Identification and validation of novel characteristic genes based on multi-tissue osteoarthritis

doi:10.21203/rs.3.rs-4706641/v1

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Identification and validation of novel characteristic genes based on multi-tissue osteoarthritis

https://doi.org/10.21203/rs.3.rs-4706641/v1

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Background

Osteoarthritis (OA) is characterized by synovial inflammation, articular cartilage degradation, and subchondral bone changes. Currently, there are no reliable biomarkers for the diagnosis and treatment of OA. Therefore, exploring OA biomarkers is crucial for its prevention, diagnosis, and treatment.

Materials and Methods

The GSE51588, GSE12021, GSE55457, GSE56409, GSE114007, GSE168505, GSE169077, GSE55235, GSE129147, and GSE48556 datasets of patients with OA and normal control samples were obtained from the GEO database. Differentially expressed genes (DEGs) in OA and normal controls were identified using R language. Protein-protein interaction (PPI) network and module analysis were performed to screen and filter key genes. Enrichment analyses were conducted to determine the biological functions and pathways of key DEGs and predict potential transcription factors. Machine learning models (XGBoost, LASSO regression, and SVM) were used to identify the best characteristic genes, and the intersection of hub genes was used as the final diagnostic genes. ROC analysis and nomogram were used to evaluate the diagnostic value of candidate genes. The expression levels of characteristic genes were validated in external GEO datasets containing cartilage, synovial membrane, and blood samples from patients. The expression levels of the key gene IRS2 in chondrocytes were further confirmed through in vitro experiments.

Results

Fifteen OA characteristic genes (IRS2, ADM, SIK1, PTN, CX3CR1, WNT5A, IL21R, APOD, CRLF1, FKBP5, PNMAL1, NPR3, RARRES1, ASPN, POSTN) were identified using three machine learning algorithms. Enrichment analysis indicated that abnormal expression of DEGs and hub genes may be mediated by extracellular matrix organization, extracellular structure organization, Relaxin signaling pathway, IL-17 signaling pathway, AGE-RAGE signaling pathway in diabetic complications, and PI3K-Akt signaling pathway, which are involved in OA occurrence. Four diagnostic genes (IRS2, WNT5A, PTN, POSTN) were highly correlated with OA. Validation data set analysis showed that IRS2 was down-regulated, while WNT5A, PTN, and POSTN were up-regulated in the experimental group compared to the normal group. qRT-PCR and WB results verified that the expression level of diagnostic gene IRS2 was consistent with bioinformatics analysis results.

Conclusion

This study integrates bioinformatics analysis and machine learning algorithms to identify and validate four promising biomarkers: IRS2, WNT5A, PTN, and POSTN. POSTN can be used as a biomarker for OA cartilage, and early diagnosis of PTN in OA deserves attention. WNT5A and IRS2 offer new diagnostic perspectives for OA.

Osteoarthritis

Biomarkers

Machine Learning

Bioinformatics

Tissue-specific expressed genes

Osteoarthritis (OA) remains a global health challenge¹, affecting an estimated 600 million people worldwide, or 7.6% of the world's population ², with a higher prevalence in women³. The incidence and disability rate of OA have been rising since 1990, increasing by as much as 8% to 10%⁴, Its prevalence is expected to nearly double in the next decade⁵. Additionally, the socioeconomic costs of OA are substantial, estimated to reach $80 billion in 2016 alone, and growing at a rate of 5.7% per year⁴. As the population ages, the prevalence of OA increases year by year, and in the elderly, the risk of mobility impairment and disability caused by OA surpasses that of any other disease.^6,7.

OA is characterized by the degradation of the extracellular matrix (ECM), decreased synthesis of type II collagen, increased production of matrix metalloproteinase 13 (MMP13), and activation of Cyclooxygenase-2 (COX2) by destructive intracellular cytokines. These cytokines promote the conversion of arachidonic acid into inflammatory prostaglandins, resulting in an inflammatory response⁸. During this process, the synovial membrane, cartilage, and subchondral bone interact with each other, jointly promoting disease development^9-11. Although the etiology of OA is multi-factorial, the exact cause remains unknown. Therefore, developing new OA-related targets for better diagnosis and treatment of patients with OA is urgently needed and remains a focus and challenge of current research.

With the rapid development of high-throughput sequencing technology, an increasing number of OA-related datasets have been generated and utilized ^12-15. Although some studies have focused on OA biomarkers, most are based on single tissues or individual datasets¹⁶. The occurrence and development of OA are closely related to the interactions among cartilage, subchondral bone, and synovitis. Despite some controversy regarding the order of pathological events, it is undeniable that these components are all involved in disease progression ^17,18, which may lead to the unreliability of current research results ^19-22. The application of machine learning (ML) in OA biomarker development has garnered growing research interest. For instance, Chen et al.²² applied synovial tissue microarray datasets and identified ZBTB16, TNFSF11, SCRG1, and KDELR3 as diagnostic biomarkers for OA. A study by Han et al.[14] also reported that TLR7, CSF1R, APOE, C1QA, and CCL5 are key genes with high diagnostic value for OA after applying weighted gene co-expression network analysis (WGCNA) and the cytoHubba algorithm. Although these findings may contribute to a better understanding of OA and provide new insights into its diagnosis and treatment, the limited sample sizes and number of algorithms used may undermine the reliability and robustness of these studies to some extent.

In this study, we aim to integrate public OA datasets from subchondral bone, cartilage, and synovium tissues to discover and validate meaningful OA biomarkers. We employed Robust Rank Aggregation (RRA) and Surrogate Variable Analysis (SVA) to jointly screen for differentially expressed genes. Additionally, we used machine learning simulations to identify the best method for screening disease biomarkers and validated these biomarkers in OA cartilage, synovium, and blood samples. Furthermore, we investigated the biological processes and predicted transcription factors of disease hub genes.

2.1 Data Collection

The datasets GSE51588, GSE12021, GSE55457, GSE56409, GSE114007, GSE168505, GSE169077, GSE55235, GSE129147, and GSE48556 were obtained from the Gene Expression Omnibus (GEO) database (https://www.ncbi.nlm.nih.gov/geo/). GSE51588 is a whole-genome expression profile of subchondral bone. GSE12021, GSE55457, GSE56409, and GSE55235 contain data measured in synovial samples from OA patients and normal controls. GSE114007, GSE168505, GSE169077, and GSE129147 are RNA sequencing data from joint cartilage tissues, while GSE48556 consists of mononuclear cells from the blood of OA patients and normal controls. All these data are derived from human samples.

2.2 Identification of Differentially Expressed Genes

2.2.1 Identification of DEGs Using the RRA Method

The seven raw datasets were processed using the limma package in Bioconductor, which included normalization and log transformation. Subsequently, DEGs were analyzed using the limma package, and ranked lists of upregulated and downregulated DEGs were generated for each dataset based on their fold changes. Finally, we integrated the results of these seven datasets using the R package "RobustRankAggreg" based on the robust rank aggregation (RRA) method to identify the most significant DEGs. In the RRA analysis, genes with a log fold change (logFC) >1 and an adjusted p-value <0.05 were selected as significant DEGs.

2.2.2 Identification of Differentially Expressed Genes (DEGs) Using the SVA Method

Before merging the seven microarray datasets, we used the ComBat function in the Surrogate Variable Analysis (SVA) package to correct for batch effects, aiming to minimize experimental variance before subsequent analyses. Box plots and Principal Component Analysis (PCA) were used to assess the data before and after correction. The final merged dataset consisted of 167 samples, including 66 normal control samples and 101 OA samples. In the SVA analysis, genes with a log fold change (logFC) >1 and an adjusted p-value <0.05 were selected as significant DEGs. Finally, the common genes identified by both the RRA and SVA methods were considered as key genes for OA.

2.3 Gene Ontology（GO） Functional Annotation, Kyoto Encyclopedia of Genes and Genomes (KEGG) Analysis, and Disease Ontology (DO) Enrichment Analysis

GO functional annotation, KEGG analysis, and DO enrichment analysis were performed using R packages (enrichplot, RColorBrewer, Heatmap, etc.). p-value <0.05 was considered statistically significant for enrichment.

2.4 Identification of Gene Clusters and Construction of Protein-Protein Interaction Networks

The STRING database (https://string-db.org/) was used to construct the protein-protein interaction (PPI) network, which was then imported into Cytoscape v3.9.1 software to visualize the key nodes in the molecular interaction network. The cytoHubba algorithms were employed to analyze the PPI network. Only genes identified as hub genes by all cytoHubba calculation methods ("MCC", "DMNC", "MNC", "Degree", "EPC", "BottleNeck", "EcCentricity", "Closeness", "Radiality", "Betweenness", "Stress", "ClusteringCoefficient") were included in the final selection. The interaction genes and functions of these hub genes were predicted, and relevant PPI networks were generated using the GeneMANIA database (https://genemania.org/). Enrichment analysis of the hub genes was further conducted using GO functional terms and KEGG pathway enrichment analysis, with a p-value <0.05 considered statistically significant.

2.5 Construction of Transcriptional Regulatory Networks

To identify substantial transcriptional changes and gain deeper insights into the regulatory roles of key osteoarthritis (OA) genes, we utilized TRRUST (https://www.grnpedia.org/trrust/) for sentence-based text mining to uncover transcriptional regulatory relationships. Subsequently, we assessed the enrichment of hub genes to identify the corresponding transcription factors (TFs). Finally, we constructed the TF regulatory network using Cytoscape software and validated the TFs that regulate multiple hub genes.

2.6 Machine Learning

We used the `train` function from the caret package to predict the best model using various machine learning methods, including Least Absolute Shrinkage and Selection Operator (LASSO), Support Vector Machines (SVM), Decision Trees (DT), Random Forest (RF), eXtreme Gradient Boosting (XGBoost), and Generalized Linear Model (GLM).

LASSO regression is a linear regression technique that combines L1 regularization to constrain the complexity of the model, thus achieving feature selection and preventing overfitting²³. SVM is a powerful supervised learning model that can be used for classification and regression tasks. It has shown wide applicability and superior performance in high-dimensional data, complex data structures, and small sample learning²⁴. DT is a supervised learning algorithm that splits nodes by selecting the features that best divide the data²⁵. RF is an ensemble learning method that improves model performance and stability by voting (classification) or averaging (regression) the results of multiple decision trees. It is very powerful in identifying subtle patterns in complex datasets²⁶. XGBoost is a gradient boosting framework that uses tree structure models for efficient and flexible machine learning algorithms. It is widely praised for its excellent performance and efficiency, being widely used in various machine learning competitions and real-world problems²⁷. GLM is an extension of linear models that can be applied to a wide range of data types, with strong interpretability and good applicability.

Through training and testing these models, we selected the best three models to analyze the differentially expressed genes and identify the optimal feature genes. The results obtained were intersected with the hub genes to serve as diagnostic genes for the disease.

2.7 Validation of Feature Genes with Receiver Operating Characteristic(ROC) Curve and Construction of Clinical Diagnostic Model

To validate the importance of candidate genes in diagnosis, we used the "rms" R package to construct a nomogram and to predict and interpret the constructed model. Box plots and violin plots were used to visualize the differential expression of genes between the control and OA groups. We further evaluated the prognostic value of the candidate genes through ROC analysis, obtaining the area under the ROC curve (AUC) and the 95% confidence interval (CI). An AUC value >0.7 was considered to indicate good diagnostic performance.

2.8 Validation of Feature Genes by Western Blot(WB) and Quantitative Real-Time PCR (qRT-PCR)

Human SW1353 chondrocytes, commonly used for OA modeling^8,28, were cultured in an incubator under standard conditions (37℃, 5% CO2) using specialized SW1353 culture medium (DMEM, Pricella, CN), with the medium changed every 2-3 days. When SW1353 cells reached 50% confluence, they were stimulated with 10 ng/ml IL-1β(Peprotech, USA) for 24 hours to model OA^29-31, The treated chondrocytes were lysed in RIPA lysis buffer (Solarbio, CN) containing 1% protease and phosphatase inhibitors (GlpBio, USA) for 30 minutes. The lysates were collected by scraping the adherent cells thoroughly, then incubated on a shaker at 4°C for 1 hour before centrifugation to collect the supernatant for subsequent experiments. The protein concentration in the supernatant was measured using a BCA kit (Solarbio, China) on a microplate reader (Thermo Fisher Scientific, USA) at a wavelength of 562 nm.

Subsequently, equal amounts of sample protein and protein markers were separated by 10% sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) and transferred to PVDF membranes (Millipore, USA). After blocking with 5% BSA (Servicebio, Wuhan, CN) for 1 hour, the membranes were incubated with primary antibodies overnight at 4℃. After washing three times with Tris-buffered saline containing Tween (TBST), the membranes were incubated with secondary antibodies at room temperature for 2 hours. Details of the primary and secondary antibodies are listed in Table 1. Purified protein bands were visualized using enhanced chemiluminescence reagent (YEASEN, CN) and imaged with a Bio-Rad scanner (Bio-Rad, USA). GAPDH was used as the housekeeping gene. Band analysis was performed using ImageJ software.

Table 1 Antibody Information

Name	Type	Species	Source
COL2A1	primary antibodies	Rabbit	Proteintech/China
IRS2	primary antibodies	Mouse	Proteintech/China
MMP13	primary antibodies	Rabbit	Proteintech/China
SOX9	primary antibodies	Rabbit	Zenbio/China
COX2	primary antibodies	Rabbit	Zenbio/China
GAPDH	primary antibodies	Mouse	Proteintech/China
Gout anti-Rabbit IgG	secondary antibodies	-	Proteintech/China
Gout anti-Mouse IgG	secondary antibodies	-	Proteintech/China

Similarly, total RNA was extracted from the treated chondrocytes using Trizol reagent (TaKaRa, JPN) and purified with an RNA extraction kit (TransGen, CN). The total RNA concentration of the samples was measured using a micro-UV spectrophotometer (Thermo Fisher Scientific, USA). The RNA samples were then reverse transcribed into cDNA using a reverse transcription kit (TransGen, CN). Finally, the target mRNA was amplified on a real-time fluorescence quantitative PCR instrument (Bio-Rad, USA). The final quantitative results were normalized to GAPDH and calculated using the 2^(-△△Ct) method. The primer sequences for the target genes are shown in Table 2.

Table 2 Primer Information

Primer Name	Primer Sequence (5'-3')	Species
COL2A1	Forword :TGGACGATCAGGCGAAACC	Human
	Reverse :GCTGCGGATGCTCTCAATCT
IRS2	Forword :CGGTGAGTTCTACGGGTACAT	Human
	Reverse :TCAGGGTGTATTCATCCAGCG
MMP13	Forword :ACTGAGAGGCTCCGAGAAATG	Human
	Reverse :GAACCCCGCATCTTGGCTT
SOX9	Forword :AGCGAACGCACATCAAGAC	Human
	Reverse :CTGTAGGCGATCTGTTGGGG
COX2	Forword :GCACCCCGACATAGAGAGC	Human
	Reverse :CTGCGGAGTGCAGTGTTCT
GAPDH	Forword :TGTGGGCATCAATGGATTTGG	Human
	Reverse :ACACCATGTATTCCGGGTCAAT

2.9 Statistical Analysis

Data were analyzed using R 4.3.0 (https://www.rproject.org/), Cytoscape 3.9.1 (https://cytoscape.org/index.html), Perl 5.32.1 (https://www.perl.org), and GraphPad Prism 9.7 (https://www.graphpad.com/) software. Continuous data were described as mean ± standard deviation. For comparisons between two groups of continuous data, if the data followed a normal distribution and had equal variances, a t-test (T test) was used. If the data did not follow a normal distribution, a non-parametric test (Mann-Whitney U test) was employed. All experiments were independently repeated three times, and a P value less than 0.05 was considered statistically significant.

3.1 Identification of DEGs using RRA Method

Table 3 describes the sample characteristics of the datasets included in our study, including dataset ID, platform ID, total number of samples, number of normal samples, number of OA samples, and grouping information. Differential expression was analyzed for each dataset, and based on the results of DEGs from each dataset, the RRA method identified a total of 202 significantly up-regulated and 49 significantly down-regulated DEGs. A heatmap displays the top 50 up-regulated and top 10 down-regulated DEGs (Figure 1A).

Table 3 Sample Characteristics of Included Datasets

GEO dataset	platform	samples	Total	normal	OA	group
GSE51588	GPL13497	Subchondral Bone	50	10	40	Train
GSE12021	GPL96	Synovium	19	9	10	Train
GSE55457	GPL96	Synovium	20	10	10	Train
GSE56409	GPL570	Synovium	22	11	11	Train
GSE114007	GPL11154/GPL18573	Cartilage	38	18	20	Train
GSE168505	GPL16791	Cartilage	7	3	4	Train
GSE169077	GPL96	Cartilage	11	5	6	Train
GSE55235	GPL96	Synovium	20	10	10	Test
GSE129147	GPL6947	Cartilage	40	7	33	Test
GSE48556	GPL6947	Blood	139	33	106	Test

3.2 Identification of DEGs using SVA Method

Box plots (Figures S1A-S1B) and PCA cluster plots (Figures S1C-S1D) present the gene expression matrix of the seven combined datasets before and after removing batch effects. The results indicate that, after normalization and batch effect adjustment, the batch effects between different datasets are not obvious, suggesting that the final combined dataset is suitable for subsequent analysis. The volcano plot and heatmap in Figures 1B-1C show the genetic differences between OA and normal controls in the combined dataset. Among these, 30 genes were found to be down-regulated and 64 genes were up-regulated. A total of 83 genes, identified by both RRA and SVA methods, were considered key genes for OA and retained for further analysis (Figure 1D).

3.3 Functional Enrichment and Pathway Analysis of Key Genes

We analyzed the GO annotations, KEGG pathways, and DO enrichment for these key genes to explore their potential biological information. GO analysis results indicate that, in the biological process (BP) category, the genes are involved in extracellular matrix organization, extracellular structure organization, and external encapsulating structure organization. In the cellular component (CC) category, they are associated with the collagen-containing extracellular matrix, complex of collagen trimers, and fibrillar collagen trimer. The enriched molecular function (MF) terms include extracellular matrix structural constituent, extracellular matrix structural constituent conferring tensile strength, and platelet-derived growth factor binding (Figures 1E,1H). KEGG enrichment results show that key genes are primarily enriched in the Relaxin signaling pathway, Protein digestion and absorption, AGE-RAGE signaling pathway in diabetic complications, and Rheumatoid arthritis (Figures 1F,1I). DO enrichment analysis results (Figures 1G,1J) indicate that the key genes are enriched in diseases including degenerative disc disease, connective tissue cancer, cell type benign neoplasm, Osteoarthritis, and musculoskeletal system cancer.

3.4 Construction of PPI Network for Key Genes

We used the key genes to construct a PPI network in STRING to understand the potential connections between proteins, with a minimum required interaction score of 0.4 and a PPI enrichment p-value <1.0e-16. This score indicates that the connections have a high level of confidence. To understand the expression of interacting proteins in OA, we scored the protein-protein interactions and combined the PPI network with scores greater than 0.4. We used Cytoscape software to visualize the PPI network constructed in STRING (Figure 2A). To understand the interactions between hub genes, we used the 'cytoHubba' algorithm to identify hub genes. We considered all computational methods in the cytoHubba algorithm ("MCC", "DMNC", "MNC", "Degree", "EPC", "BottleNeck", "EcCentricity", "Closeness", "Radiality", "Betweenness", "Stress", "ClusteringCoefficient") as hub genes, and only those identified by all methods were included as final hub genes. In the end, we identified 25 hub genes (Figure 2B): COL1A2, COL3A1, POSTN, COL11A1, CDH11, SULF1, FAP, MMP13, MMP1, OGN, THY1, JUN, CDH2, WNT5A, ATF3, CDKN1A, GAP43, PTN, DDIT4, STMN2, TOP2A, IRS2, IGFBP1, TUBB3, and SPOCK1. The interactions of these genes were visualized, with yellow representing up-regulated genes and pink representing down-regulated genes (Figure 2C). We also plotted the volcano plot and heatmap of the hub genes (Figures 2D-2E). GeneMANIA was used alongside PPI to evaluate the 25 hub genes and 20 interacting genes to predict relationships in terms of co-expression, shared protein domains, co-localization, and pathways (Figure 2F). The outer circle represents predicted genes, and the inner circle represents hub genes. Network analysis indicated that these genes are related to fibroblast proliferation, regulation of fibroblast proliferation, sensory organ morphogenesis, axonogenesis, extracellular matrix organization, banded collagen fibril, and collagen-containing extracellular matrix.

To further understand the function of hub genes in OA, we performed enrichment analysis on the hub genes. According to GO analysis, the biological processes (BP) enriched include extracellular matrix organization, extracellular structure organization, external encapsulating structure organization, axon development, and regeneration. The cellular components (CC) enriched include collagen-containing extracellular matrix, fibrillar collagen trimer, banded collagen fibril, endoplasmic reticulum lumen, and complex of collagen trimers. The molecular functions (MF) enriched include extracellular matrix structural constituent, extracellular matrix structural constituent conferring tensile strength, integrin binding, platelet-derived growth factor binding, and SMAD binding (Figures 2G-2H). KEGG analysis indicated that the hub genes are enriched in the Relaxin signaling pathway, IL-17 signaling pathway, AGE-RAGE signaling pathway in diabetic complications, Protein digestion and absorption, and PI3K-Akt signaling pathway (Figures 2I-2J).

3.5 Association between Hub Genes and TFs

Transcription factors (TFs) are involved in gene regulation. To explore the role of TFs, we used TRRUST to predict key TFs influencing OA through hub genes. The analysis of interactions between TFs and hub genes indicated that 49 TFs coordinated the regulation of 18 common DEGs (JUN is both a differentially expressed gene and a transcription factor), indicating a complex regulatory relationship (Figure 3A). The top-ranked (TFs), based on the complexity of gene regulation, include JUN, NFKB1, SP1, ETS1, AR, RELA, HDAC4, ATF2, ATF4, ESR1, EGR1, SP3, and TP53. Additionally, we validated the expression of several of these top-ranked TFs in OA (Figures 3B-3F). These findings reveal significant relationships between OA hub genes and TFs.

3.6 Identifying Diagnostic Biomarkers through Machine Learning

To identify key diagnostic biomarkers for osteoarthritis (OA), we conducted a thorough feature selection process in our study. Initially, we employed the `train` function from the caret package to predict the optimal machine learning algorithms. The dataset was divided into training and validation sets, with training set proportions of either 0.7 or 0.8. Among the six machine learning methods evaluated, XGBoost, SVM, and Lasso regression consistently ranked high in terms of residuals and root mean square error (Figures 4A, 4C), with areas under the ROC curve exceeding 0.9 (Figures 4B, 4D), indicating superior diagnostic efficacy and learning performance. Consequently, we selected XGBoost, SVM, and Lasso regression to screen for key diagnostic biomarkers for OA.

Using the XGBoost algorithm, we identified 23 candidate genes (Figure 4E). The SVM algorithm identified 23 key genes (Figures 4F-4G), and Lasso regression selected 26 candidate genes (Figures 4H-4I). By intersecting the feature genes identified by these three machine learning methods, we pinpointed 15 candidate genes: IRS2, ADM, SIK1, PTN, CX3CR1, WNT5A, IL21R, APOD, CRLF1, FKBP5, PNMAL1, NPR3, RARRES1, ASPN, and POSTN. Further intersecting these candidate genes with the hub genes, we identified IRS2, PTN, POSTN, and WNT5A as diagnostic biomarkers for OA (Figure 4J).

Next, we constructed box plots for these candidate genes, which showed differential expression in the dataset. We used ROC curves to evaluate the specificity and sensitivity of these four features in distinguishing OA from normal tissues in the test set. The diagnostic values of the four genes were as follows: IRS2 (AUC = 0.879, 95% CI: 0.826-0.927), PTN (AUC = 0.784, 95% CI: 0.707-0.852), POSTN (AUC = 0.668, 95% CI: 0.583-0.748), and WNT5A (AUC = 0.783, 95% CI: 0.706-0.857) (Figure 5A). Since the AUC of the POSTN gene in the test dataset was 0.668, which is less than 0.7, we ultimately selected IRS2, PTN, and WNT5A as the diagnostic biomarkers for OA.We then constructed a diagnostic nomogram for OA based on the training dataset to develop a clinically applicable diagnostic model for OA (Figure 5B). The clinical calibration curve (Figure 5C) and clinical decision curve (Figure 5D) of the model clearly demonstrated its high predictive ability for OA (AUC = 0.913, 95% CI: 0.866-0.953, Figure 5E).

3.7 Validation of Relevant Screening Genes

To better validate the clinical diagnostic capability of the candidate genes, we used the cartilage tissue dataset GSE129147, the synovial tissue dataset GSE55235, and the blood sample dataset from OA patients GSE48556 for verification, with sample sizes of 40, 20, and 139, respectively. We constructed violin plots of the candidate genes, which showed differential expression in the datasets (Figures 6A-6C). Additionally, we plotted ROC curves to evaluate the diagnostic value of each gene in different tissue samples (Figures 6D-6F).

Among these, PTN had lower diagnostic efficiency in cartilage samples, POSTN had poor diagnostic efficiency in synovial samples, and only IRS2 exhibited high differential expression (p<0.01) in all samples with good diagnostic value, indicating a high clinical value.

3.8 Experimental Validation of IRS2 Gene

To test the reliability of the candidate gene IRS2 in OA patients, we simulated OA by adding IL-1β to human SW1353 chondrocytes. We identified the protein level expression and relative mRNA expression level of the IRS2 gene in this sample through WB and qRT-PCR. Additionally, we checked OA-related phenotypes to ensure the accuracy of the OA simulation. The study data indicated that, compared to the control group, OA-related markers COL2A1 and SOX9 decreased, while MMP13 and COX2 significantly increased, consistent with OA characteristics. In the OA group, the protein and mRNA expression levels of the IRS2 gene were significantly reduced, showing statistical differences (Figures 7A-7C).

Osteoarthritis is a common degenerative joint disease that primarily affects weight-bearing joints such as the knees and hips. It is characterized by synovial inflammation, joint cartilage degradation, and subchondral bone alterations. Synovial inflammation leads to joint swelling and pain, while cartilage degradation results in direct bone contact, causing pain and joint stiffness. Subchondral bone sclerosis and osteophyte formation further exacerbate the progression of the disease^9,32-34. These pathological changes interact with each other, leading to joint dysfunction and a decrease in quality of life³⁵. With the global population aging, the number of elderly people suffering from osteoarthritis is increasing. Meanwhile, the underlying mechanisms of OA are not yet fully understood^36,37. Therefore, it is urgent to identify new targets related to the pathogenesis of OA to prevent its progression at an early stage.

In recent years, advances in sequencing technology and bioinformatics have made it possible to reanalyze previous osteoarthritis datasets^38,39, thereby identifying relevant mechanisms and pathogenic targets. The application of machine learning algorithms to biomedicine has greatly facilitated a better understanding and interpretation of high-dimensional sequence information. Although some studies have made progress in decoding the heterogeneity of OA, a comprehensive understanding of the disease remains limited. Outdated algorithms have restricted the reliability of clinical practice, but using advanced machine learning algorithms for comprehensive analysis of OA across multiple cohorts has become extremely urgent. ,

In this study, we explored and validated OA across multiple tissues using bioinformatics and machine learning algorithms, aiming to provide new insights into the mechanisms of OA from multiple perspectives. First, we identified 83 DEGs from multiple tissue datasets using RRA and SVA methods. Functional enrichment, pathway analysis, and disease ontology enrichment analysis of these DEGs revealed their involvement in extracellular matrix organization, extracellular structure organization, Relaxin signaling pathway, PI3K-Akt signaling pathway, and diseases such as connective tissue cancer and musculoskeletal system cancer.

Next, we constructed a PPI network and identified hub genes from the identified DEGs. Our methods identified 25 hub genes associated with OA. Functional enrichment and pathway analysis of these hub genes revealed their involvement in extracellular matrix organization, extracellular structure organization, Relaxin signaling pathway, IL-17 signaling pathway, AGE-RAGE signaling pathway in diabetic complications, and PI3K-Akt signaling pathway. These pathways and functions were highly consistent with those of the DEGs, demonstrating the representative value of the identified hub genes. Additionally, these pathways and enrichments are in line with previous studies^19,40-42.

To better identify OA characteristic biomarkers, we employed machine learning algorithms for screening. Initially, we divided the training dataset into training and test groups and evaluated the performance of six commonly used machine learning methods. We found that regardless of the proportion of the training set, XGBoost, SVM, and Lasso regression demonstrated superior performance in screening for OA biomarker features and exhibited higher diagnostic efficiency, significantly outperforming other machine learning methods.

Consequently, we selected these three algorithms for screening OA characteristic biomarkers, identifying 15 candidates: IRS2, ADM, SIK1, PTN, CX3CR1, WNT5A, IL21R, APOD, CRLF1, FKBP5, PNMAL1, NPR3, RARRES1, ASPN, and POSTN. By intersecting these genes with the identified hub genes, we pinpointed IRS2, WNT5A, PTN, and POSTN as OA diagnostic genes.

Although POSTN showed statistically significant differences in OA (p<0.01), its ROC curve was below 0.7, indicating poor diagnostic performance. Thus, we constructed an OA risk prediction model based on the remaining three genes. The constructed and tested risk score nomogram could distinguish OA from normal tissues, with an AUC value of 0.913, indicating high accuracy. We validated these diagnostic genes using external datasets of OA cartilage samples, synovial samples, and blood samples. In the cartilage dataset, PTN did not show significant expression differences and had a ROC curve area below 0.7. In the synovial tissue dataset, POSTN did not exhibit differential expression compared to the control group. However, in OA blood samples, IRS2 showed statistically significant expression differences and exhibited significant expression differences across all three tissue samples (p<0.01). Its ROC curve area was consistently above 0.7, demonstrating good diagnostic performance.Thus, we have reason to believe that IRS2 might be involved in the majority of OA progression processes, highlighting its potential as a powerful diagnostic biomarker for OA.

Periostin (POSTN) is a 90kDa matrix cell protein discovered in 1993⁴³, involved in the pathogenesis of various diseases including tumors, pulmonary fibrosis, and allergic diseases, with expression levels increasing as the disease progresses in most cases^43-45. POSTN interacts with extracellular matrix (ECM) proteins to regulate cell-matrix organization, leading to remodeling and fibrosis. The unique characteristics of POSTN can be attributed to highly complex signaling pathways that lead to increased POSTN production⁴³. In recent years, its association with orthopedic diseases has also become apparent⁴⁶. Numerous studies have confirmed that POSTN expression increases in OA cartilage^47-50, but its expression in OA synovial tissue is controversial. Tajika et al.⁵¹ found that it is highly expressed in OA synovial cells, while Mukundan Attur et al.⁵² found no statistically significant difference in its expression in degenerative OA. In OA blood, J C Rousseau et al.⁵³ found that serum POSTN is associated with the development of OA in women,but Sittisak Honsawek et al.⁵⁴ found no statistically significant difference in serum POSTN between OA and control groups. Recently, Tan et al.⁵⁵also showed that serum periostin levels are insufficient as clinical biomarkers for osteoarthritis. In our study, as shown in Figure 1A, POSTN was highly expressed in OA chondrocytes but appeared to be lowly expressed in synovial tissue. From Figure 6, we validated with external OA datasets and found that POSTN consistently showed high expression in cartilage but seemed to trend towards low expression in synovial tissue, without statistical significance, inconsistent with current research. This discrepancy requires further investigation through extensive related studies.

Pleiotrophin (PTN) is a member of the midkine family⁵⁶, a secreted heparin-binding peptide expressed during development in mesodermal and neuroectodermal cells but rarely in adult tissues⁵⁷. T. Pufe et al.⁵⁸ found that PTN is almost undetectable in normal cartilage and synovial cells but is highly expressed in OA, with significant expression in early and middle stages of OA but not in late stages. Furthermore, T. Pufe et al.⁵⁹ suggested that PTN might participate in the early onset and development of OA by stimulating the activation of AP-1 (activator protein-1) transcription factor and altering gene expression. In blood, studies by Fadda et al.⁶⁰ showed no significant difference in average PTN levels between OA patients and healthy controls, suggesting that while PTN may play an important role in OA, its potential as a disease biomarker requires larger-scale exploration and further research. In our study, as seen in Figure 6, PTN did not show statistical significance in external cartilage tissue datasets, possibly because our cartilage tissue dataset represented late-stage OA. However, its high expression in the OA synovial tissue dataset is consistent with previous findings. Combining these results, we believe that PTN might play a role in diagnosing early synovitis in OA.

WNT5A is a member of the Wnt protein family, which comprises a group of highly conserved signaling proteins that play crucial roles in embryonic development and various cellular processes such as cell migration, polarity, and differentiation. WNT5A operates in the non-canonical Wnt signaling pathway, particularly influencing cell movement and polarity rather than directly affecting cell proliferation⁶¹. The high expression of WNT5A in OA cartilage has been confirmed in numerous studies, and its mechanisms of action in OA have been widely explored^62-66, However, the impact of WNT5A in OA synovium is less understood. Lambert et al.⁶⁷ found high expression of WNT5A in OA synovium, regulated via the Wnt signaling pathway. This finding aligns with our conclusions. However, the expression of WNT5A in OA blood remains unstudied, necessitating further investigation.

Insulin Receptor Substrate 2 (IRS2) is a crucial member of the insulin receptor substrate family, playing a key role in insulin signaling and metabolic regulation⁶⁸. It is expressed in various cell types, including liver, muscle, and adipose tissues, and is involved in multiple physiological processes⁶⁹. The role of IRS2 in OA, however, has not been studied. In our research, using external cartilage, synovial, and OA blood sample datasets, we found that IRS2 consistently showed low expression. Given the absence of in vivo and in vitro studies on IRS2 in OA, we conducted in vitro experiments for validation. Through WB and qRT-PCR experiments, we confirmed that IRS2 is significantly downregulated in OA chondrocytes (p<0.001), consistent with our dataset validation results.

In summary, this study integrates bioinformatics analysis and machine learning algorithms to identify and validate four promising biomarkers: IRS2, WNT5A, PTN, and POSTN. POSTN can serve as a biomarker for OA cartilage, PTN shows potential for early diagnosis of OA, and WNT5A and IRS2 introduce new diagnostic perspectives for OA.

Data availability statement

Yes,The datasets used and/or analysed during the current study available from the corresponding author on reasonable request.

Author contributions

Guihao zheng, yulong Ouyang contributed equally to this work.

GHZ, YLOY, SLC, SX and BH were responsible for study concept and writing the article. GCS was responsible for reviewing and writing the article. All authors read and approved the final manuscript.

Competing interests

There were no competing interests.

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No competing interests reported.

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Identification and validation of novel characteristic genes based on multi-tissue osteoarthritis

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