Bioinformatics approach to explore the common pathogenic genes between lumbar disc degeneration and osteoarthritis

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Methods To better understand how IDD is related to OA, we used transcriptomic and single-cell analysis to look for molecular and cellular overlaps between the two diseases in this work. Two gene expression profiles (GSE124272 and GSE55235) from the Gene Expression Omnibus database are downloaded to detect common differentially expressed genes (DEGs) for OA and IDD diseases to find similar pathways, possible medicines, and regulatory networks. The single-cell transcriptome of synovial tissues sequenced from OA patients was analyzed for the immune characteristics of the shared DEGs. Results Based on RNA-seq datasets, 184 DEGs shared by these two diseases were found. 114 hub genes were then identified using degree and Matthew's correlation coefficient algorithms based on creating the Protein-protein interactions network. Interactions between transcription factors and shared hub genes, coregulatory networks between miRNAs and these hub genes, and protein-drug interactions were found. Moreover, scRNA-seq of cells of synovial tissues from OA patients revealed that 30 of these hub genes were highly enriched in the inflammatory macrophages (IL1B + Mac). Finally, CSF1 and S100A12 were identified as potential biomarkers by machine learning, and their area under the curve in IDD and OA is greater than 0.7 by the receiver operating characteristic curve analysis. Conclusions IDD might contribute to the advancement of OA by inciting cytokine storms, thereby enhancing comprehension and treatment strategies for OA patients accompanied by IDD. Moreover, the identified candidate drugs, DEG-regulated networks, and potential diagnostic genes (CSF1 and S100A12) offer promising therapeutic avenues for addressing both conditions. Osteoarthritis lumbar disc degeneration disease differentially expressed genes hub genes immune response drug treatment Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Introduction Osteoarthritis (OA), one of the most prevalent forms of arthritis in elderly adults, involves several joints and harms joint function, mainly characterized by synovial inflammation, cartilage degeneration, and reduced joint function[ 1 ]. Its clinical manifestations include local pain and limited joint movement[ 2 ]. Besides, patients with OA may experience symptoms such as swelling, pain, or stiffness, which can harm their quality of life[ 3 ]. Women over the age of 55 and males over the age of 65 have an increased risk of developing OA[ 4 ]. OA has been associated with an increased incidence rate in the past few decades due to the aging population and obesity[ 5 ]. According to a recent study, there will be approximately 400 million cases of OA in China by 2030[ 6 ]. However, the etiology and pathogenesis of the disease are still unknown. It is urgent to explore the potential mechanisms responsible for OA development, which is beneficial for developing novel and effective strategies. Intervertebral disc degeneration (IDD) is a degenerative disease recognized as the primary cause of low back pain[ 7 ], and affects 70%-85% of the global population. This disease is distinguished by biochemical alterations in the nucleus pulposus, extracellular matrix degradation in the nucleus pulposus, and exterior damage to the fibrous ring accompanied by increasing fibrosis[ 8 ]. Research reports suggest that inflammation and signaling substances may play important roles in the occurrence and development of intervertebral disc degeneration, such as interleukin-1 (IL-1), tumor necrosis factor-alpha (TNF), nitric oxide, matrix metalloproteinases and metalloproteinases[ 9 ]. In addition, during the progression of IDD, macrophage infiltration leads to the release of many inflammatory factors, forming an inflammatory storm[ 10 ]. Meanwhile, OA is a chronic inflammatory disease, and the release of inflammatory factors has a memorable role in the process of OA. For example, enhanced IL-1 and TNF levels are related to the increased risk of OA[ 11 , 12 ]. Besides, significantly increased levels of inflammatory cytokines, such as IL-1 and IL-6, were found in OA patients' synovial fluid, synovium, subchondral bone, and cartilage [ 13 ]. Inhibition of IL-1 and TNF can significantly reduce cartilage degeneration and inflammatory response in experimental OA[ 14 , 15 ]. Meanwhile, clinical studies have shown a significant correlation between hip OA and endplate degeneration at L1, L3, and L5 levels[ 16 ], and degenerative diseases of the upper lumbar spine can induce the occurrence of primary unilateral knee osteoarthritis[ 17 ]. Therefore, IDD and OA may be interdependent and potentially have considerable pathological and physiological overlap. However, so far, the impact of IDD on the severity and progression of OA is still unclear. Given this, exploring the potential mechanisms underlying the impact of lumbar disc degeneration on the progression of osteoarthritis (OA) is crucial for improving the treatment of OA patients. By comprehensively deciphering the shared differentially expressed genes between these two diseases and identifying potential diagnostic biomarkers, a foundation can be established for more effective treatment strategies and ultimately improve clinical outcomes. Here, we examined the overlapping molecular and cellular intersections of gene expression in IDDD and OA, utilizing transcriptome and single-cell sequencing data. Our investigation suggests that IDDD may exacerbate OA by an excessive cytokine storm. These findings have significant implications for advancing our comprehension and treatment strategies of OA patients with concurrent IDD. The entire workflow diagram in this study is shown in Fig. 1 . Material and Methods Dataset collection and processing Synovial tissue microarray dataset (GSE55235), including 10 normal samples and 10 Osteoarthritis (OA) samples, was processed using the Affymetrix GPL96 platform, and GSE124272 [the database consists of 16 whole blood samples from 8 patients with Intervertebral disc degeneration (IDD) and 8 healthy controls] was analyzed using the platform of Agilent GPL21185. In addition, a validated dataset, GSE55457, was analyzed using the GPL96 Affymetrix U133A Array. GSE55457's gene expression profile included 10 OA patients and 10 healthy controls. GSE150408 contains the whole blood RNA-seq transcriptome data of 17 IDD patients and 17 healthy volunteers and was analyzed using the Agilent GPL21185 platform. GSE176205, containing 3 controls and 6 IDD tissue samples, was analyzed using GPL20301. All these datasets were obtained from the Gene Expression Omnibus (GEO) database ( http://www.ncbi.nlm.nih.gov/geo ), and all four datasets of data's gene expression matrix files were created with the par pl package of Perl from raw RNA-seq data. Identification of common differentially expressed genes (DEGs) between IDD and OA First, use R software (version 4.2.1) to read the expression matrices of datasets GSE1919, GSE55235, and GSE51588 for batch correction[ 18 ] and normalization[ 19 ]. Subsequently, the "limma" R package was performed to identify DEGs between IDD patients and controls or OA patients and controls. Meanwhile, genes with |log2FC | > 0.585 and p-value < 0.05 were screened to be DEGs[ 20 ]. Volcano plots and DEG expression heatmaps were constructed using the "ggplot2" and "heatmap" packages. Besides, the common DEGs of GSE124272 and GSE55457 were acquired based on the Jvenn, a web-based Venn diagram tool. Gene ontology (GO) and pathway enrichment analysis The "clusterProfile" package[ 21 ], which is a widely used general enrichment tool for interpreting omics data, was next performed for analyzing GO and Disease Ontology (DO) to explore further the potential molecular mechanism of these shared DEGs and their link to disease. Disease ontology is a unified cross-species disease annotation[ 22 ]. Meanwhile, Enrichr, an online application ( https://maayanlab.cloud/Enrichr/ ), was used to discover the major signaling pathways of these common DEGs, which included Kyoto Encyclopaedia of Genes and Genomes (KEGG) Pathways, WikiPathways, Reactome, and Hallmark. We used a p -value of 0.05 as the cut-off for significance[ 23 ]. Protein-protein interactions (PPI) network construction and hub gene identification Cytoscape (v3.9.1), which is a software tool used for visualizing and analyzing biological molecular interaction networks, was used to generate a visual representation of the PPI networks based on gene interactions combined with a total score of > 0.4[ 24 ], which were derived from the STRING database ( https://string-db.org/ ). The Cytoscape plugin CytoHubba, a crucial tool for analyzing and identifying the center of biological networks, was used to determine the degree of each node. Hub genes were identified as those with a degree score (highly connected nodes with a degree) of > 10[ 25 , 26 ]. Then, two algorithms of the cytoHubba plugin, including degree and Matthew's correlation coefficient (MCC) algorithms, were employed to identify shared hub genes. Recognition of TFs and miRNA interact with shared hub genes Transcription factors (TFs) are proteins that attach to specific genes, exerting control over the rate of genetic information transcription. As a result, they play a pivotal role in facilitating a comprehensive understanding of molecular processes[ 27 ]. Using the 'NetworkAnalyst' function to find topologically plausible TFs derived from the JASPAR database that tend to bind to common DEGs opens up new avenues of investigation into the TFs regulating common hub genes. Six different taxonomic groupings are represented in the publicly available JASPAR database of TF binding profiles ( https://jaspar.genereg.net/ ). Meta-analysis of gene expression data using the PPI network is performed in NetworkAnalyst to study biological mechanisms in greater depth[ 28 ]. Additionally, microRNAs (miRNAs), a class of small non-coding RNAs that bind to the transcripts of widely used DEG, thus altering protein expression, were identified by doing a target gene-miRNA interaction analysis. MirTarBase ( https://mirtarbase.cuhk.edu.cn/~miRTarBase/ ) is the major database of miRNA-target gene interactions. Here, from the interaction of miRNAs-gene interaction based on the NetworkAnalyst platform, we extracted miRNAs that interact with shared hub genes focused on topological analysis from the mirTarBase database. NetworkAnalyst simplifies the exploration of complex datasets for researchers, helping to identify biological features and functions and formulating meaningful biological hypotheses[ 29 ]. The interaction network of TFs and miRNAs was visualized using Cytoscape. Download and Analysis of scRNA-seq data The single-cell raw matrix data of synovial tissues from GSE152805 were acquired from the GEO database. We then employed the Seurat package (version 4.1.2) to process the raw count matrix (UMI counts for each sample). The low-quality cells, defined as those with fewer than 200 genes per cell, less than 10 cells per gene, and more than 15% mitochondrial genes, were excluded. Following this, we performed normalization using the 'NormalizeData' function based on the corrected expression matrix. After data normalization, we scaled the data by applying the 'FindVariableGenes' function, focusing on the expression of 2000 highly variable genes (HVGs). Principal component analysis of these HVGs was executed using the 'RunPCA' function, and we eliminated batch effects between different samples using the 'harmony' function. The purpose of identifying cell types across all cell populations is to set the resolution to 1.0. Subsequently, we employed the "RunTSNE" function to project the combined cells into a two-dimensional space. Following this, we utilized the "Dimplot" function to visualize the cell clusters, concluding with their annotation based on known markers. Assessment of Potential drugs Understanding the features of diseases requires careful consideration of protein-drug interactions (PDIs) or drug molecule identification. As a result, we forecasted PDIs using the web application Enrichr. First, based on the shared hub genes, drug compounds were discovered utilizing the Drug Signatures database (DSigDB) via the Enrichr database based on the common hub genes of OA and IDD. Enrichr is a popular online platform that hosts a vast collection of diverse gene set libraries, enabling the exploration of gene set enrichment across the entire genome ( https://maayanlab.cloud/Enrichr/ ). DSigDB ( http://dsigdb.tanlab.org/DSigDBv1.0/ ) is a data repository for identifying targeted therapeutic compounds connected to these hub genes[ 30 ]. The database consists of 22,527 gene sets, and one accessible way to access the DSigDB database is through Enrichr under the Disease/Drug functionality category. Immune Cell Infiltration and Single-sample Gene Set Enrichment Analysis (ssGSEA) To explore the correlation between these common DEGs and immune cell infiltration characteristics in OA, infiltration levels of different immune cells in OA were evaluated using the "CIBERSORT" package, and the relative proportions of 22 immune cells were estimated via iterations (1000 times). The "CIBERSORT" package utilizes linear support vector regression on gene expression profiles, allowing for the estimation of immune cell quantities in samples using RNA sequencing data[ 31 ]. Subsequently, the Wilcoxon test was run to calculate the differences between the OA disease group and the normal control group in each immune cell population. Then, to further validate the activity of these DEGs during OA progression, the ssGSEA algorithm was implemented to elucidate the enrichment scores of differential expression genes quantitatively shared in each sample[ 32 ]. The Gene Set Variation Analysis (GSVA) R package (Version 1.26.0) was employed to calculate normalized enrichment scores for gene sets. Candidate diagnosis marker identification Then, to identify the diagnostic markers in OA patients, we used two machine learning algorithms (LASSO logistic regression and the SVM algorithm). As a dimensionality reduction method, compared to regression analysis, LASSO regression demonstrates superior performance when evaluating high-dimensional data. It employs regularization to enhance prediction accuracy. Here, the "Glmnet" software package for LASSO regression analysis is used to identify genes significantly associated with the discrimination of OA and healthy samples. Meanwhile, as a supervised machine learning method for support vectors, support vector machines (SVM) rank features recursively to prevent overfitting and find the optimal variable by deleting the feature vectors generated by the support vector machine[ 33 ]. The SVM classifier from the "e1071" R package was adopted for screened biomarkers in diagnosing OA patients. Receiver Operating Characteristic (ROC) analysis of these feature genes was conducted in RStudio using the pROC package. Area under the curve (AUC), with a range of 0.7 to 1, typically has good predictive performance[ 34 ]. Therefore, feature genes with an AUC value of > 0.7 are considered diagnostic for OA. GSEA function enrichment and immune cell infiltration analysis of CSF1 and S100A12 in OA The "limma" and "cluster Profiler" packages in R were used to carry out gene set enrichment analysis (GSEA) at a threshold of p < 0.05 to identify differences in signaling pathways of CSF1 and S100A12 (grouped by median expression level). GSEA is for enrichment analysis of pathways based on whole-genome expression profiles[ 35 ]. The gene sets of c2.cp.kegg.v7.4.symbols were derived from the MSigDB database for running the GSEA analysis. To determine the interaction relationship among two effective biomarkers (CSF1 and S100A12) and differential immune cells and inflammatory factors, the "limma" and "vioplot" packages in R were used to compare the relative proportions of the immune cells between control and OA patients, and the "correlation analysis" was run to perform Pearson correlation analysis for these two genes (CSF1and S100A12) and immune cells and inflammatory factors. Results 184 shared DEGs were identified between IDD and OA To examine the potential influences of IDD on OA, we compared the differential gene expression in the whole blood samples of 8 intervertebral disc degeneration patients and 8 healthy intervertebral disc patients from dataset GSE124272. Meanwhile, we analyzed the differential gene expression between 10 osteoarthritides (OA) patients' pathological synovial tissues and 10 healthy control tissues using GEO data GSE55235. Subsequently, 2381 DEGs (986 upregulated and 1395 down-regulated) in the IDD dataset and 2361 DEGs (938 upregulated and 1423 down-regulated) in the OA dataset were identified using "limma" R package with a threshold of p -value < 0.05 and |log2FC| ≥ 0.585. Volcano plots and heatmaps are commonly used to visualize DEGs for IDD and OA (Supplementary Fig. 1A-D). Next, 184 common DEGs between IDD and OA datasets were identified using the Venn diagram (Fig. 2 ). Hence, these findings can be inferred that there exists a mutual linkage between these two diseases, as they exhibit a shared presence of multiple common genes (184 common DEGs); these shared DEGs were used in further analysis to delve deeper into exploring the potential connections between the two diseases. GO and pathway enrichment analysis To explore the potential biological function and diseases related to these common DEGs, the "clusterProfile" package was performed to carry out GO and DO enrichment analysis. The top 10 enriched GO terms for these shared DEGs are shown in Supplementary Fig. 2A. The GO analysis showed that shared DEGs were remarkably enriched in neutrophil activation involved in immune response, neutrophil-mediated immunity, and negative regulation of inflammatory response; nuclear chromosome, phagocytic vesicle lumen and phagocytic vesicle; transition metal ion binding, protein phosphatase binding and zinc ion binding (Supplementary Fig. 2A and Supplementary Table 1). Meanwhile, the top 40 enriched DO terms for these shared DEGs are shown in the Fig. 3 B. The findings indicated that these DEGs exhibited enrichment in diseases such as hip osteoarthritis, autoimmune diseases, systemic lupus erythematosus, breast cancer, rheumatoid arthritis, and other related illnesses (Supplementary Fig. 2B and Supplementary Table 2). Subsequently, the four global databases were used further to investigate the possible pathways of these common DEGs. The results showed that these common DEGs were significantly related to the MAPK signaling pathway, T cell receptor signaling pathway, Rap1 signaling pathway, immune system, innate immune system, and VEGFA-VEGFR2 signaling pathway (Supplementary Fig. 3A-B and Supplementary Table 3, 4). These results indicate that these DEGs may influence the occurrence of OA through pathways associated with inflammation and immune response. Hub gene classification and construction of regulatory networks The PPI network of these shared DEGs was created by using the STRING database ( https://string-db.org/ ), which is a database for predicting functional associations between proteins, and Cytoscape (v3.7.2) visualized these DEGs with a combined score ≥ 0.4 as the cut-off criteria. Figure 3 A depicts the PPI network of common DEGs consisting of 184 nodes and 912 edges. The degree and MCC algorithms of the cytoHubba plugin in Cytoscape were utilized to calculate the protein-protein interaction (PPI) network of 181 DEGs. Among 184 common DEGs, 131 hub genes were identified with a threshold degree of > 10 based on Degree algorithms of the cytoHubba plugin (Fig. 3 B and Supplementary Table 5). A Venn diagram was then constructed using the intersection of hub genes obtained from these two algorithms, and 114 shared hub genes were identified (Fig. 3 C). Additionally, to ascertain the primary alterations at the transcriptional level and delve further into key protein regulatory molecules or common DEGs, we employed a network-based approach to decode the regulatory transcription factors (TFs) and miRNAs. Figure 4 A depicts the network of TF regulators of shared DEGs, and Fig. 4 B depicts the interaction of miRNAs with shared DEGs. We identified 36 transcription factors (TFs) and 29 post-transcriptional regulators (miRNAs) regulated with more than one shared DEGs by analyzing the interaction network between transcription factor genes and miRNA genes. These results essentially indicate a strong interplay between them. scRNA-seq reveals the accumulation of common 22 IRGs in inflammatory macrophages of OA patients To clarify the impact of IDD on the process of OA, we subsequently explored the immune characteristics of these common hub genes in OA patients. The scRNA-seq dataset of OA patients (including three OA synovial samples) was obtained from the dataset GSE152805. A total of 9822 cells were acquired, and these cells were divided into 17 clusters based on unsupervised graph-based clustering (Fig. 5 A). These clusters were then defined as synovial subintimal fibroblasts (SSF), synovial intimal fibroblasts (SIF), macrophage, DC, endothelial cell (EC), smooth muscle cell (SMC), Mast Cell, proliferating immune cell (ProIC), T cell, and B cell based on the known cell markers (Fig. 5 B-E). Subsequently, we analyzed the expression of these shared hub genes in different cell types within OA patients. Among their hub genes were 30 genes highly expressed in macrophages, compared with other cell types (Fig. 5 F). To delve deeper into the potential roles of these 29 genes in macrophages of OA patients, we conducted a subgroup analysis by extracting macrophages. Firstly, we performed unsupervised graph-based clustering of macrophages, identifying a total of 10 clusters (Fig. 6 A), and then these clusters were defined as FOLR2 + macrophages (FOLR2 + Mac) and IL1B + macrophages (IL1B + Mac) according to specific gene signatures (Fig. 6 B-E). Subsequently, we further explored the distribution of these 30 genes in the subgroups of macrophages. Interestingly, we noticed that almost all these genes were highly expressed in IL1B + Mac compared with the FOLR2 + Mac except HCRT (Fig. 6 F). Simultaneously, we further observed that these genes were highly expressed in IDD patients (blood and tissue samples) compared to normal samples and also found to be highly expressed in OA patients (Supplementary Fig. 4A-B). These genes were highly expressed in IL1B + Mac and were defined as inflammation-related DEGs (IRGs). Subsequently, to clarify the characteristic changes of these IRGs in OA immune cell infiltration. We then utilized single-sample Gene Set Enrichment Analysis (ssGSEA) to calculate each sample's scores based on these IRGs' expressions (Supplementary Fig. 5A). The results indicated that the ssGSEA score (DEGs signature) is significantly higher in the OA disease samples than in the control samples (Supplementary Fig. 5B). Besides, this score also shows a significant positive correlation with M1 macrophages, follicular helper T cells, Eosinophils and Neutrophils (Supplementary Fig. 5C-G). Simultaneously, we further assessed the associations between several pro-inflammatory cytokines (IL1B, IL2, IL6, IL12B and TNF) in OA and DEGs signature. The results reveal a significant positive correlation between these pro-inflammatory cytokines and the DEG signature (Supplementary Fig. 6). To conclude, these results indicated that the IRGs, which were highly expressed by IL1B + Mac, may be the main contributors to the cytokine storm in OA disease, as well as IDD may also promote the progression of OA through cytokine storms. Determination and verification of diagnostic markers in OA patients by machine learning Finally, to explore potential biomarkers for OA, we further screened diagnostic genes based on these IRGs of the OA datasets using LASSO regression. As shown in Fig. 7 A, LASSO regression identified 13 variables that were determined among these common hub genes as characteristic genes for OA. Meanwhile, other arithmetics (SVM-RFE method) were utilized to explore OA's diagnostic biomarkers. The data showed that 22 genes were identified as signature genes by the SVM-RFE algorithm (Fig. 7 B). Then, regarding the results of the two algorithms, after taking the intersection. In total, 2 overlapping genes (Colony stimulating factor 1, CSF1 and S100 calcium-binding protein A12, S100A12) were selected between those two arithmetics via Venn diagrams (Fig. 7 C). Additionally, Supplementary Table S6 displays the shared hub genes discovered by the LASSO regression and SVM-RFE algorithm. Subsequently, we further validated the expression level of these two genes between OA patients and controls. The results showed that these two genes were significantly upregulated in the OA patients compared with control samples (Fig. 7 D). Besides, ROC analysis confirmed that these two genes exhibited strong diagnosis stability, and the AUC value was 0.820 and 0.870, respectively (Fig. 7 E). Thus, these two genes may be critical diagnostic markers involved in OA progression. Meanwhile, these two genes were also significantly more expressed in the whole blood and tissue samples of IDD patients than in control samples (Fig. 7 F), and the AUC value of these two genes also revealed an excellent diagnostic value for LDDD patients (Fig. 7 G). Moreover, the expression and AUC value of these two potential genes were also confirmed in other OA datasets (GSE55457) and IDD external independent datasets (GSE150408) (Supplementary Fig. 7A-D). In conclusion, these results demonstrated that these two genes may be potential targets for treating and diagnosing OA and IDD. Correlation between CSF1 and S100A12 expression and immune cell infiltration across OA To further investigate the potential role of these two genes (CSF1 and S100A12) in OA, GSEA was used to screen the gene sets enriched in different expression levels of these two genes. As shown in Supplementary Fig. 8A-B, compared with the group of CSF1 low expression, the Calcium signaling pathway, Focal-adhesion, and ECM-receptor interaction were significantly enriched in the CSF1 high expression group. Chemokine signaling pathway, Focal-adhesion and Cytokine-cytokine receptor interaction were more involved in the S100A12 high expression group than in the S100A12 low expression group. Therefore, these findings showed that these two genes may influence the occurrence of OA through pathways associated with chemokine and cytokine-cytokine receptor interaction. The study mentioned above reveals a significant enrichment of inflammation-related infiltrating cells (M1 macrophages and neutrophils) in patients with OA. Therefore, to further investigate whether these two prognostic remarkers are associated with immune cell infiltration during the development of OA, Pearson correlation analysis was performed on these two genes (CSF1 and S100A12) and immune cells, and the results showed that the expression of these two genes was appreciably positively correlated with the MO and M1 macrophages (Fig. 8 A-B). Meanwhile, we further explored the relationship between pro-inflammatory cytokines and these two genes in OA. We observed a significant positive correlation between the expression of these pro-inflammatory cytokines and CSF1 and S100A12 (Fig. 8 C). Thus, the above results suggested that these two genes may promote the progression of OA through inflammatory-related responses. Identification of candidate drugs Assessment of PDIs is essential to understand the structural features recommended for receptor sensitivity. In the aspects of common DEGs as potential drug targets in OA and IDD, the top 15 candidate therapeutic compounds were identified by Enrichr based on the transcriptome signatures from the DSigDB database. These chemical compounds are extracted based on their p-value. These potential drugs are suggested for the common hub genes. These drugs could be chemical compounds that are used to treat both diseases. The DSigDB database's list of effective medications for common hub genes is presented in Table 1 . Table 1 List of the top 15 suggested drugs for OA. Term p- value Counts Chemical Formula VALPROIC ACID 0.001155287 64 C 8 H 16 O 2 Retinoic acid 2.99E-06 46 C 20 H 28 O 2 estradiol 1.24E-05 45 C 18 H 24 O 2 benzo[a]pyrene 2.43E-04 42 C 20 H 12 Tetradioxin 2.97E-05 40 C 12 H 4 C l4 O 2 trichostatin A 5.37E-05 38 C 17 H 22 N 2 O 3 AFLATOXIN B1 3.89E-04 32 C 17 H 12 O 6 Triclocarban 5.92E-04 29 C 13 H 9 C l3 N 2 O COPPER 1.87E-07 28 Cu resveratrol 2.35E-07 27 C 14 H 12 O 3 calcitriol 1.11E-05 27 C 27 H 44 O 3 hydrogen peroxide 0.001913058 27 H 2 O 2 Bisphenol A 7.66E-09 26 C 15 H 16 O 2 progesterone 2.21E-05 26 C 21 H 30 O 2 Decitabine 4.76E-04 22 C 8 H 12 N 4 O 4 Discussion OA, a widespread chronic joint disease, affects many individuals worldwide[ 36 ]. OA has gradually increased healthcare and social expenditures as the primary cause of impairment in older persons [ 37 ]. Although multiple diagnostic and therapeutic approaches are available for OA, the clinical outcomes are still unsatisfactory. IDD is a degenerative spine disease that causes chronic lower back pain and motor disturbance[ 38 ]. In more severe stages, loss of joint space, subchondral sclerosis, and osteophytes, similar to OA in the articular joint, accompany intervertebral disc degeneration[ 39 ]. Thus, IDD may accelerate the progression of OA. Nevertheless, there are few reports illustrating the interactions between the two diseases. Here, we employed various bioinformatics analysis methods to reveal the potential molecular mechanism of IDD in the pathogenesis of OA and provide a possible basis for future targeted and preventive treatment. Here, 184 common DEGs were identified between OA and IDD. After constructing the PPI network of shared DEGs, 114 common hub genes were identified based on the degree and MCC algorithm of the cytoHubba plugin in Cytoscape. Subsequently, 114 identified shared hub genes were evaluated by functional enrichment and pathway analysis. The enrichment analysis results showed that these common hub genes were significantly associated with the MAPK signaling pathway, T cell receptor signaling pathway, Rap1 signaling pathway, immune response, neutrophil-mediated immunity, and negative regulation of inflammatory response. Existing research indicates an inherent connection between the MAPK signaling pathway and osteoarthritis (OA) advancement. An imbalance in the MAPK pathway expedites inflammatory reactions, releasing a multitude of cartilage matrix-degrading enzymes and intensifying cartilage degeneration[ 40 ]. The Rap1 signaling pathway is an essential component of pathological osteoclast resorption, contributing to the progression of arthritis[ 41 ]. Furthermore, research has reported that T-cell signaling mediates the onset of various inflammatory diseases, such as inflammatory bowel disease and autoimmune arthritis[ 42 ]. Therefore, these data indicated that these common DEG-related pathways are closely associated with the inflammatory response. Previous research has shown that OA, as an inflammatory disease, can be exacerbated by a dysregulated inflammatory storm[ 43 ], and large amounts of pro-inflammatory cytokines can be detected in patients with IDD[ 44 ]. To investigate the distribution of these DEGs across various immune cell types, we delved deeper into the characteristics of single-cell atlases derived from OA tissue samples. Our data proved that 30 of these hub genes were broadly enriched in macrophages compared to other cell types, and subgroup analysis of macrophages also revealed that the IL1B + macrophage subtype was a prominent source of expression for these 30 genes. Meanwhile, we further observed that these genes were highly expressed in IDD and OA patients compared to control samples. Subsequently, based on the expression of these 30 genes, the score derived from ssGSEA calculations for these gene sets is to be constructed to explore further the characteristic changes of these genes in OA immune cell infiltration. The ssGSEA score of these gene sets was significantly higher in the OA disease group than in the control group. These scores also positively correlated with M1 macrophages, eosinophils, and neutrophils. Meanwhile, we further assessed the associations between several pro-inflammatory cytokines in OA and these scores. The results showed a significant positive correlation between these pro-inflammatory cytokines and these scores. Polarization of synovial macrophages towards M1 exacerbates the development of OA[ 45 ]. Additionally, research has indicated that neutrophil elastase (NE) contributes to tissue degeneration, osteophyte formation, and the release of inflammatory cytokines and chemokines, thereby participating in the progression of OA[ 46 ]. Therefore, based on these findings, it can be inferred that the highly expressed IRGs in IL1B + macrophages could be the primary drivers of the cytokine storm in OA. IDD might also contribute to OA progression through cytokine storms. Subsequently, we conducted feature selection utilizing two distinct algorithms to delve deeper into potential biomarkers for osteoarthritis (OA). The results showed that two hub genes (CSF1 and S100A12) were identified as feature genes using LASSO regression analysis and the SVM-RFE method. CSF1, the protein encoded by this gene, is a cytokine that controls macrophage production, differentiation, and function. CSF regulates the development, proliferation, differentiation and activation of myeloid cells in health and disease[ 47 ]. In addition, it has been reported that CSF-1 can promote the survival, proliferation and differentiation of bone marrow cells (including monocytes, macrophages and osteoclast) by binding to the tyrosine kinase receptor CSF1R[ 48 ]. Exogenous administration of CSF-1 exacerbates the incidence and severity of OA[ 49 ]. The protein encoded by is a member of the S100 protein family containing two EF-hand calcium-binding motifs. S100 protein has also been proven to have important extracellular pro-inflammatory effects and cytokine-like activity[ 51 ]. Clinical research shows serum S100A12 expression is elevated in patients with OA [ 52 ]. The change in S100A12 expression level in synovial fluid can reflect the clinical severity of patients with primary knee osteoarthritis[ 53 ]. Here, our findings revealed significant overexpression of CSF1 and S100A12 in both the test and validation sets among IDD and OA patients and the OA patients with high CSF1 and S100A12 levels had positively correlated with M1 macrophages. Meanwhile, the results of the correlation analysis indicate a significant positive correlation between these two genes and pro-inflammatory cytokines in the progression of OA. Experimental studies also demonstrated that blocking the CSF1R can reduce inflammation in human and mouse models of rheumatoid arthritis[ 50 ]. Furthermore, clinical case study results indicate that S100A12 in the serum and synovial fluid (SF) of patients with OA may be an independent factor for predicting the risk of this condition[ 53 ]. Therefore, these two genes may serve as potential diagnostic biomarkers for OA patients and could be involved in the pathogenesis of OA through inflammatory responses. Transcription factors (TFs) govern the proportion of transcriptional processes, while miRNAs play a crucial role in gene regulation at the post-transcriptional level and in RNA silencing. Both TFs and miRNAs are of significant importance in understanding disease progression. Then, a study of the interactions between TFs-genes and miRNAs-genes was done to determine which regulators control the transcription and expression of common hub genes. 36 TFs and 29 miRNAs were found to have substantial interactions with common hub genes. For example, YY1[ 54 ] and NFKB1[ 55 ] are associated with the regulation and progression of OA diseases. Furthermore, some miRNAs, such as miR-335-5p[ 56 ] and miR-218-5p[ 57 ], participated in the progression of OA. In recent years, several chemical agents and drugs have been utilized as potential therapeutic approaches to OA. Local nonsteroidal anti-inflammatory drugs (NSAIDs) such as lurbiprofen cataplasms and loxoprofen sodium cataplasm have effectively treated OA[ 58 ]. However, the incidence rate of OA disease has increased significantly, so it is urgent to develop new drugs to treat OA. Based on shared hub genes, some potential drugs and compounds targeting OA and IDD have been identified using database, such as valproic acid (VPA), estradiol, and trichostatin A. VPA is a flavonoid compound extracted from herbaceous plants and is a first-line drug for treating epilepsy and mania[ 59 ]. VPA has been proven to have multiple therapeutic effects, including anticancer, neuroprotective, differentiation, and neurodegenerative activity[ 60 ]. Besides, studies found that VPA was identified as a putative agent associated with OA progression and protected chondrocytes from damage caused by OA stimulation[ 61 ]. Estradiol is a well-known homeostatic regulator of the bone and immune system and can also control exercise ability and pain sensitivity[ 62 ]. In addition, experimental studies have confirmed that the physiological level of estradiol limits the progression of OA in mice[ 63 ]. Trichostatin A (TSA) is a non-selective histone deacetylase inhibitor that inhibits OA and rheumatoid arthritis progression by inhibiting the expression of MMP[ 64 ]. So, these chemical compounds might be good choices for drugs to treat OA. However, there are still some limitations to this study. First, the sample size of this study is small, which may affect the reliability of the conclusions. In addition, the results of this study are all based on the analysis of database data. The necessary support of experimental evidence is lacking. CSF1 and S100A12 need further validation through cell experiments, animal models and clinical samples. Additionally, these drugs need to undergo chemical experiments for further confirmation. Despite these limitations, the results of the current study may provide potential directions for future research. Our study elucidates the potential molecular connections between IDD and OA at the cellular and molecular levels through comprehensive transcriptome and single-cell sequencing data analysis. Our research has identified that IDDD exacerbates the severity of OA through an excessive cytokine storm. Additionally, we identified two potential diagnostic biomarkers for these diseases. By expanding our understanding of these conditions, our research provides potential directions for further investigating the molecular mechanisms of OA. It may lead to more effective therapeutic strategies and improved clinical outcomes for OA patients. Abbreviations Osteoarthritis (OA) lumbar disc degeneration disease (IDD) differentially expressed genes (DEGs) interleukin-1 (IL-1) tumor necrosis factor-alpha (TNF) L1, L3, and L5 levels Gene Expression Omnibus (GEO) Gene ontology (GO) Disease Ontology (DO) Kyoto Encyclopaedia of Genes and Genomes (KEGG) Protein-protein interactions (PPI) Matthew's correlation coefficient (MCC) Transcription factors (TFs) microRNAs (miRNAs) protein-drug interactions (PDIs) Drug Signatures database (DSigDB) Single-sample Gene Set Enrichment Analysis (ssGSEA) Gene Set Variation Analysis (GSVA) support vector machines (SVM) Receiver Operating Characteristic (ROC) Area under the curve (AUC) gene set enrichment analysis (GSEA) transcription factors (TFs) protein-protein interaction (PPI) subintimal fibroblasts (SSF) synovial intimal fibroblasts (SIF) endothelial cell (EC) smooth muscle cell (SMC) proliferating immune cell (ProIC) FOLR2 + macrophages (FOLR2 + Mac) IL1B + macrophages (IL1B + Mac) inflammation-related DEGs (IRGs) ingle-sample Gene Set Enrichment Analysis (ssGSEA) Declarations Ethics approval and consent to participate Not applicable. Consent for publication Not applicable. Funding This work was supported by the National Natural Science Foundation of China (82371751). Data availability statement All data needed to evaluate the conclusions in the paper are presented in the main manuscript and the Supplementary Information. Acknowledgments Not applicable. Authors' contributions WSC and ZMY designed the study. HGL conducted and interpreted the bioinformatics analysis. WGZ and SYY prepared the draft of the manuscript. All authors revised and approved the final manuscript Competing interests The authors declare no competing interests. References Kraus VB, Blanco FJ, Englund M, Karsdal MA, Lohmander LS. Call for standardized definitions of osteoarthritis and risk stratification for clinical trials and clinical use. Osteoarthritis Cartilage. 2015;23(8):1233–41. Winter AR, Collins JE, Katz JN. The likelihood of total knee arthroplasty following arthroscopic surgery for osteoarthritis: a systematic review. 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Supplementary Files SupplementaryFigures.docx Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 01 Sep, 2024 Reviews received at journal 01 Sep, 2024 Reviews received at journal 27 Aug, 2024 Reviewers agreed at journal 23 Aug, 2024 Reviewers agreed at journal 17 Aug, 2024 Reviewers invited by journal 09 Aug, 2024 Editor assigned by journal 09 Aug, 2024 Submission checks completed at journal 09 Aug, 2024 First submitted to journal 05 Aug, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4860142","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":348006914,"identity":"ea4253e2-c7f9-4095-b0b2-9f40b7e80e58","order_by":0,"name":"Huigen Lu","email":"","orcid":"","institution":"Department of Orthopedic Surgery, the Second Affiliated Hospital, Zhejiang University School of Medicine, 310009 Hangzhou, China","correspondingAuthor":false,"prefix":"","firstName":"Huigen","middleName":"","lastName":"Lu","suffix":""},{"id":348006915,"identity":"ad78d44c-528e-4dc3-8a5a-e01b3e52a0f4","order_by":1,"name":"Weiguo Zhu","email":"","orcid":"","institution":"Department of Cardiology, Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, 200092 Shanghai, China","correspondingAuthor":false,"prefix":"","firstName":"Weiguo","middleName":"","lastName":"Zhu","suffix":""},{"id":348006916,"identity":"ce4d412f-f7af-4530-8d95-715910bbd3f1","order_by":2,"name":"Yingying Shen","email":"","orcid":"","institution":"Laboratory of Cancer Biology, Key Lab of Biotherapy in Zhejiang, Cancer Center of Zhejiang University, Sir Run Run Shaw Hospital, Medical School of Zhejiang University, 310020 Hangzhou, China","correspondingAuthor":false,"prefix":"","firstName":"Yingying","middleName":"","lastName":"Shen","suffix":""},{"id":348006917,"identity":"9bf7e5de-7956-4b41-bbb8-645c6299f298","order_by":3,"name":"Zhaoming Ye","email":"","orcid":"","institution":"Department of Orthopedic Surgery, the Second Affiliated Hospital, Zhejiang University School of Medicine, 310009 Hangzhou, China","correspondingAuthor":false,"prefix":"","firstName":"Zhaoming","middleName":"","lastName":"Ye","suffix":""},{"id":348006918,"identity":"a3724423-e0b9-4e5c-9aca-433a9deed646","order_by":4,"name":"Weishan Chen","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA70lEQVRIiWNgGAWjYDACCShpwMDA+ICBB8RJIF4LswEpWhhAWtigbAJa+Gc3H3vMU2Fhb85+9ljlD5nDDPzsOQYMP3fgseTOsXRjnjMSiTt78tJuSPAcZpDseWPA2HsGtxYDiRwz6dw2iQSDAzlmNwyAWgxu5BgwM7bh05L/TTr3n4S9wfk3ZgUJQC32hLXksEnnNkgwbriRY8ZwAGSLBAEtEjfSzKT/HJNI3HDjjbFkA086j8SZZwUHe/Fo4Z+R/ExyRk0d0GE5hh9/9ljL8bcnb3zwE48WVMDYA4nMA8RqAIIfJKgdBaNgFIyCEQMAwOdLPrS7dEwAAAAASUVORK5CYII=","orcid":"","institution":"Department of Orthopedic Surgery, the Second Affiliated Hospital, Zhejiang University School of Medicine, 310009 Hangzhou, China","correspondingAuthor":true,"prefix":"","firstName":"Weishan","middleName":"","lastName":"Chen","suffix":""}],"badges":[],"createdAt":"2024-08-05 07:49:28","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4860142/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4860142/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":66118081,"identity":"5382d8db-908a-4dbc-8729-2bf54a4a38ab","added_by":"auto","created_at":"2024-10-08 00:59:09","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":863926,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFlowchart of the overall workflow of this study.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn this study, to examine the potential influences of IDD on OA, two gene expression profiles (GSE124272 and GSE55235) from the Gene Expression Omnibus (GEO) database are downloaded to detect common differentially expressed genes (DEGs) for OA and IDD diseases to find similar pathways, possible medicines, regulatory networks and immune cell infiltration by using \"limma\", \"cluster profile\", \"CIBERSORT\", and single cells RNA-seq analysis. Subsequently, the potential diagnostic markers for OA patients among these shared hub genes were identified using LASSO regression analysis and the SVM-RFE method.\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4860142/v1/07eb9d2cd4f13cf4b10a839f.jpg"},{"id":66118542,"identity":"b1b1d530-3ed9-4dfe-9ed6-97e1b9f2dc89","added_by":"auto","created_at":"2024-10-08 01:07:09","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":233939,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eVenn diagram indicating the common DEGs between IDD and OA.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe shared DEGs were extracted from IDD and OA and shown in the Venn diagram.\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4860142/v1/92dea90e9ac42088489426dc.jpg"},{"id":66118084,"identity":"63360de2-cb7c-4f22-a134-843165322915","added_by":"auto","created_at":"2024-10-08 00:59:10","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":2803092,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePPI network construction and hub genes identification.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) PPI network development of shared DEGs. (B) The network of hub genes with the degree \u0026gt;10. (C) The Venn diagram displayed shared hub genes based on degree and MCC algorithms for the cytoHubba plugin.\u003c/p\u003e","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4860142/v1/5fe8b5ae963375afc5a217b7.jpg"},{"id":66116919,"identity":"132882d3-d14f-4a2b-aa54-1be2ca4a3425","added_by":"auto","created_at":"2024-10-08 00:51:09","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":2126268,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe regulatory interaction network of TFs-hub genes and miRNA-hub genes is based on the Network Analyst.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) The Network Analyst created a regulatory interaction network of TFs-hub genes. Dark pink square nodes represent TFs. Dark green circular nodes represent genes that interact with TFs. (B) The Network Analyst created a regulatory interaction network of miRNA-hub genes. Blue square nodes represent miRNAs. Red circular nodes represent genes that interact with miRNAs.\u003c/p\u003e","description":"","filename":"Figure4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4860142/v1/786c09889a61a8d9fdc173e5.jpg"},{"id":66116921,"identity":"d75f6fcc-0c9f-48a4-942e-e6eea173b840","added_by":"auto","created_at":"2024-10-08 00:51:09","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1515829,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eImmune characteristics of the shared hub genes in the single cell atlas of OA patients.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) The plot of tSNE visualizing 9822 cells from 3 OA synovial samples. (B) The plot of Dot shows the expression level of known marker genes. (C) tSNE showing the expression of marker genes in different clusters. (D) tSNE visualizing the main cell types of OA synovial samples. (E) tSNE plot visualization of each cell type in different groups. (F) The heatmap displays the expression patterns of shared DEGs in each cell type.\u003c/p\u003e","description":"","filename":"Figure5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4860142/v1/24ae23bb9678651a87bc1a7e.jpg"},{"id":66116926,"identity":"4e9b94c0-aa90-4a05-8104-5a602aee8756","added_by":"auto","created_at":"2024-10-08 00:51:10","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":807923,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eImmune characteristics of the IRGs in subgroups of OA macrophages.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) tSNE plot visualizing the clusters of macrophages. (B) Dot plot representation of the expression level of known marker genes. (C) tSNE showing the expression of marker genes in different clusters. (D) tSNE plot representation of macrophage subgroups in IBD. (E) UMAP plot visualizes each myeloid cell's subgroups in different groups. (F) The heatmap reveals the differential expression patterns of shared DEGs within the IL1B+ macrophage population in OA patients.\u003c/p\u003e","description":"","filename":"Figure6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4860142/v1/50532e3db3d2be004ea9b9e8.jpg"},{"id":66116924,"identity":"62f2fb2b-74a5-4ec0-84cf-43ee73faa638","added_by":"auto","created_at":"2024-10-08 00:51:10","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":812224,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIdentification of potential diagnosis genes in OA by machine learning.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) A LASSO regression identified 13 potential diagnosis genes in OA. (B) The SVM-RFE algorithm identified 22 potential diagnosis genes in OA. (C) Venn diagram of shared key genes between the LASSO and SVM-RFE methods. (D) The difference in expression levels of CSF1 and S100A12 between OA patients and normal controls. (E, G) ROC curve analysis of CSF1 and S100A12 in OA and IDD datasets. (F) The difference in expression levels of CSF1 and S100A12 between IDD patients and normal controls.\u003c/p\u003e","description":"","filename":"Figure7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4860142/v1/19792dbe93fd9521fe85ab1b.jpg"},{"id":66118083,"identity":"d7434920-6c2b-4232-8715-b12920ce9ec1","added_by":"auto","created_at":"2024-10-08 00:59:10","extension":"jpg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":1201458,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCorrelation analysis of immune cell infiltrates at different CSF1 and S100A12 expression levels in OA.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Correlation analysis of 22 immune cells between CSF1 expression levels. (B) Correlation analysis of 22 immune cells between S100A12 expression level. (C) Correlation analysis of inflammatory factors with CSF1 and S100A12.\u003c/p\u003e","description":"","filename":"Figure8.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4860142/v1/1e009c9b760efd8cee6c1a5e.jpg"},{"id":66119123,"identity":"af2cfea4-b29f-44c0-ba33-68007188ba48","added_by":"auto","created_at":"2024-10-08 01:15:13","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":11287793,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4860142/v1/b16214bb-b708-43cc-bc90-b031d88e3e1a.pdf"},{"id":66116927,"identity":"1a4d1b31-5921-4ac0-a6aa-9d1079839fda","added_by":"auto","created_at":"2024-10-08 00:51:10","extension":"docx","order_by":11,"title":"","display":"","copyAsset":false,"role":"supplement","size":9508935,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigures.docx","url":"https://assets-eu.researchsquare.com/files/rs-4860142/v1/5239d9a296d2405aa677415a.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Bioinformatics approach to explore the common pathogenic genes between lumbar disc degeneration and osteoarthritis","fulltext":[{"header":"Introduction","content":"\u003cp\u003eOsteoarthritis (OA), one of the most prevalent forms of arthritis in elderly adults, involves several joints and harms joint function, mainly characterized by synovial inflammation, cartilage degeneration, and reduced joint function[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Its clinical manifestations include local pain and limited joint movement[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Besides, patients with OA may experience symptoms such as swelling, pain, or stiffness, which can harm their quality of life[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Women over the age of 55 and males over the age of 65 have an increased risk of developing OA[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. OA has been associated with an increased incidence rate in the past few decades due to the aging population and obesity[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. According to a recent study, there will be approximately 400\u0026nbsp;million cases of OA in China by 2030[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. However, the etiology and pathogenesis of the disease are still unknown. It is urgent to explore the potential mechanisms responsible for OA development, which is beneficial for developing novel and effective strategies.\u003c/p\u003e \u003cp\u003eIntervertebral disc degeneration (IDD) is a degenerative disease recognized as the primary cause of low back pain[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], and affects 70%-85% of the global population. This disease is distinguished by biochemical alterations in the nucleus pulposus, extracellular matrix degradation in the nucleus pulposus, and exterior damage to the fibrous ring accompanied by increasing fibrosis[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Research reports suggest that inflammation and signaling substances may play important roles in the occurrence and development of intervertebral disc degeneration, such as interleukin-1 (IL-1), tumor necrosis factor-alpha (TNF), nitric oxide, matrix metalloproteinases and metalloproteinases[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. In addition, during the progression of IDD, macrophage infiltration leads to the release of many inflammatory factors, forming an inflammatory storm[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Meanwhile, OA is a chronic inflammatory disease, and the release of inflammatory factors has a memorable role in the process of OA. For example, enhanced IL-1 and TNF levels are related to the increased risk of OA[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Besides, significantly increased levels of inflammatory cytokines, such as IL-1 and IL-6, were found in OA patients' synovial fluid, synovium, subchondral bone, and cartilage [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Inhibition of IL-1 and TNF can significantly reduce cartilage degeneration and inflammatory response in experimental OA[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Meanwhile, clinical studies have shown a significant correlation between hip OA and endplate degeneration at L1, L3, and L5 levels[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], and degenerative diseases of the upper lumbar spine can induce the occurrence of primary unilateral knee osteoarthritis[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Therefore, IDD and OA may be interdependent and potentially have considerable pathological and physiological overlap. However, so far, the impact of IDD on the severity and progression of OA is still unclear. Given this, exploring the potential mechanisms underlying the impact of lumbar disc degeneration on the progression of osteoarthritis (OA) is crucial for improving the treatment of OA patients. By comprehensively deciphering the shared differentially expressed genes between these two diseases and identifying potential diagnostic biomarkers, a foundation can be established for more effective treatment strategies and ultimately improve clinical outcomes.\u003c/p\u003e \u003cp\u003eHere, we examined the overlapping molecular and cellular intersections of gene expression in IDDD and OA, utilizing transcriptome and single-cell sequencing data. Our investigation suggests that IDDD may exacerbate OA by an excessive cytokine storm. These findings have significant implications for advancing our comprehension and treatment strategies of OA patients with concurrent IDD. The entire workflow diagram in this study is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Material and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eDataset collection and processing\u003c/h2\u003e \u003cp\u003eSynovial tissue microarray dataset (GSE55235), including 10 normal samples and 10 Osteoarthritis (OA) samples, was processed using the Affymetrix GPL96 platform, and GSE124272 [the database consists of 16 whole blood samples from 8 patients with Intervertebral disc degeneration (IDD) and 8 healthy controls] was analyzed using the platform of Agilent GPL21185. In addition, a validated dataset, GSE55457, was analyzed using the GPL96 Affymetrix U133A Array. GSE55457's gene expression profile included 10 OA patients and 10 healthy controls. GSE150408 contains the whole blood RNA-seq transcriptome data of 17 IDD patients and 17 healthy volunteers and was analyzed using the Agilent GPL21185 platform. GSE176205, containing 3 controls and 6 IDD tissue samples, was analyzed using GPL20301. All these datasets were obtained from the Gene Expression Omnibus (GEO) database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.ncbi.nlm.nih.gov/geo\u003c/span\u003e\u003cspan address=\"http://www.ncbi.nlm.nih.gov/geo\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), and all four datasets of data's gene expression matrix files were created with the par pl package of Perl from raw RNA-seq data.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eIdentification of common differentially expressed genes (DEGs) between IDD and OA\u003c/h2\u003e \u003cp\u003eFirst, use R software (version 4.2.1) to read the expression matrices of datasets GSE1919, GSE55235, and GSE51588 for batch correction[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] and normalization[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Subsequently, the \"limma\" R package was performed to identify DEGs between IDD patients and controls or OA patients and controls. Meanwhile, genes with |log2FC | \u0026gt; 0.585 and p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were screened to be DEGs[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Volcano plots and DEG expression heatmaps were constructed using the \"ggplot2\" and \"heatmap\" packages. Besides, the common DEGs of GSE124272 and GSE55457 were acquired based on the Jvenn, a web-based Venn diagram tool.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eGene ontology (GO) and pathway enrichment analysis\u003c/h2\u003e \u003cp\u003eThe \"clusterProfile\" package[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], which is a widely used general enrichment tool for interpreting omics data, was next performed for analyzing GO and Disease Ontology (DO) to explore further the potential molecular mechanism of these shared DEGs and their link to disease. Disease ontology is a unified cross-species disease annotation[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Meanwhile, Enrichr, an online application (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://maayanlab.cloud/Enrichr/\u003c/span\u003e\u003cspan address=\"https://maayanlab.cloud/Enrichr/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), was used to discover the major signaling pathways of these common DEGs, which included Kyoto Encyclopaedia of Genes and Genomes (KEGG) Pathways, WikiPathways, Reactome, and Hallmark. We used a \u003cem\u003ep\u003c/em\u003e-value of 0.05 as the cut-off for significance[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eProtein-protein interactions (PPI) network construction and hub gene identification\u003c/h2\u003e \u003cp\u003eCytoscape (v3.9.1), which is a software tool used for visualizing and analyzing biological molecular interaction networks, was used to generate a visual representation of the PPI networks based on gene interactions combined with a total score of \u0026gt;\u0026thinsp;0.4[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e], which were derived from the STRING database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://string-db.org/\u003c/span\u003e\u003cspan address=\"https://string-db.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The Cytoscape plugin CytoHubba, a crucial tool for analyzing and identifying the center of biological networks, was used to determine the degree of each node. Hub genes were identified as those with a degree score (highly connected nodes with a degree) of \u0026gt;\u0026thinsp;10[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Then, two algorithms of the cytoHubba plugin, including degree and Matthew's correlation coefficient (MCC) algorithms, were employed to identify shared hub genes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eRecognition of TFs and miRNA interact with shared hub genes\u003c/h2\u003e \u003cp\u003eTranscription factors (TFs) are proteins that attach to specific genes, exerting control over the rate of genetic information transcription. As a result, they play a pivotal role in facilitating a comprehensive understanding of molecular processes[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Using the 'NetworkAnalyst' function to find topologically plausible TFs derived from the JASPAR database that tend to bind to common DEGs opens up new avenues of investigation into the TFs regulating common hub genes. Six different taxonomic groupings are represented in the publicly available JASPAR database of TF binding profiles (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://jaspar.genereg.net/\u003c/span\u003e\u003cspan address=\"https://jaspar.genereg.net/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Meta-analysis of gene expression data using the PPI network is performed in NetworkAnalyst to study biological mechanisms in greater depth[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAdditionally, microRNAs (miRNAs), a class of small non-coding RNAs that bind to the transcripts of widely used DEG, thus altering protein expression, were identified by doing a target gene-miRNA interaction analysis. MirTarBase (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://mirtarbase.cuhk.edu.cn/~miRTarBase/\u003c/span\u003e\u003cspan address=\"https://mirtarbase.cuhk.edu.cn/~miRTarBase/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) is the major database of miRNA-target gene interactions. Here, from the interaction of miRNAs-gene interaction based on the NetworkAnalyst platform, we extracted miRNAs that interact with shared hub genes focused on topological analysis from the mirTarBase database. NetworkAnalyst simplifies the exploration of complex datasets for researchers, helping to identify biological features and functions and formulating meaningful biological hypotheses[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. The interaction network of TFs and miRNAs was visualized using Cytoscape.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eDownload and Analysis of scRNA-seq data\u003c/h2\u003e \u003cp\u003eThe single-cell raw matrix data of synovial tissues from GSE152805 were acquired from the GEO database. We then employed the Seurat package (version 4.1.2) to process the raw count matrix (UMI counts for each sample). The low-quality cells, defined as those with fewer than 200 genes per cell, less than 10 cells per gene, and more than 15% mitochondrial genes, were excluded. Following this, we performed normalization using the 'NormalizeData' function based on the corrected expression matrix. After data normalization, we scaled the data by applying the 'FindVariableGenes' function, focusing on the expression of 2000 highly variable genes (HVGs). Principal component analysis of these HVGs was executed using the 'RunPCA' function, and we eliminated batch effects between different samples using the 'harmony' function. The purpose of identifying cell types across all cell populations is to set the resolution to 1.0. Subsequently, we employed the \"RunTSNE\" function to project the combined cells into a two-dimensional space. Following this, we utilized the \"Dimplot\" function to visualize the cell clusters, concluding with their annotation based on known markers.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eAssessment of Potential drugs\u003c/h2\u003e \u003cp\u003eUnderstanding the features of diseases requires careful consideration of protein-drug interactions (PDIs) or drug molecule identification. As a result, we forecasted PDIs using the web application Enrichr. First, based on the shared hub genes, drug compounds were discovered utilizing the Drug Signatures database (DSigDB) via the Enrichr database based on the common hub genes of OA and IDD. Enrichr is a popular online platform that hosts a vast collection of diverse gene set libraries, enabling the exploration of gene set enrichment across the entire genome (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://maayanlab.cloud/Enrichr/\u003c/span\u003e\u003cspan address=\"https://maayanlab.cloud/Enrichr/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). DSigDB (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://dsigdb.tanlab.org/DSigDBv1.0/\u003c/span\u003e\u003cspan address=\"http://dsigdb.tanlab.org/DSigDBv1.0/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) is a data repository for identifying targeted therapeutic compounds connected to these hub genes[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. The database consists of 22,527 gene sets, and one accessible way to access the DSigDB database is through Enrichr under the Disease/Drug functionality category.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eImmune Cell Infiltration and Single-sample Gene Set Enrichment Analysis (ssGSEA)\u003c/h2\u003e \u003cp\u003eTo explore the correlation between these common DEGs and immune cell infiltration characteristics in OA, infiltration levels of different immune cells in OA were evaluated using the \"CIBERSORT\" package, and the relative proportions of 22 immune cells were estimated via iterations (1000 times). The \"CIBERSORT\" package utilizes linear support vector regression on gene expression profiles, allowing for the estimation of immune cell quantities in samples using RNA sequencing data[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Subsequently, the Wilcoxon test was run to calculate the differences between the OA disease group and the normal control group in each immune cell population. Then, to further validate the activity of these DEGs during OA progression, the ssGSEA algorithm was implemented to elucidate the enrichment scores of differential expression genes quantitatively shared in each sample[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. The Gene Set Variation Analysis (GSVA) R package (Version 1.26.0) was employed to calculate normalized enrichment scores for gene sets.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eCandidate diagnosis marker identification\u003c/h2\u003e \u003cp\u003eThen, to identify the diagnostic markers in OA patients, we used two machine learning algorithms (LASSO logistic regression and the SVM algorithm). As a dimensionality reduction method, compared to regression analysis, LASSO regression demonstrates superior performance when evaluating high-dimensional data. It employs regularization to enhance prediction accuracy. Here, the \"Glmnet\" software package for LASSO regression analysis is used to identify genes significantly associated with the discrimination of OA and healthy samples. Meanwhile, as a supervised machine learning method for support vectors, support vector machines (SVM) rank features recursively to prevent overfitting and find the optimal variable by deleting the feature vectors generated by the support vector machine[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. The SVM classifier from the \"e1071\" R package was adopted for screened biomarkers in diagnosing OA patients. Receiver Operating Characteristic (ROC) analysis of these feature genes was conducted in RStudio using the pROC package. Area under the curve (AUC), with a range of 0.7 to 1, typically has good predictive performance[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Therefore, feature genes with an AUC value of \u0026gt;\u0026thinsp;0.7 are considered diagnostic for OA.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eGSEA function enrichment and immune cell infiltration analysis of CSF1 and S100A12 in OA\u003c/h2\u003e \u003cp\u003eThe \"limma\" and \"cluster Profiler\" packages in R were used to carry out gene set enrichment analysis (GSEA) at a threshold of p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 to identify differences in signaling pathways of CSF1 and S100A12 (grouped by median expression level). GSEA is for enrichment analysis of pathways based on whole-genome expression profiles[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. The gene sets of c2.cp.kegg.v7.4.symbols were derived from the MSigDB database for running the GSEA analysis. To determine the interaction relationship among two effective biomarkers (CSF1 and S100A12) and differential immune cells and inflammatory factors, the \"limma\" and \"vioplot\" packages in R were used to compare the relative proportions of the immune cells between control and OA patients, and the \"correlation analysis\" was run to perform Pearson correlation analysis for these two genes (CSF1and S100A12) and immune cells and inflammatory factors.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e184 shared DEGs were identified between IDD and OA\u003c/h2\u003e \u003cp\u003eTo examine the potential influences of IDD on OA, we compared the differential gene expression in the whole blood samples of 8 intervertebral disc degeneration patients and 8 healthy intervertebral disc patients from dataset GSE124272. Meanwhile, we analyzed the differential gene expression between 10 osteoarthritides (OA) patients' pathological synovial tissues and 10 healthy control tissues using GEO data GSE55235. Subsequently, 2381 DEGs (986 upregulated and 1395 down-regulated) in the IDD dataset and 2361 DEGs (938 upregulated and 1423 down-regulated) in the OA dataset were identified using \"limma\" R package with a threshold of \u003cem\u003ep\u003c/em\u003e-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and |log2FC| \u0026ge; 0.585. Volcano plots and heatmaps are commonly used to visualize DEGs for IDD and OA (Supplementary Fig.\u0026nbsp;1A-D). Next, 184 common DEGs between IDD and OA datasets were identified using the Venn diagram (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Hence, these findings can be inferred that there exists a mutual linkage between these two diseases, as they exhibit a shared presence of multiple common genes (184 common DEGs); these shared DEGs were used in further analysis to delve deeper into exploring the potential connections between the two diseases.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eGO and pathway enrichment analysis\u003c/h2\u003e \u003cp\u003eTo explore the potential biological function and diseases related to these common DEGs, the \"clusterProfile\" package was performed to carry out GO and DO enrichment analysis. The top 10 enriched GO terms for these shared DEGs are shown in Supplementary Fig.\u0026nbsp;2A. The GO analysis showed that shared DEGs were remarkably enriched in neutrophil activation involved in immune response, neutrophil-mediated immunity, and negative regulation of inflammatory response; nuclear chromosome, phagocytic vesicle lumen and phagocytic vesicle; transition metal ion binding, protein phosphatase binding and zinc ion binding (Supplementary Fig.\u0026nbsp;2A and Supplementary Table\u0026nbsp;1). Meanwhile, the top 40 enriched DO terms for these shared DEGs are shown in the Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB. The findings indicated that these DEGs exhibited enrichment in diseases such as hip osteoarthritis, autoimmune diseases, systemic lupus erythematosus, breast cancer, rheumatoid arthritis, and other related illnesses (Supplementary Fig.\u0026nbsp;2B and Supplementary Table\u0026nbsp;2). Subsequently, the four global databases were used further to investigate the possible pathways of these common DEGs. The results showed that these common DEGs were significantly related to the MAPK signaling pathway, T cell receptor signaling pathway, Rap1 signaling pathway, immune system, innate immune system, and VEGFA-VEGFR2 signaling pathway (Supplementary Fig.\u0026nbsp;3A-B and Supplementary Table\u0026nbsp;3, 4). These results indicate that these DEGs may influence the occurrence of OA through pathways associated with inflammation and immune response.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eHub gene classification and construction of regulatory networks\u003c/h2\u003e \u003cp\u003eThe PPI network of these shared DEGs was created by using the STRING database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://string-db.org/\u003c/span\u003e\u003cspan address=\"https://string-db.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), which is a database for predicting functional associations between proteins, and Cytoscape (v3.7.2) visualized these DEGs with a combined score\u0026thinsp;\u0026ge;\u0026thinsp;0.4 as the cut-off criteria. Figure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA depicts the PPI network of common DEGs consisting of 184 nodes and 912 edges. The degree and MCC algorithms of the cytoHubba plugin in Cytoscape were utilized to calculate the protein-protein interaction (PPI) network of 181 DEGs. Among 184 common DEGs, 131 hub genes were identified with a threshold degree of \u0026gt;\u0026thinsp;10 based on Degree algorithms of the cytoHubba plugin (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB and Supplementary Table\u0026nbsp;5). A Venn diagram was then constructed using the intersection of hub genes obtained from these two algorithms, and 114 shared hub genes were identified (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). Additionally, to ascertain the primary alterations at the transcriptional level and delve further into key protein regulatory molecules or common DEGs, we employed a network-based approach to decode the regulatory transcription factors (TFs) and miRNAs. Figure\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA depicts the network of TF regulators of shared DEGs, and Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB depicts the interaction of miRNAs with shared DEGs. We identified 36 transcription factors (TFs) and 29 post-transcriptional regulators (miRNAs) regulated with more than one shared DEGs by analyzing the interaction network between transcription factor genes and miRNA genes. These results essentially indicate a strong interplay between them.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003escRNA-seq reveals the accumulation of common 22 IRGs in inflammatory macrophages of OA patients\u003c/h2\u003e \u003cp\u003eTo clarify the impact of IDD on the process of OA, we subsequently explored the immune characteristics of these common hub genes in OA patients. The scRNA-seq dataset of OA patients (including three OA synovial samples) was obtained from the dataset GSE152805. A total of 9822 cells were acquired, and these cells were divided into 17 clusters based on unsupervised graph-based clustering (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). These clusters were then defined as synovial subintimal fibroblasts (SSF), synovial intimal fibroblasts (SIF), macrophage, DC, endothelial cell (EC), smooth muscle cell (SMC), Mast Cell, proliferating immune cell (ProIC), T cell, and B cell based on the known cell markers (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB-E). Subsequently, we analyzed the expression of these shared hub genes in different cell types within OA patients. Among their hub genes were 30 genes highly expressed in macrophages, compared with other cell types (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eF). To delve deeper into the potential roles of these 29 genes in macrophages of OA patients, we conducted a subgroup analysis by extracting macrophages. Firstly, we performed unsupervised graph-based clustering of macrophages, identifying a total of 10 clusters (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA), and then these clusters were defined as FOLR2\u003csup\u003e+\u003c/sup\u003emacrophages (FOLR2\u003csup\u003e+\u003c/sup\u003eMac) and IL1B\u003csup\u003e+\u003c/sup\u003emacrophages (IL1B\u003csup\u003e+\u003c/sup\u003eMac) according to specific gene signatures (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB-E). Subsequently, we further explored the distribution of these 30 genes in the subgroups of macrophages.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eInterestingly, we noticed that almost all these genes were highly expressed in IL1B\u003csup\u003e+\u003c/sup\u003eMac compared with the FOLR2\u003csup\u003e+\u003c/sup\u003eMac except HCRT (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eF). Simultaneously, we further observed that these genes were highly expressed in IDD patients (blood and tissue samples) compared to normal samples and also found to be highly expressed in OA patients (Supplementary Fig.\u0026nbsp;4A-B). These genes were highly expressed in IL1B\u0026thinsp;+\u0026thinsp;Mac and were defined as inflammation-related DEGs (IRGs). Subsequently, to clarify the characteristic changes of these IRGs in OA immune cell infiltration. We then utilized single-sample Gene Set Enrichment Analysis (ssGSEA) to calculate each sample's scores based on these IRGs' expressions (Supplementary Fig.\u0026nbsp;5A). The results indicated that the ssGSEA score (DEGs signature) is significantly higher in the OA disease samples than in the control samples (Supplementary Fig.\u0026nbsp;5B). Besides, this score also shows a significant positive correlation with M1 macrophages, follicular helper T cells, Eosinophils and Neutrophils (Supplementary Fig.\u0026nbsp;5C-G). Simultaneously, we further assessed the associations between several pro-inflammatory cytokines (IL1B, IL2, IL6, IL12B and TNF) in OA and DEGs signature. The results reveal a significant positive correlation between these pro-inflammatory cytokines and the DEG signature (Supplementary Fig.\u0026nbsp;6). To conclude, these results indicated that the IRGs, which were highly expressed by IL1B\u003csup\u003e+\u003c/sup\u003eMac, may be the main contributors to the cytokine storm in OA disease, as well as IDD may also promote the progression of OA through cytokine storms.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eDetermination and verification of diagnostic markers in OA patients by machine learning\u003c/h2\u003e \u003cp\u003eFinally, to explore potential biomarkers for OA, we further screened diagnostic genes based on these IRGs of the OA datasets using LASSO regression. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA, LASSO regression identified 13 variables that were determined among these common hub genes as characteristic genes for OA. Meanwhile, other arithmetics (SVM-RFE method) were utilized to explore OA's diagnostic biomarkers. The data showed that 22 genes were identified as signature genes by the SVM-RFE algorithm (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eB). Then, regarding the results of the two algorithms, after taking the intersection. In total, 2 overlapping genes (Colony stimulating factor 1, CSF1 and S100 calcium-binding protein A12, S100A12) were selected between those two arithmetics via Venn diagrams (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eC).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAdditionally, Supplementary Table S6 displays the shared hub genes discovered by the LASSO regression and SVM-RFE algorithm. Subsequently, we further validated the expression level of these two genes between OA patients and controls. The results showed that these two genes were significantly upregulated in the OA patients compared with control samples (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eD). Besides, ROC analysis confirmed that these two genes exhibited strong diagnosis stability, and the AUC value was 0.820 and 0.870, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eE). Thus, these two genes may be critical diagnostic markers involved in OA progression.\u003c/p\u003e \u003cp\u003eMeanwhile, these two genes were also significantly more expressed in the whole blood and tissue samples of IDD patients than in control samples (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eF), and the AUC value of these two genes also revealed an excellent diagnostic value for LDDD patients (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eG). Moreover, the expression and AUC value of these two potential genes were also confirmed in other OA datasets (GSE55457) and IDD external independent datasets (GSE150408) (Supplementary Fig.\u0026nbsp;7A-D). In conclusion, these results demonstrated that these two genes may be potential targets for treating and diagnosing OA and IDD.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eCorrelation between CSF1 and S100A12 expression and immune cell infiltration across OA\u003c/h2\u003e \u003cp\u003eTo further investigate the potential role of these two genes (CSF1 and S100A12) in OA, GSEA was used to screen the gene sets enriched in different expression levels of these two genes. As shown in Supplementary Fig.\u0026nbsp;8A-B, compared with the group of CSF1 low expression, the Calcium signaling pathway, Focal-adhesion, and ECM-receptor interaction were significantly enriched in the CSF1 high expression group. Chemokine signaling pathway, Focal-adhesion and Cytokine-cytokine receptor interaction were more involved in the S100A12 high expression group than in the S100A12 low expression group. Therefore, these findings showed that these two genes may influence the occurrence of OA through pathways associated with chemokine and cytokine-cytokine receptor interaction. The study mentioned above reveals a significant enrichment of inflammation-related infiltrating cells (M1 macrophages and neutrophils) in patients with OA. Therefore, to further investigate whether these two prognostic remarkers are associated with immune cell infiltration during the development of OA, Pearson correlation analysis was performed on these two genes (CSF1 and S100A12) and immune cells, and the results showed that the expression of these two genes was appreciably positively correlated with the MO and M1 macrophages (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eA-B).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eMeanwhile, we further explored the relationship between pro-inflammatory cytokines and these two genes in OA. We observed a significant positive correlation between the expression of these pro-inflammatory cytokines and CSF1 and S100A12 (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eC). Thus, the above results suggested that these two genes may promote the progression of OA through inflammatory-related responses.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eIdentification of candidate drugs\u003c/h2\u003e \u003cp\u003eAssessment of PDIs is essential to understand the structural features recommended for receptor sensitivity. In the aspects of common DEGs as potential drug targets in OA and IDD, the top 15 candidate therapeutic compounds were identified by Enrichr based on the transcriptome signatures from the DSigDB database. These chemical compounds are extracted based on their p-value. These potential drugs are suggested for the common hub genes. These drugs could be chemical compounds that are used to treat both diseases. The DSigDB database's list of effective medications for common hub genes is presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eList of the top 15 suggested drugs for OA.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTerm\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ep-\u003c/em\u003evalue\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCounts\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eChemical Formula\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVALPROIC ACID\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.001155287\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eC\u003csub\u003e8\u003c/sub\u003eH\u003csub\u003e16\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRetinoic acid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.99E-06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eC\u003csub\u003e20\u003c/sub\u003eH\u003csub\u003e28\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eestradiol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.24E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eC\u003csub\u003e18\u003c/sub\u003eH\u003csub\u003e24\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ebenzo[a]pyrene\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.43E-04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eC\u003csub\u003e20\u003c/sub\u003eH\u003csub\u003e12\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTetradioxin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.97E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eC\u003csub\u003e12\u003c/sub\u003eH\u003csub\u003e4\u003c/sub\u003eC\u003csub\u003el4\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003etrichostatin A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.37E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eC\u003csub\u003e17\u003c/sub\u003eH\u003csub\u003e22\u003c/sub\u003eN\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAFLATOXIN B1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.89E-04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eC\u003csub\u003e17\u003c/sub\u003eH\u003csub\u003e12\u003c/sub\u003eO\u003csub\u003e6\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTriclocarban\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.92E-04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eC\u003csub\u003e13\u003c/sub\u003eH\u003csub\u003e9\u003c/sub\u003eC\u003csub\u003el3\u003c/sub\u003eN\u003csub\u003e2\u003c/sub\u003eO\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCOPPER\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.87E-07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCu\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eresveratrol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.35E-07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eC\u003csub\u003e14\u003c/sub\u003eH\u003csub\u003e12\u003c/sub\u003eO\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecalcitriol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.11E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eC\u003csub\u003e27\u003c/sub\u003eH\u003csub\u003e44\u003c/sub\u003eO\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehydrogen peroxide\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.001913058\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eH\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBisphenol A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.66E-09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eC\u003csub\u003e15\u003c/sub\u003eH\u003csub\u003e16\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eprogesterone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.21E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eC\u003csub\u003e21\u003c/sub\u003eH\u003csub\u003e30\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDecitabine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.76E-04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eC\u003csub\u003e8\u003c/sub\u003eH\u003csub\u003e12\u003c/sub\u003eN\u003csub\u003e4\u003c/sub\u003eO\u003csub\u003e4\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eOA, a widespread chronic joint disease, affects many individuals worldwide[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. OA has gradually increased healthcare and social expenditures as the primary cause of impairment in older persons [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Although multiple diagnostic and therapeutic approaches are available for OA, the clinical outcomes are still unsatisfactory. IDD is a degenerative spine disease that causes chronic lower back pain and motor disturbance[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. In more severe stages, loss of joint space, subchondral sclerosis, and osteophytes, similar to OA in the articular joint, accompany intervertebral disc degeneration[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Thus, IDD may accelerate the progression of OA. Nevertheless, there are few reports illustrating the interactions between the two diseases. Here, we employed various bioinformatics analysis methods to reveal the potential molecular mechanism of IDD in the pathogenesis of OA and provide a possible basis for future targeted and preventive treatment.\u003c/p\u003e \u003cp\u003eHere, 184 common DEGs were identified between OA and IDD. After constructing the PPI network of shared DEGs, 114 common hub genes were identified based on the degree and MCC algorithm of the cytoHubba plugin in Cytoscape. Subsequently, 114 identified shared hub genes were evaluated by functional enrichment and pathway analysis. The enrichment analysis results showed that these common hub genes were significantly associated with the MAPK signaling pathway, T cell receptor signaling pathway, Rap1 signaling pathway, immune response, neutrophil-mediated immunity, and negative regulation of inflammatory response. Existing research indicates an inherent connection between the MAPK signaling pathway and osteoarthritis (OA) advancement. An imbalance in the MAPK pathway expedites inflammatory reactions, releasing a multitude of cartilage matrix-degrading enzymes and intensifying cartilage degeneration[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. The Rap1 signaling pathway is an essential component of pathological osteoclast resorption, contributing to the progression of arthritis[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Furthermore, research has reported that T-cell signaling mediates the onset of various inflammatory diseases, such as inflammatory bowel disease and autoimmune arthritis[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. Therefore, these data indicated that these common DEG-related pathways are closely associated with the inflammatory response.\u003c/p\u003e \u003cp\u003ePrevious research has shown that OA, as an inflammatory disease, can be exacerbated by a dysregulated inflammatory storm[\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e], and large amounts of pro-inflammatory cytokines can be detected in patients with IDD[\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. To investigate the distribution of these DEGs across various immune cell types, we delved deeper into the characteristics of single-cell atlases derived from OA tissue samples. Our data proved that 30 of these hub genes were broadly enriched in macrophages compared to other cell types, and subgroup analysis of macrophages also revealed that the IL1B\u003csup\u003e+\u003c/sup\u003e macrophage subtype was a prominent source of expression for these 30 genes. Meanwhile, we further observed that these genes were highly expressed in IDD and OA patients compared to control samples. Subsequently, based on the expression of these 30 genes, the score derived from ssGSEA calculations for these gene sets is to be constructed to explore further the characteristic changes of these genes in OA immune cell infiltration. The ssGSEA score of these gene sets was significantly higher in the OA disease group than in the control group. These scores also positively correlated with M1 macrophages, eosinophils, and neutrophils. Meanwhile, we further assessed the associations between several pro-inflammatory cytokines in OA and these scores. The results showed a significant positive correlation between these pro-inflammatory cytokines and these scores. Polarization of synovial macrophages towards M1 exacerbates the development of OA[\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAdditionally, research has indicated that neutrophil elastase (NE) contributes to tissue degeneration, osteophyte formation, and the release of inflammatory cytokines and chemokines, thereby participating in the progression of OA[\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. Therefore, based on these findings, it can be inferred that the highly expressed IRGs in IL1B\u003csup\u003e+\u003c/sup\u003e macrophages could be the primary drivers of the cytokine storm in OA. IDD might also contribute to OA progression through cytokine storms.\u003c/p\u003e \u003cp\u003eSubsequently, we conducted feature selection utilizing two distinct algorithms to delve deeper into potential biomarkers for osteoarthritis (OA). The results showed that two hub genes (CSF1 and S100A12) were identified as feature genes using LASSO regression analysis and the SVM-RFE method. CSF1, the protein encoded by this gene, is a cytokine that controls macrophage production, differentiation, and function. CSF regulates the development, proliferation, differentiation and activation of myeloid cells in health and disease[\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. In addition, it has been reported that CSF-1 can promote the survival, proliferation and differentiation of bone marrow cells (including monocytes, macrophages and osteoclast) by binding to the tyrosine kinase receptor CSF1R[\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. Exogenous administration of CSF-1 exacerbates the incidence and severity of OA[\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. The protein encoded by is a member of the S100 protein family containing two EF-hand calcium-binding motifs. S100 protein has also been proven to have important extracellular pro-inflammatory effects and cytokine-like activity[\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. Clinical research shows serum S100A12 expression is elevated in patients with OA [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. The change in S100A12 expression level in synovial fluid can reflect the clinical severity of patients with primary knee osteoarthritis[\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]. Here, our findings revealed significant overexpression of CSF1 and S100A12 in both the test and validation sets among IDD and OA patients and the OA patients with high CSF1 and S100A12 levels had positively correlated with M1 macrophages.\u003c/p\u003e \u003cp\u003eMeanwhile, the results of the correlation analysis indicate a significant positive correlation between these two genes and pro-inflammatory cytokines in the progression of OA. Experimental studies also demonstrated that blocking the CSF1R can reduce inflammation in human and mouse models of rheumatoid arthritis[\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. Furthermore, clinical case study results indicate that S100A12 in the serum and synovial fluid (SF) of patients with OA may be an independent factor for predicting the risk of this condition[\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]. Therefore, these two genes may serve as potential diagnostic biomarkers for OA patients and could be involved in the pathogenesis of OA through inflammatory responses.\u003c/p\u003e \u003cp\u003eTranscription factors (TFs) govern the proportion of transcriptional processes, while miRNAs play a crucial role in gene regulation at the post-transcriptional level and in RNA silencing. Both TFs and miRNAs are of significant importance in understanding disease progression. Then, a study of the interactions between TFs-genes and miRNAs-genes was done to determine which regulators control the transcription and expression of common hub genes. 36 TFs and 29 miRNAs were found to have substantial interactions with common hub genes. For example, YY1[\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e] and NFKB1[\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e] are associated with the regulation and progression of OA diseases. Furthermore, some miRNAs, such as miR-335-5p[\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e] and miR-218-5p[\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e], participated in the progression of OA.\u003c/p\u003e \u003cp\u003eIn recent years, several chemical agents and drugs have been utilized as potential therapeutic approaches to OA. Local nonsteroidal anti-inflammatory drugs (NSAIDs) such as lurbiprofen cataplasms and loxoprofen sodium cataplasm have effectively treated OA[\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]. However, the incidence rate of OA disease has increased significantly, so it is urgent to develop new drugs to treat OA. Based on shared hub genes, some potential drugs and compounds targeting OA and IDD have been identified using database, such as valproic acid (VPA), estradiol, and trichostatin A. VPA is a flavonoid compound extracted from herbaceous plants and is a first-line drug for treating epilepsy and mania[\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e]. VPA has been proven to have multiple therapeutic effects, including anticancer, neuroprotective, differentiation, and neurodegenerative activity[\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e]. Besides, studies found that VPA was identified as a putative agent associated with OA progression and protected chondrocytes from damage caused by OA stimulation[\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e]. Estradiol is a well-known homeostatic regulator of the bone and immune system and can also control exercise ability and pain sensitivity[\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e]. In addition, experimental studies have confirmed that the physiological level of estradiol limits the progression of OA in mice[\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e]. Trichostatin A (TSA) is a non-selective histone deacetylase inhibitor that inhibits OA and rheumatoid arthritis progression by inhibiting the expression of MMP[\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e]. So, these chemical compounds might be good choices for drugs to treat OA. However, there are still some limitations to this study. First, the sample size of this study is small, which may affect the reliability of the conclusions. In addition, the results of this study are all based on the analysis of database data. The necessary support of experimental evidence is lacking. CSF1 and S100A12 need further validation through cell experiments, animal models and clinical samples. Additionally, these drugs need to undergo chemical experiments for further confirmation. Despite these limitations, the results of the current study may provide potential directions for future research.\u003c/p\u003e \u003cp\u003eOur study elucidates the potential molecular connections between IDD and OA at the cellular and molecular levels through comprehensive transcriptome and single-cell sequencing data analysis. Our research has identified that IDDD exacerbates the severity of OA through an excessive cytokine storm. Additionally, we identified two potential diagnostic biomarkers for these diseases. By expanding our understanding of these conditions, our research provides potential directions for further investigating the molecular mechanisms of OA. It may lead to more effective therapeutic strategies and improved clinical outcomes for OA patients.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eOsteoarthritis \u0026nbsp;(OA)\u003c/p\u003e\n\u003cp\u003elumbar disc degeneration disease \u0026nbsp;(IDD)\u003c/p\u003e\n\u003cp\u003edifferentially expressed genes \u0026nbsp;(DEGs)\u003c/p\u003e\n\u003cp\u003einterleukin-1 \u0026nbsp;(IL-1)\u003c/p\u003e\n\u003cp\u003etumor necrosis factor-alpha \u0026nbsp;(TNF)\u003c/p\u003e\n\u003cp\u003eL1, L3, and L5 levels\u003c/p\u003e\n\u003cp\u003eGene Expression Omnibus \u0026nbsp;(GEO)\u003c/p\u003e\n\u003cp\u003eGene ontology \u0026nbsp;(GO)\u003c/p\u003e\n\u003cp\u003eDisease Ontology \u0026nbsp;(DO)\u003c/p\u003e\n\u003cp\u003eKyoto Encyclopaedia of Genes and Genomes \u0026nbsp;(KEGG)\u003c/p\u003e\n\u003cp\u003eProtein-protein interactions (PPI)\u003c/p\u003e\n\u003cp\u003eMatthew\u0026apos;s correlation coefficient \u0026nbsp;(MCC)\u003c/p\u003e\n\u003cp\u003eTranscription factors \u0026nbsp;(TFs)\u003c/p\u003e\n\u003cp\u003emicroRNAs \u0026nbsp;(miRNAs)\u003c/p\u003e\n\u003cp\u003eprotein-drug interactions \u0026nbsp;(PDIs)\u003c/p\u003e\n\u003cp\u003eDrug Signatures database \u0026nbsp;(DSigDB)\u003c/p\u003e\n\u003cp\u003eSingle-sample Gene Set Enrichment Analysis \u0026nbsp;(ssGSEA)\u003c/p\u003e\n\u003cp\u003eGene Set Variation Analysis \u0026nbsp;(GSVA)\u003c/p\u003e\n\u003cp\u003esupport vector machines \u0026nbsp;(SVM)\u003c/p\u003e\n\u003cp\u003eReceiver Operating Characteristic \u0026nbsp;(ROC)\u003c/p\u003e\n\u003cp\u003eArea under the curve \u0026nbsp;(AUC)\u003c/p\u003e\n\u003cp\u003egene set enrichment analysis \u0026nbsp;(GSEA)\u003c/p\u003e\n\u003cp\u003etranscription factors \u0026nbsp;(TFs)\u003c/p\u003e\n\u003cp\u003eprotein-protein interaction \u0026nbsp;(PPI)\u003c/p\u003e\n\u003cp\u003esubintimal fibroblasts \u0026nbsp;(SSF)\u003c/p\u003e\n\u003cp\u003esynovial intimal fibroblasts \u0026nbsp;(SIF)\u003c/p\u003e\n\u003cp\u003eendothelial cell \u0026nbsp;(EC)\u003c/p\u003e\n\u003cp\u003esmooth muscle cell \u0026nbsp;(SMC)\u003c/p\u003e\n\u003cp\u003eproliferating immune cell \u0026nbsp;(ProIC)\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;FOLR2\u003csup\u003e+\u003c/sup\u003emacrophages \u0026nbsp;(FOLR2\u003csup\u003e+\u003c/sup\u003eMac)\u003c/p\u003e\n\u003cp\u003eIL1B\u003csup\u003e+\u003c/sup\u003emacrophages \u0026nbsp;(IL1B\u003csup\u003e+\u003c/sup\u003eMac)\u003c/p\u003e\n\u003cp\u003einflammation-related DEGs \u0026nbsp;(IRGs)\u003c/p\u003e\n\u003cp\u003eingle-sample Gene Set Enrichment Analysis (ssGSEA)\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the National Natural Science Foundation of China (82371751).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data needed to evaluate the conclusions in the paper are presented in the main manuscript and the Supplementary Information.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWSC and ZMY designed the study. HGL conducted and interpreted the bioinformatics analysis. WGZ and SYY prepared the draft of the manuscript. All authors revised and approved the final manuscript\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eKraus VB, Blanco FJ, Englund M, Karsdal MA, Lohmander LS. Call for standardized definitions of osteoarthritis and risk stratification for clinical trials and clinical use. Osteoarthritis Cartilage. 2015;23(8):1233\u0026ndash;41.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWinter AR, Collins JE, Katz JN. The likelihood of total knee arthroplasty following arthroscopic surgery for osteoarthritis: a systematic review. 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Rheumatol Int. 2011;31(10):1325\u0026ndash;31.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bioelectronic-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"beme","sideBox":"Learn more about [Bioelectronic Medicine](https://bioelecmed.biomedcentral.com)","snPcode":"42234","submissionUrl":"https://submission.springernature.com/new-submission/42234/3","title":"Bioelectronic Medicine","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Osteoarthritis, lumbar disc degeneration disease, differentially expressed genes, hub genes, immune response, drug treatment","lastPublishedDoi":"10.21203/rs.3.rs-4860142/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4860142/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eOsteoarthritis (OA) and lumbar disc degeneration disease (IDD) are both degenerative and chronic inflammatory diseases that directly and profoundly impact human quality of life and medical services.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eTo better understand how IDD is related to OA, we used transcriptomic and single-cell analysis to look for molecular and cellular overlaps between the two diseases in this work. Two gene expression profiles (GSE124272 and GSE55235) from the Gene Expression Omnibus database are downloaded to detect common differentially expressed genes (DEGs) for OA and IDD diseases to find similar pathways, possible medicines, and regulatory networks. The single-cell transcriptome of synovial tissues sequenced from OA patients was analyzed for the immune characteristics of the shared DEGs.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eBased on RNA-seq datasets, 184 DEGs shared by these two diseases were found. 114 hub genes were then identified using degree and Matthew's correlation coefficient algorithms based on creating the Protein-protein interactions network. Interactions between transcription factors and shared hub genes, coregulatory networks between miRNAs and these hub genes, and protein-drug interactions were found. Moreover, scRNA-seq of cells of synovial tissues from OA patients revealed that 30 of these hub genes were highly enriched in the inflammatory macrophages (IL1B\u003csup\u003e+\u003c/sup\u003eMac). Finally, CSF1 and S100A12 were identified as potential biomarkers by machine learning, and their area under the curve in IDD and OA is greater than 0.7 by the receiver operating characteristic curve analysis.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eIDD might contribute to the advancement of OA by inciting cytokine storms, thereby enhancing comprehension and treatment strategies for OA patients accompanied by IDD. Moreover, the identified candidate drugs, DEG-regulated networks, and potential diagnostic genes (CSF1 and S100A12) offer promising therapeutic avenues for addressing both conditions.\u003c/p\u003e","manuscriptTitle":"Bioinformatics approach to explore the common pathogenic genes between lumbar disc degeneration and osteoarthritis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-10-08 00:51:05","doi":"10.21203/rs.3.rs-4860142/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-09-01T17:16:58+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-09-01T17:03:43+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-08-28T01:08:34+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"220494612648467479532936709080505554048","date":"2024-08-23T11:26:02+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"262747316463975186735646255097542603673","date":"2024-08-17T05:13:51+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-08-09T20:19:59+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-08-09T12:59:31+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-08-09T12:57:36+00:00","index":"","fulltext":""},{"type":"submitted","content":"Bioelectronic Medicine","date":"2024-08-05T07:48:10+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bioelectronic-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"beme","sideBox":"Learn more about [Bioelectronic Medicine](https://bioelecmed.biomedcentral.com)","snPcode":"42234","submissionUrl":"https://submission.springernature.com/new-submission/42234/3","title":"Bioelectronic Medicine","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"660749fc-662c-4421-a336-9f5913b86819","owner":[],"postedDate":"October 8th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2024-10-30T13:09:27+00:00","versionOfRecord":[],"versionCreatedAt":"2024-10-08 00:51:05","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4860142","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4860142","identity":"rs-4860142","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}