEGG diagram of intersection genes of the method for identifying osteoarthritis characteristic genes using ADCC provided in the second embodiment of the present invention;

[0043] Figure 8 c is a GO functional network diagram of the method for identifying osteoarthritis characteristic genes using ADCC provided in the second embodiment of the present invention;

[0044] Figure 8 d is a KEGG dendrogram of intersection genes of the method for identifying osteoarthritis characteristic genes using ADCC provided in the second embodiment of the present invention;

[0045] Figure 8 e is a GO function dendrogram of the method for identifying osteoarthritis characteristic genes using ADCC provided in the second embodiment of the present invention;

[0046] Fig. 9 a is a diagram showing the interaction relationship of the method for identifying osteoarthritis characteristic genes using ADCC provided in the second embodiment of the present invention;

[0047] Fig. 9 b is an intersection gene network diagram of the method for identifying osteoarthritis characteristic genes using ADCC provided by the second embodiment of the present invention;

[0048] Fig.10 a is a graph showing the coefficient changes of variables in the method for identifying osteoarthritis characteristic genes using ADCC as lambda changes, provided in the second embodiment of the present invention;

[0049] Fig.10 b is a diagram showing genes and their coefficients corresponding to the method for identifying osteoarthritis characteristic genes using ADCC provided by the second embodiment of the present invention;

[0050] Fig.11 a is a graph showing the error rate of a support vector machine model for the method of identifying osteoarthritis characteristic genes using ADCC provided in the second embodiment of the present invention;

[0051] Fig.11 b is a graph showing the accuracy of a support vector machine model for the method of identifying osteoarthritis characteristic genes using ADCC provided in the second embodiment of the present invention;

[0052] Fig.12 a is an error rate diagram of random forest analysis of the method for identifying osteoarthritis characteristic genes using ADCC provided in the second embodiment of the present invention;

[0053] Fig.12b is a variable diagram of random forest analysis of the method for identifying osteoarthritis characteristic genes using ADCC provided by the second embodiment of the present invention;

[0054] Fig.13 A Venn diagram of the intersection of characteristic genes obtained by three machine learning algorithms in the method for identifying characteristic genes of osteoarthritis using ADCC provided in the second embodiment of the present invention;

[0055] Fig.14 a is a box plot of characteristic gene expression in a training set of a method for identifying characteristic genes of osteoarthritis using ADCC provided in a second embodiment of the present invention;

[0056] Fig.14 b is a box plot of characteristic gene expression of a validation set of the method for identifying characteristic genes of osteoarthritis using ADCC provided in the second embodiment of the present invention;

[0057] Fig.14 c is a ROC analysis diagram of a training set of a method for identifying osteoarthritis characteristic genes using ADCC provided in a second embodiment of the present invention;

[0058] Fig.14 d is a ROC analysis diagram of a validation set of a method for identifying osteoarthritis characteristic genes using ADCC provided in the second embodiment of the present invention;

[0059] Fig.15 a is a box plot showing the difference between the healthy and OA groups in the immune cells of the method for identifying osteoarthritis characteristic genes using ADCC provided in the second embodiment of the present invention;

[0060] Fig.15 b is a heat map showing the correlation between characteristic genes and differential immune infiltration in the method for identifying characteristic genes of osteoarthritis using ADCC provided in the second embodiment of the present invention;

[0061] Fig.15 c is a lollipop graph showing the correlation between a single gene and differential immune cells in the method for identifying osteoarthritis characteristic genes using ADCC provided in the second embodiment of the present invention;

[0062] Fig.16 a is a box plot showing the difference between the healthy and OA groups in the immune cells of the method for identifying osteoarthritis characteristic genes using ADCC provided in the second embodiment of the present invention;

[0063] Fig.16 b is a heat map showing the correlation between characteristic genes and differential immune infiltration in the method for identifying characteristic genes of osteoarthritis using ADCC provided in the second embodiment of the present invention;

[0064] Fig.16 c is a lollipop diagram showing the correlation between EGFR gene and differential immune cells in the method for identifying osteoarthritis characteristic genes using ADCC provided in the second embodiment of the present invention;

[0065] Fig.16 d is a lollipop diagram of the correlation between the CX3CR1 gene and differential immune cells in the method for identifying osteoarthritis characteristic genes using ADCC provided in the second embodiment of the present invention;

[0066] Fig.16 e is a lollipop diagram showing the correlation between the TNFSF11 gene and differential immune cells in the method for identifying osteoarthritis characteristic genes using ADCC provided in the second embodiment of the present invention;

[0067] Fig.17 a is a Nomogram diagram of a method for identifying osteoarthritis characteristic genes using ADCC to construct a diagnostic marker model for osteoarthritis provided in the second embodiment of the present invention;

[0068] Fig.17 b is a calibration curve diagram of the method for identifying osteoarthritis characteristic genes using ADCC provided in the second embodiment of the present invention;

[0069] Fig.17 c is a DCA curve diagram of the method for identifying osteoarthritis characteristic genes using ADCC provided in the second embodiment of the present invention;

[0070] Fig.17 d is a ROC curve diagram of the nomogram training set of the method for identifying osteoarthritis characteristic genes using ADCC provided in the second embodiment of the present invention;

[0071] Fig.17 e is a ROC curve diagram of the validation set nomogram of the method for identifying osteoarthritis characteristic genes using ADCC provided in the second embodiment of the present invention;

[0072] Fig.18 a is a graph showing the single gene GSEA enrichment results of EGFR in the method for identifying osteoarthritis characteristic genes using ADCC provided in the second embodiment of the present invention;

[0073] Fig.18 b is a graph showing the single gene GSEA enrichment results of CX3CR1 in the method for identifying osteoarthritis characteristic genes using ADCC provided in the second embodiment of the present invention;

[0074] Fig.18 c is a graph showing the single gene GSEA enrichment results of TNFSF11 in the method for identifying osteoarthritis characteristic genes using ADCC provided in the second embodiment of the present invention;

[0075] Fig.19 a is a molecular docking result diagram of the method for identifying osteoarthritis characteristic genes using ADCC provided in the second embodiment of the present invention;

[0076] Fig.19b is a diagram showing the binding mode between target protein and drug small molecule in the method for identifying osteoarthritis characteristic genes using ADCC provided in the second embodiment of the present invention;

[0077] Fig. 20 a is a consensus CDF diagram of the method for identifying osteoarthritis characteristic genes using ADCC provided in the second embodiment of the present invention;

[0078] Fig. 20 b is a DeltaArea diagram of the method for identifying osteoarthritis characteristic genes using ADCC provided in the second embodiment of the present invention;

[0079] Fig. 20 c is a subgroup clustering heat map of the method for identifying osteoarthritis characteristic genes using ADCC provided in the second embodiment of the present invention;

[0080] Fig. 20 d is a PCA diagram of a method for identifying osteoarthritis characteristic genes using ADCC provided in the second embodiment of the present invention;

[0081] Fig.21 a is a sample quality control box diagram of the method for identifying osteoarthritis characteristic genes using ADCC provided in the second embodiment of the present invention;

[0082] Fig.21 b is a scatter plot of sample count and feature number of the method for identifying osteoarthritis characteristic genes using ADCC provided by the second embodiment of the present invention;

[0083] Fig.21 c is a scatter plot of PC dimension selection for the method for identifying osteoarthritis characteristic genes using ADCC provided in the second embodiment of the present invention;

[0084] Fig.21 d is a scatter plot of highly variable genes of samples of the method for identifying osteoarthritis characteristic genes using ADCC provided in the second embodiment of the present invention;

[0085] Fig.21 e is a scatter plot of the P value of the PC dimension of the method for identifying osteoarthritis characteristic genes using ADCC provided in the second embodiment of the present invention;

[0086] Fig.21 f is a single cell UMAP image of the method for identifying osteoarthritis characteristic genes using ADCC provided in the second embodiment of the present invention;

[0087] Fig.21 g is a single cell UMAP graph between groups of the method for identifying osteoarthritis characteristic genes using ADCC provided in the second embodiment of the present invention;

[0088] Fig.21h is a subgroup expression diagram of characteristic genes in the method for identifying characteristic genes of osteoarthritis using ADCC provided in the second embodiment of the present invention;

[0089] Fig.21 i is the expression diagram of characteristic genes in each subgroup of the method for identifying characteristic genes of osteoarthritis using ADCC provided by the second embodiment of the present invention;

[0090] Fig.21 j is a box plot of the difference analysis of characteristic gene scores among immune cell groups in the method for identifying characteristic genes of osteoarthritis using ADCC provided in the second embodiment of the present invention;

[0091] Fig. 22 a is a box plot of CNV scores of each subgroup in the method for identifying osteoarthritis characteristic genes using ADCC provided in the second embodiment of the present invention;

[0092] Fig. 22 b is a UMAP diagram of the high, medium and low malignancy of RepC cells in the method for identifying osteoarthritis characteristic genes using ADCC provided in the second embodiment of the present invention;

[0093] Fig. 22 c is a correlation diagram between CNV score and ADCC score of the method for identifying osteoarthritis characteristic genes using ADCC provided in the second embodiment of the present invention;

[0094] Fig. 22 d is a box plot of ADCC scores between high, medium and low malignancy groups in the method for identifying osteoarthritis characteristic genes using ADCC provided in the second embodiment of the present invention;

[0095] Fig. 22 e is a differentiation trajectory diagram of high, medium and low malignancy groups in the method for identifying osteoarthritis characteristic genes using ADCC provided in the second embodiment of the present invention;

[0096] Fig. 22 f is a diagram showing the results of a pseudo-sequential analysis of a method for identifying osteoarthritis characteristic genes using ADCC provided in the second embodiment of the present invention;

[0097] Fig. 22 g is a graph showing expression changes of characteristic genes in the differentiation process between high, medium and low malignancy groups in the method for identifying characteristic genes of osteoarthritis using ADCC provided in the second embodiment of the present invention;

[0098] Fig.23 a is a UMAP diagram of RepC cell subpopulations in the method for identifying osteoarthritis characteristic genes using ADCC provided in the second embodiment of the present invention;

[0099] Fig.23 b is a differential gene map of each subgroup in the method for identifying osteoarthritis characteristic genes using ADCC provided in the second embodiment of the present invention;

[0100] Fig.23 c is a subgroup heat map of the method for identifying osteoarthritis characteristic genes using ADCC provided in the second embodiment of the present invention;

[0101] Fig.23 d is a GO enrichment analysis diagram of differentially expressed genes in each subgroup of the method for identifying osteoarthritis characteristic genes using ADCC provided in the second embodiment of the present invention;

[0102] Fig.23 e is a UMAP diagram for comparing subgroups of the method for identifying osteoarthritis characteristic genes using ADCC provided in the second embodiment of the present invention;

[0103] Fig.23 f is a bar graph showing the proportions among subgroups of the method for identifying osteoarthritis characteristic genes using ADCC provided by the second embodiment of the present invention;

[0104] Fig.24 a is a diagram showing the interaction between ProC cells and other cells in the method for identifying osteoarthritis characteristic genes using ADCC provided in the second embodiment of the present invention;

[0105] Fig.24 b is a diagram showing the interaction between RepC and other cells in the method for identifying osteoarthritis characteristic genes using ADCC provided in the second embodiment of the present invention;

[0106] Fig.24 c is a diagram showing the independent interaction between each cell and other cells in the method for identifying osteoarthritis characteristic genes using ADCC provided in the second embodiment of the present invention;

[0107] Fig.24 d is a heat map of the signal pathways received by each cell in the method for identifying osteoarthritis characteristic genes using ADCC provided in the second embodiment of the present invention;

[0108] Fig.24 e is a heat map of the signal pathways sent by each cell in the method for identifying osteoarthritis characteristic genes using ADCC provided in the second embodiment of the present invention;

[0109] Fig.25 a is a diagram showing the interaction between RepC2 cells and other cells in the method for identifying osteoarthritis characteristic genes using ADCC provided in the second embodiment of the present invention;

[0110] Fig.25 b is a diagram showing the interaction between RepC5 cells and other cells in the method for identifying osteoarthritis characteristic genes using ADCC provided in the second embodiment of the present invention;

[0111] Fig.25 c is a diagram showing the independent interaction between each cell and other cells in the method for identifying osteoarthritis characteristic genes using ADCC provided in the second embodiment of the present invention;

[0112] Fig.25 d is a heat map of the signal pathways received by each cell in the method for identifying osteoarthritis characteristic genes using ADCC provided in the second embodiment of the present invention;

[0113] Fig.25 e is a heat map of the signal pathways sent by each cell in the method for identifying osteoarthritis characteristic genes using ADCC provided in the second embodiment of the present invention;

[0114] Fig.26 CellphoneDB cell communication analysis diagram of the method for identifying osteoarthritis characteristic genes using ADCC provided in the second embodiment of the present invention;

[0115] Fig. 27 a is a UMAP graph of characteristic gene expression in the method for identifying characteristic genes of osteoarthritis using ADCC provided in the second embodiment of the present invention;

[0116] Fig. 27 b is a UMAP diagram of the expression of characteristic genes in RepC cell subpopulations in the method for identifying characteristic genes of osteoarthritis using ADCC provided in the second embodiment of the present invention;

[0117] Fig. 27 c is a scatter plot of expression changes of characteristic genes in the process of cell differentiation in the method for identifying characteristic genes of osteoarthritis using ADCC provided by the second embodiment of the present invention;

[0118] Fig.28 This is a structural block diagram of the first embodiment of the device for identifying osteoarthritis characteristic genes using ADCC according to the present invention. DETAILED DESCRIPTION

[0119] It should be understood that the specific embodiments described herein are only used to explain the present invention, and are not used to limit the present invention.

[0120] Reference Figure 1 , Figure 1 This is a schematic diagram of the structure of a device for identifying osteoarthritis gene characteristics in the hardware operating environment involved in an embodiment of the present invention.

[0121] like Figure 1As shown, the device for identifying osteoarthritis gene features may include: a processor 1001, such as a central processing unit (CPU), a communication bus 1002, a user interface 1003, a network interface 1004, and a memory 1005. Among them, the communication bus 1002 is used to realize the connection and communication between these components. The user interface 1003 may include a display screen (Display), an input unit such as a keyboard (Keyboard), and the optional user interface 1003 may also include a standard wired interface and a wireless interface. The network interface 1004 may optionally include a standard wired interface and a wireless interface (such as a wireless fidelity (Wireless-Fidelity, Wi-Fi) interface). The memory 1005 may be a high-speed random access memory (Random Access Memory, RAM), or a stable non-volatile memory (Non-Volatile Memory, NVM), such as a disk storage. The memory 1005 may also be a storage device independent of the aforementioned processor 1001.

[0122] Those skilled in the art will understand that Figure 1 The structure shown in the figure does not constitute a limitation on the device for identifying osteoarthritis characteristic genes using ADCC, and may include more or less components than shown in the figure, or combine certain components, or arrange the components differently.

[0123] like Figure 1 As shown, the memory 1005 as a storage medium may include an operating system, a network communication module, a user interface module, and a program for identifying characteristic genes of osteoarthritis using ADCC.

[0124] exist Figure 1 In the device for identifying osteoarthritis characteristic genes using ADCC shown, the network interface 1004 is mainly used for data communication with the network server; the user interface 1003 is mainly used for data interaction with the user; the processor 1001 and the memory 1005 in the device for identifying osteoarthritis characteristic genes using ADCC of the present invention can be set in the device for identifying osteoarthritis characteristic genes using ADCC, and the device for identifying osteoarthritis characteristic genes using ADCC calls the program for identifying osteoarthritis characteristic genes using ADCC stored in the memory 1005 through the processor 1001, and executes the method for identifying osteoarthritis characteristic genes using ADCC provided in an embodiment of the present invention.

[0125] The embodiment of the present invention provides a method for identifying osteoarthritis characteristic genes using ADCC, referring to Figure 2 , Figure 2 The figure is a flow chart of the first embodiment of a method for identifying osteoarthritis characteristic genes using ADCC according to the present invention.

[0126] A method for identifying osteoarthritis characteristic genes using ADCC, comprising the following steps:

[0127] Step S1, searching for ADCC-related genes in a database, using R software to identify data in the database to obtain significantly differentially expressed genes, and screening out module genes related to osteoarthritis in the database according to the WGCNA network;

[0128] Step S2, performing an intersection operation on the significantly differentially expressed genes, the osteoarthritis-related module genes, and the ADCC-related genes to obtain intersection genes;

[0129] Step S3, constructing a protein interaction network for the intersection genes using the STRING database, and obtaining key differentially expressed genes;

[0130] Step S4: combining the key differential genes obtained in step S3 with a machine learning method to obtain characteristic genes.

[0131] Based on the above first embodiment, in this embodiment, if Figure 3 As shown, the step S1 is specifically as follows:

[0132] Step S11, downloading a data set from a database, processing the data set to obtain a training set and a validation set, and retrieving ADCC-related genes from the database;

[0133] Step S12, using R software to identify the data of the training set to obtain significantly differentially expressed genes;

[0134] Step S13, performing enrichment analysis on the significantly differentially expressed genes, and constructing a WGCNA network using the training set as the phenotype to screen out module genes associated with osteoarthritis.

[0135] Download the dataset from the NCBI GEO database (Gene Expression Omnibus, GEO, http: / / www.ncbi.nlm.nih.gov / geo / ):

[0136] Dataset 1: GSE55235, a total of 20 samples, 10 osteoarthritis (OA) samples and 10 normal samples;

[0137] Dataset 2: GSE55457, a total of 20 cases, 10 OA samples and 10 Normal samples;

[0138] Dataset 3: GSE1919, a total of 10 cases, 5 OA samples and 5 Normal samples.

[0139] The above GSE55235 and GSE55457 datasets were merged into batches as the training set, and GSE1919 was used as the validation set for subsequent analysis.

[0140] Dataset 4: GSE220243, a total of 26 single cell samples, including cartilage tissue and meniscus tissue samples, including 12 osteoarthritis samples and 14 control samples. This report uses 6 normal cartilage tissue samples and 6 cartilage tissue osteoarthritis samples, a total of 12 samples, for single cell correlation analysis.

[0141] The two datasets GSE55235 and GSE55457 were integrated and de-batch as training sets for subsequent analysis. The dataset has a total of 40 samples, including 20 disease samples and 20 control samples. The data before and after de-batch are shown below. Figure 4 From the box plot, we can see that before removing the batch effect, the sample distributions of each data set are quite different. From the umap plot, we can see that before removing the batch effect, the samples of each data set are clustered together, indicating that there is a batch effect. After removing the batch effect, the data distributions of each data set in the box plot tend to be consistent, and the medians are on a line. In the umap plot, the samples of each data set are clustered and intertwined, indicating that the batch effect has been well removed.

[0142] The limma package of R software was used to perform differential analysis on all genes in the disease and normal groups, and the corresponding P.Value and logFC information of the genes were calculated. The Benjamini&Hochberg method was used for multiple test correction to obtain the corrected p-value, i.e., P.Value. The differential expression threshold was set as follows: P-values<0.05&|logFC|>0.263. According to the threshold described in the method, 3945 differentially expressed genes were obtained, 2174 up-regulated genes, and 1771 down-regulated genes. The volcano plot was drawn according to the threshold of the method. The results are shown in the figure. Figure 5 As shown in a, a heat map of the top 20 genes that are differentially up-regulated and down-regulated is drawn. Figure 5 As shown in b.

[0143] GO and KEGG enrichment analysis was performed on the above differentially expressed genes, and the top 30 pathways or functional entries were visualized. In KEGG, the genes were mainly enriched in Rheumatoid arthritis, MAPK signaling pathway, HIF-1 signaling pathway, TNF signaling pathway, Human T-cell leukemia virus 1 infection, etc. Figure 6As shown in a. In GO, it is mainly enriched in pathways such as positive regulation of cytokine production, positive regulation of cell adhesion, cell-substrate adhesion, chemotaxis, and positive regulation of leukocyte migration. Figure 6 As shown in b.

[0144] In order to find out the module genes that are highly related to osteoarthritis, the sample disease type was used as the phenotype to construct the WGCNA network, and the key modules and genes related to OA were screened out. The keyword antibody-dependent cell cytotoxicity was used to search for relevant results in the genecards database, and 600 ADCC-related genes were obtained. Figure 7 The horizontal axis of a represents the weight parameter. The vertical axis of the left figure is ScaleFreeTopologyModelFit, i.e. signedR^2, which is the square of the correlation coefficient between log(k) and log(p(k)) in the corresponding network. The higher the square of the correlation coefficient, the closer the network is to the distribution without network scale. The vertical axis of the right figure represents the mean of all gene adjacency functions in the corresponding gene module. The recommended optimal power value in the WGCNA package is 6. According to the position of the red line in the figure, the power threshold is determined to be 6, that is, Figure 7 The vertical coordinate R^2 of a (left) is around 0.9, indicating that the network is closer to scale-free distribution, and Figure 7 The mean of the adjacency function in a (right) also gradually approaches 0, showing a gentle trend. Then, according to the standard of the hybrid dynamic tree cutting algorithm (dynamic tree cutting), the highly correlated genes are clustered into modules, and then these modules are clustered. Figure 7 As can be seen in b, a total of 12 modules were obtained, and the visualized gene network (TOMplot) is shown in Figure 7 c.

[0145] The correlation between the module and the sample traits is analyzed to associate the module with the phenotype. In these 12 modules, the heat map of the correlation between the genes and traits of each module is shown in Figure 7 d. Module genes with correlation > 0.5 and significant were selected. Finally, the three modules of green (n = 276, cor = 0.53), brown (n = 983, cor = 0.69), and turquoise (n = 1028, cor = 0.57) were retained for subsequent analysis, with a total of 2287 genes.

[0146] The intersection of DEGs (significantly different genes), WGCNA module genes (module genes related to osteoarthritis) and ADCC genes was taken to identify DEGs-WGCNA-ADCC genes (intersection genes), see for details. Figure 8 a, as shown in the figure, there are 55 intersection genes. Functional enrichment analysis of these 55 genes showed that they were mainly enriched in KEGG in Osteoclast differentiation, Hematopoietic cell lineage, Primary immunodeficiency, PD-L1 expression and PD-1 checkpoint pathway in cancer, Cytokine-cytokiner receptor interaction, etc. ( Figure 8 b and Figure 8 d). In GO, it is mainly enriched in leukocytemediatedimmunity, lymphocytemediatedimmunity, leukocytemediatedcytotoxicity, cellkilling, tumornecrosisfactorproduction and other pathways ( Figure 8 c and Figure 8 e).

[0147] The protein interaction network of the above-mentioned intersection genes was constructed using the STRING database, and a total of 338 interaction pairs between 46 genes were obtained. The specific results are shown in Fig. 9 a. The four topological analysis algorithms MCC, MNC, Degree, and EPC in cytoHubba were used to predict and explore the top 30 important hub genes in the PPI network. The top 30 intersection genes obtained by the four algorithms were the key differentially expressed genes.

[0148] According to the method, based on the expression values ​​of the 30 key differentially expressed genes in all samples obtained in the previous step, combined with the sample grouping (Nromal and OA), LASSOlogistic regression was performed, and finally 4 genes (EGFR, GZMB, CX3CR1, TNFSF11) were included in the lasso model construction, as shown in Fig.10 shown.

[0149] Perform SVM-RFE feature screening according to the method, such as Fig.11As shown, the model achieved the highest accuracy after incorporating the first 17 features, when all 17 characteristic ADCC genes were included in the model (CX3CR1, EGFR, MS4A1, TNFSF11, TLR7, CD3D, CD4, LILRB1, SYK, MAPK3, TREM2, GZMB, APOE, PTPRC, MMP9, VCAM1, CXCL10).

[0150] According to the method, a random forest model was constructed for 30 hub genes, and the genes obtained by the RF algorithm were ranked as follows Fig.12 As shown, the top 10 genes (CX3CR1, TLR7, CD2, TREM2, TNFSF11, CD52, CD4, MMP9, EGFR, and BTK) were screened out as key feature genes.

[0151] The characteristic genes obtained by the three machine learning algorithms are intersected, such as Fig.13 As shown, three key genes (EGFR, CX3CR1, and TNFSF11) were obtained.

[0152] The expression levels of the key genes obtained in the above analysis were extracted from the training set and the validation set, and the inter-group difference analysis and ROC curve analysis of each gene were performed. Fig.14 It can be seen that all key genes in the training set and the validation set showed significant differences between the groups, and the AUC value of each gene was greater than 0.85, indicating that the three characteristic genes had a high diagnostic value.

[0153] Based on ssGSEA, the proportion of immune cell types in the included analysis samples was evaluated, and the correlation between characteristic genes and differential immune cells was calculated using the ggplot2 package in R language. Fig.15 a It can be seen that 12 types of immune cells have significant differences between normal and diseased samples. Heat map of correlation between differential immune cells and characteristic genes, see Fig.15 b, Fig.15 c is a lollipop plot of the correlation between individual genes and differential immune cells.

[0154] At the same time, the proportion of immune cell types in the samples included in the analysis was evaluated based on CIBERSORT. Fig.16 a It can be seen that 12 types of immune cells have significant differences between normal and diseased samples. Heat map of correlation between differential immune cells and characteristic genes, see Fig.16 b, Fig.16 c is a lollipop plot of the correlation between individual genes and differential immune cells.

[0155] Based on the diagnostic genes obtained from the above analysis, combined with the sample grouping information and the expression level of the diagnostic genes, the “RMS” package was used to construct a nomogram model of diagnostic genes, such as Fig.17 As shown. To evaluate the predictive ability of the nomogram model, the calibration curve was drawn using the calibrate function in the "RMS" package, and the decision curve was drawn using the "rmda" package. The calibration curve showed that the error between the actual risk of disease and the predicted risk was very small, indicating that the nomogram model has a high predictive accuracy for the diagnosis of OA. Decision curve analysis (DCA) showed that the nomogram curve was higher than the gray line and the DCA curve of each gene.

[0156] The GSEA algorithm was used to identify differentially regulated pathways between the high expression and low expression groups of the final three diagnostic genes to determine the signaling pathways activated in the disease. The pathways closely related to the high expression of the final hub gene were selected as the activated pathways. The gene sets enriched with P<0.05 were selected, and the top 15 pathways were displayed in a ridge diagram according to the P value. From left to right, the single gene GSEA enrichment results of EGFR, CX3CR1, and TNFSF11 are shown in the figure. Fig.18 shown.

[0157] Predict the targeted drugs of the characteristic genes as described in the method, screen the drug results, and select the top 5 drugs sorted by InteractionCount for network diagram drawing. Use AutoDock4.2 and AutoDocktools1.5.7 software for molecular docking of proteins and small molecules. In this study, the protein was dehydrated, hydrogenated, and charged, and saved in pdbqt format. Import the processed protein and small molecule ligand into AutoDock, select the semi-flexible docking method for molecular docking, and the size of the docking box is The grid spacing is The obtained molecular docking results were visualized and analyzed using pymol2.2.0, as shown in Fig.19 shown.

[0158] Fig.19 a The molecular docking results show that the binding energies of the three gene targets with the drug small molecules are all negative, indicating that the target proteins can spontaneously bind to the drug small molecules. Among them, the binding energies of EGFR, TNFSF11, CX3CR1 with Benzo(a)pyrene, Gefitinib, Niclosamide, and RTKI-cpd are all below -7kcal / mol, indicating that the target proteins have strong binding affinity with the drug small molecules. Pymol visualization shows that the main type of force between the target protein and the drug small molecule is hydrogen bond interaction, and there are π-π forces and π-alkyl forces between some protein small molecules. The specific binding modes are as follows: Fig.19 As shown in b.

[0159] Based on the consistent clustering of the training set OA data, the 20 sample classes are divided into two subgroups, Cluster 1 and Cluster 2. C1 contains 17 samples and C2 contains 3 samples. See below for details. Fig.17 , PCA of the two subgroups Fig. 20 d. From the PCA graph, we can see that there is a clear distinction between the two groups of samples.

[0160] Single-cell datasets were analyzed and annotated using cellular markers as described in the Methods. Fig.21 ae is an analysis chart for early quality control of data processing, PC dimension selection, top 10 highly variable genes, etc.

[0161] A total of 82,785 cells were obtained through analysis and divided into 10 cell populations, including 10,971 proliferative chondrocytes (ProC), 9,585 homeostatic chondrocytes (HomC), 15,163 fibrochondrocytes (FC), 9,285 prefibrochondrocytes (preFC), 7,815 effector chondrocytes (EC), 8,723 repair chondrocytes (RepC), 15,421 regulatory chondrocytes (RegC), 2,909 prehypertrophic chondrocytes (preHTC), 2,716 hypertrophic chondrocytes (HTC), and 197 immune cells (immune), see Fig.21 f. UMAP diagram between groups Fig.21 g, the bar graph of the proportion distribution between groups is shown in Fig.21 h.

[0162] The expression levels of these characteristic genes in each cell were quantified using the AddModuleScore function and the previously obtained characteristic genes. The expression differences of the risk score characteristics between the osteoarthritis group and the normal group in each cell cluster were compared to obtain the cells with the largest significant differences, as shown below. Fig.21 As can be seen from the figure, the characteristic genes have the greatest difference between groups in the repair chondrocytes RepC, and the proportion in the OA group is higher than that in the control group, so RepC cells were selected for subsequent analysis.

[0163] The CNV scores in RepC cells were calculated by the inferCNV algorithm and divided into three groups according to the CNV scores: high malignancy, medium malignancy and low malignancy ( Fig. 22 ab). The correlation between ADCC_Score and CNV_Score was calculated ( Fig. 22 c). At the same time, the differences between the high, medium and low malignancy groups were compared. The ADCC scores of the moderately malignant cells were significantly different from those of the low and high malignancy cells ( Fig. 22 d). Pseudo-time series analysis of RepC cells shows that the cell trajectory is differentiated from low malignancy to medium / high malignancy groups, as shown in the figure. Fig. 22 ef. At the same time, the expression changes of characteristic genes during the differentiation process between high, medium and low malignancy groups were checked, and no changes in the expression of characteristic genes were found ( Fig. 22 g).

[0164] RepC cells are divided into 5 subpopulations, see below Fig.23 a. The scatter plot and heat map of differentially expressed genes in each subgroup are shown below Fig.23 b, 23c. GO functional enrichment analysis was performed on the differentially expressed genes among the subgroups. The specific results are shown below. Fig.23 d. Fig.23 e is the UMAP diagram between RepC cell subgroups, Fig.23 f is a bar graph showing the changes in cell proportions among RepC cell subgroups. As can be seen from the figure, the proportions of RepC1 and RepC4 cells increased in the osteoarthritis group.

[0165] Cellchat analysis was performed on all cells. As can be seen from the figure, ProC cells interacted strongly with other cells, while RepC cells interacted weakly with other cells ( Fig.24 ab). Fig.24 c is the independent display of the interaction between each cell and other cells. Fig.24 d- Fig.24 e is the heat map of the signal pathways received (incoming) and sent (outgoing) by each cell.

[0166] At the same time, cellchat analysis was performed on RepC cells. It can be seen from the figure that RepC2 and RepC5 cells interacted strongly with other cells, while RepC1 did not interact with other cells ( Fig.25 ab). Fig.25 c is the independent display of the interaction between each cell and other cells. Fig.25 de is a heat map of the signal pathways received (incoming) and sent (outgoing) by each cell.

[0167] Check the expression of characteristic genes in each cell population. Fig. 27 a shows that CX3CR1 is mainly expressed in immune cells, EGFR is mainly expressed in RepC cells, and TNFSF11 is mainly expressed in HTC. The expression of characteristic genes in each cell population did not change during differentiation. Fig. 27 b As can be seen, EGFR is mainly expressed in RepC cells, and TNFSF11 is mainly expressed in HTCs. Fig. 27 c It can be seen that the expression of characteristic genes did not change during the differentiation process of each cell group.

[0168] refer to Fig.28 , Fig.28 This is a structural block diagram of a first embodiment of a device for identifying osteoarthritis characteristic genes using ADCC according to the present invention.

[0169] like Fig.28 As shown, the device for identifying the genetic signature of osteoarthritis comprises:

[0170] Data processing module: retrieve ADCC-related genes from the database, use R software to identify the data in the database to obtain significantly differentially expressed genes, and screen out module genes related to osteoarthritis from the database according to the WGCNA network;

[0171] Intersection gene processing module: performing intersection operation on the significantly different genes, the module genes related to osteoarthritis and the ADCC-related genes to obtain intersection genes;

[0172] Key differentially expressed genes processing module: using the STRING database to construct a protein interaction network for the intersection genes and obtain key differentially expressed genes;

[0173] Characteristic gene processing module: combining the key differential genes with machine learning methods to obtain characteristic genes.

[0174] It should be understood that the above is only an example and does not constitute any limitation on the technical solution of the present invention. In specific applications, technicians in this field can make settings as needed, and the present invention does not limit this.

[0175] It should be noted that the workflow described above is merely illustrative and does not limit the scope of protection of the present invention. In practical applications, technicians in this field can select part or all of them according to actual needs to achieve the purpose of the present embodiment, and no limitation is made here.

[0176] In addition, for technical details not fully described in this embodiment, reference can be made to the method for identifying osteoarthritis characteristic genes provided in any embodiment of the present invention, and will not be repeated here.

[0177] In addition, it should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or system including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or system. In the absence of further restrictions, an element defined by the sentence "comprises a ..." does not exclude the existence of other identical elements in the process, method, article or system including the element.

[0178] The serial numbers of the above embodiments of the present invention are only for description and do not represent the advantages or disadvantages of the embodiments.

[0179] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art can be embodied in the form of a software product, which is stored in a storage medium (such as a read-only memory (ROM) / RAM, a magnetic disk, or an optical disk), and includes a number of instructions for a terminal device (which can be a mobile phone, a computer, a server, or a network device, etc.) to execute the methods described in each embodiment of the present invention.

[0180] The above are only preferred embodiments of the present invention, and are not intended to limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made using the contents of the present invention specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A method for identifying osteoarthritis characteristic genes using ADCC, characterized in that: include: Step S1, searching for ADCC-related genes in a database, using R software to identify data in the database to obtain significantly differentially expressed genes, and screening out module genes related to osteoarthritis in the database according to the WGCNA network; Step S2, performing an intersection operation on the significantly differentially expressed genes, the osteoarthritis-related module genes, and the ADCC-related genes to obtain intersection genes; Step S3, constructing a protein interaction network for the intersection genes using the STRING database, and obtaining key differentially expressed genes; Step S4: combining the key differential genes obtained in step S3 with a machine learning method to obtain characteristic genes.

2. The method for identifying osteoarthritis characteristic genes using ADCC according to claim 1, characterized in that: The step S1 specifically includes: Step S11, downloading a data set from a database, processing the data set to obtain a training set and a validation set, and retrieving ADCC-related genes from the database; Step S12, using R software to identify the data of the training set to obtain significantly differentially expressed genes; Step S13: performing enrichment analysis on the significantly differentially expressed genes, and using the training set as the phenotype to screen out module genes related to osteoarthritis according to the WGCNA network.

3. The method for identifying osteoarthritis characteristic genes using ADCC according to claim 2, characterized in that: In step S3, before constructing a protein interaction network for the intersection genes using the STRING database, functional enrichment analysis of the intersection genes is required.

4. The method for identifying osteoarthritis characteristic genes using ADCC according to claim 2, characterized in that: The specific method of step S3 is: using the four topological analysis algorithms MCC, MNC, Degree, and EPC in cytoHubba to predict the protein interaction network, and obtaining the top 30 intersection genes as key differentially expressed genes.

5. The method for identifying osteoarthritis characteristic genes using ADCC according to claim 4, characterized in that: The step S4 also includes combining the expression values ​​of the key differential genes in all samples with the sample grouping to perform LASSOlogistic regression, SVM-RFE feature screening, random forest model construction and obtain characteristic genes.

6. The method for identifying osteoarthritis characteristic genes using ADCC according to claim 5, characterized in that: The step S4 also includes taking the intersection of the characteristic genes to obtain the key characteristic genes.

7. The method for identifying osteoarthritis characteristic genes using ADCC according to claim 6, characterized in that: The step S4 further includes extracting the expression levels of the key characteristic genes from the training set and the validation set, respectively, and performing inter-group difference analysis and ROC validation on each set.

8. A device for identifying osteoarthritis characteristic genes using ADCC, characterized in that: include: Data processing module: retrieve ADCC-related genes in the database, use R software to identify the data in the database to obtain significantly differentially expressed genes, construct a WGCNA network in the database and screen out module genes related to osteoarthritis; Intersection gene processing module: performing intersection operation on the significantly different genes, the module genes related to osteoarthritis and the ADCC-related genes to obtain intersection genes; Key differentially expressed genes processing module: using the STRING database to construct a protein interaction network for the intersection genes and obtain key differentially expressed genes; Characteristic gene processing module: combining the key differential genes with machine learning methods to obtain characteristic genes.

9. A device for identifying osteoarthritis characteristic genes using ADCC, characterized in that: The device for identifying osteoarthritis characteristic genes using ADCC includes: a memory, a processor, and a program for identifying osteoarthritis characteristic genes using ADCC stored in the memory and executable on the processor, wherein the program for identifying osteoarthritis characteristic genes using ADCC is configured to implement the method for identifying osteoarthritis characteristic genes using ADCC according to any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps in the method for identifying osteoarthritis characteristic genes using ADCC as described in any one of claims 1 to 7 can be implemented.

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