Study on the Molecular Mechanism amd Immune Cell Infiltration of Bushenhuoluo Decoction in Osteoarthritis Treatment Based on Network Pharmacology

Study on the Molecular Mechanism amd Immune Cell Infiltration of Bushenhuoluo Decoction in Osteoarthritis Treatment Based on Network Pharmacology | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Study on the Molecular Mechanism amd Immune Cell Infiltration of Bushenhuoluo Decoction in Osteoarthritis Treatment Based on Network Pharmacology Min Zhao, Jing Huang, Lei Cao, Xiang Zhou, Peng Wang, Keqin Li, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5765774/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Osteoarthritis (OA) is a common degenerative joint disorder marked by the deterioration of cartilage, joint discomfort, and inflammation. The Bushenhuoluo Decoction (BSHLD) is a traditional Chinese remedy utilized for the management of OA. The aim of this study is to examine and elucidate the mechanism of BSHLD and how the compound interacts with the target of OA. Methods We used a network pharmacology approach that integrates multiple bioinformatics techniques to study OA. First, we collected and analyzed OA datasets from the Gene Expression Omnibus with the R package GEOquery. To identify key differentially expressed genes (DEGs), we performed differential expression analysis using the limma package. We obtained target genes from the Online Mendelian Inheritance in Man (OMIM) and the Comparative Toxicogenomics Database (CTD). Additionally, we obtained components of traditional Chinese medicine and their targets from the Traditional Chinese Medicine Systems Pharmacology Database (TCMSP). To enhance our understanding of the biological processes and pathways involved, we conducted enrichment analyses using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). We constructed protein-protein interaction networks to pinpoint crucial genes, which we then validated using receiver operating characteristic curve analysis. Lastly, we assessed immune cell infiltration through the CIBERSORT algorithm. Results Differential expression analysis using the limma package identified key difference genes (KDGs) by intersecting DEGs with OA-RELAted genes, TCM target genes, and RBIRGs, resulting in 16 KDGs. GO and KEGG pathway enrichment analyses revealed significant biological processes and pathways, including muscle cell proliferation and PI3K-Akt signaling. Protein-protein interaction (PPI) networks were constructed using STRING, and hub genes were identified through CytoHubba algorithms, highlighting nine hub genes, including MYC and CDKN1A. Expression validation and ROC curve analysis demonstrated the diagnostic potential of hub genes, with HSP90AA1 , MYC , and CDKN1A showing high accuracy (AUC > 0.9). The immune infiltration analysis showed a significant positive corRELAtion between the hub gene MYC and activated mast cells. There was a significant negative corRELAtion between hub gene CDKN1A and immune cell Mast cells resting. Conclusion These findings provide valuable insights into the molecular interactions of BSHLD in OA treatment, potentially revealing therapeutic targets and pathways for future studies. Osteoarthritis Traditional Chinese Medicine Bioinformatics Network pharmacology Immune Infiltration Analysis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Introduction OA is a widespread and incapacitating disorder marked by the deterioration of articular cartilage and the adjacent bone, primarily impacting the knees, hips, and hands. This condition represents the most prevalent type of arthritis, influencing millions globally. In the United States, it is estimated that around 32.5 million adults are impacted by OA[ 1] . The condition notably affects patients' quality of life by inducing pain, stiffness, and limitations in mobility, positioning it as a predominant factor contributing to disability in older adults[ 2] . Existing therapeutic strategies for OA predominantly emphasize alleviating symptoms rather than altering the disease's progression. These strategies encompass non-pharmacological methods, including physical therapy and weight management, alongside pharmacological options such as NSAIDs, and surgical procedures, such as joint replacement, reserved for advanced cases[ 3] . Long-term use of NSAIDs could lead to gastrointestinal, cardiovascular, and renal complications. Consequently, there is a growing interest in alternative therapies, including Traditional Chinese Medicine (TCM) , which has been used for centuries to treat various ailments, including OA . Several studies have investigated the efficacy of TCM in the management of OA. For example, a systematic review and meta-analysis reported that TCM formulations, including herbs such as Du Huo (Angelica pubescens), Mu Gua (Chaenomeles speciosa), and Chuan Niu Xi (Cyathula officinalis), showed promising results in reducing pain and improving function in OA patients. Another study highlighted the anti-inflammatory and chondroprotective effects of TCM herbs, suggesting potential mechanisms by which these treatments might alleviate OA symptoms . Notwithstanding these encouraging results, the molecular pathways that elucidate the effects of TCM on OA remain substantially uninvestigated. TCM has been employed for the management of knee osteoarthritis for an extended period. Originating from its local and systemic applications within the realm of knee joint therapy, TCM has demonstrated efficacy in alleviating pain, enhancing joint functionality, and safeguarding joint integrity. The utilization of traditional Chinese medicine has undergone rigorous validation through extensive clinical practice. Nonetheless, the intricate "multi-component" and "multi-functional" nature of TCM has resulted in its specific mechanisms of action not being universally acknowledged. Network pharmacology offers a means to elucidate the intricate interconnections among drugs, targets, and diseases, employing high-throughput screenings, network visualizations, and analyses of network topology to accurately predict and evaluate the mechanisms through which TCM compounds exert their effects. This approach is characterized by its "multi-components, multi-targets, and multi-pathways" framework, thereby streamlining the understanding of complex substances. We propose that the therapeutic effects of the Huoluo prescription on knee osteoarthritis are mediated by its inherent characteristics of "multi-components, multi-targets, and multi-pathways." BSHLD is an empirical formula for treating osteoarthritis with the syndrome of "kidney deficiency and meridian obstruction". This formula has achieved good results in treating knee osteoarthritis. However, the specific molecular biological mechanisms are not yet fully understood and require further research. Network pharmacology represents a burgeoning discipline that merges systems biology with bioinformatics to elucidate the intricate interactions between pharmaceuticals and biological systems. Through the construction of networks that illustrate drug-target interactions, this methodology offers the potential to pinpoint prospective therapeutic targets and clarify the mechanisms underlying the actions of multi-component drugs, particularly those utilized in TCM . The main aim of this study was to clarify the molecular mechanisms through which TCM herbs exert their therapeutic effects on OA. By employing network pharmacology alongside bioinformatics methodologies, we sought to identify critical target genes and pathways associated with OA, thereby providing a scientific foundation for the application of TCM in the treatment of this condition. This investigation holds promise for revealing novel therapeutic targets and facilitating the creation of more effective and safer therapeutic options for OA. Materials and Methods 2.1 Data Download The R package GEOquery[ 4] (Version 2.70.0) downloaded datasets GSE55235 and GSE55457[ 5] from the GEO database[ 6] for osteoarthritis research, both from Homo sapiens with synovial tissue on GPL96, including 10 OA and 10 Control samples each. The specific information is shown in Table 1. The GeneCards[ 7] and MSigDB[ 8] databases identified 2262 and 319 ribosome generation-related genes (RBIRGs), respectively, yielding a total of 2306 unique RBIRGs after merging and removing duplicates, as detailed in Table S1. The R package sva[ 9] (3.50.0) debatched Datasets GSE55235 and GSE55457 into Combined GEO datasets with 20 OA and 20 Control samples, which were then standardized using limma[ 10] (3.58.1) and normalized. PCA was performed on expression matrices pre- and post-batch effect removal to verify the effectiveness of the debatching[ 11] , allowing for dimensionality reduction and visualization in 2D or 3D graphs. 2.2 Osteoarthria-target gene acquisition and screening Osteoarthritis target genes were collected from the OMIM[ 12] and CTD[ 13] databases using "Osteoarthritis" as a keyword, yielding 30 genes from OMIM and 1116 from CTD with an Inference Score >19, resulting in 1132 osteoarthritis target-related genes (OARGs) after de-duplication, detailed in Table S2. 2.3 Acquisition and screening of traditional Chinese medicine components and targets Duhuo, Mugau, Chuanniuxi, Yiyiren, Xixin, Baishao, and She were downloaded from TCMSP[ 14] , along with njincao, Duzhong, Guizhi, Weilingxian, and Fangfeng's composition data. Components with Oral Bioavailability (OB) > 40% and drug-likeness (DL) > 0.25 were screened, and their targets predicted via TCMSP[ 15] . UniProtKB[ 16] converted target names to gene names, and Cytoscape[ 17] created the traditional Chinese medicine-ingredient-target network diagram, detailed in Table S3. 2.4 Key differential genes Samples were categorized into OA and Control, with differential gene analysis using the limma R package to identify DEGs based on |logFC| > 0 and adj.p < 0.05, visualized with ggplot2. KDGs from OARGs, TCMSRGs, RBIRGs, and DEGs were identified via Venn diagrams, and heatmaps were created with pheatmap. 2.5 Gene ontology (GO) and pathway (KEGG) enrichment analysis We used the R package clusterProfiler[ 18] (version 4.10.0) to perform GO[ 19] and KEGG enrichment analysis[ 20] on key differential genes. We set the criteria of adj.p < 0.05 and FDR < 0.25, applying the Benjamini-Hochberg correction method. 2.6 Protein interaction (PPI) network and Hub gene screening The STRING database[ 21] was utilized to build a PPI network using KDGs with high confidence scores (≥0.700). The genes that interact within the PPI network were analyzed using five algorithms from the CytoHubba[ 22] plug-in in Cytoscape[ 23] : MCC, MNC, Degree, EPC, and Closeness[ 24] .After calculating the KDG scores, the top 10 KDGs were identified for further analysis. A Venn diagram was used to analyze the overlap of genes identified by the five algorithms, helping to pinpoint hub genes among the key differential genes. 2.7 Validation of differential expression of Hub genes and ROC curve analysis To investigate the differences in hub gene expression between OA and control samples, a comparative analysis was conducted. The R package pROC[ 25] was utilized to plot the ROC curve. Additionally, it was used to calculate the AUC value, which indicates diagnostic effectiveness. The AUC values range from 0.5 to 1, with higher values indicating superior diagnostic performance. AUC values between 0.5 and 0.7 reflect low accuracy, those from 0.7 to 0.9 indicate moderate accuracy, and values exceeding 0.9 signify high accuracy. 2.8 Hub gene cor RELA tion analysis and Friends analysis were performed The Spearman algorithm analyzed hub gene expression corRELAtions in Combined GEO Datasets, with results visualized through corRELAtion and chord diagrams using R packages igraph and ggraph. CorRELAtion coefficients indicated weak (r 0.8) corRELAtions. GO semantic comparison quantitatively assesses gene similarity, aiding bioinformatics analyses, while functional similarity of hub genes was analyzed using GOSemSim[ 26] . 2.9 Immune Infiltration analysis of Osteoarthritis (CIBERSORT) CIBERSORT[ 27] utilizes linear support vector regression to analyze transcriptome data for immune cell composition in mixed samples. It applies the LM22 gene matrix and filters for positive immune cell scores to create both an infiltration matrix and a proportion bar chart. The R package ggplot2 (Version 3.4.4) is used to show differences in immune cell expression between OA and Control samples in the combined GEO Datasets, helping to identify significant immune cells for further analysis. Spearman corRELAtion calculates RELAtionships among immune cells, which are visualized with the pheatmap package (Version 1.0.12). Additionally, corRELAtions between hub genes and immune cells are analyzed, retaining results with p < 0.05, and displayed using ggplot2. 2.10 Statistical analysis All data analysis was conducted using R software (Version 4.2.2). Continuous variable comparisons used Student's T-Test for normal distributions and Mann-Whitney U Test for non-normal distributions. Kruskal-Wallis test compared three or more groups, while Spearman corRELAtion analyzed molecule corRELAtions. P values were two-sided, with significance at p < 0.05. Results 3.1 Technology Roadmap (Fig1) 3.2 Merging of osteoarthritis datasets The R package sva removed batch effects from OA datasets GSE55235 and GSE55457, creating Combined GEO datasets. Boxplots (Fig2A-B) compared expression values pre- and post-removal, while PCA plots (Fig2C-D) assessed low-dimensional feature distribution. Results indicated effective elimination of batch effects in the OA dataset. 3.3 Target prediction of traditional Chinese medicine components The TCMSP database provided chemical components of 11 traditional Chinese medicines, screening 53 active molecules with OB > 40% and drug-likeness > 0.25. The detailed information is shown in Table 2. Protein targets for these components were predicted, yielding 224 targets converted to gene names via UniProtKB. The detailed information is shown in Table S3. Cytoscape software illustrated the TCM-component-target network (Fig3) with 11 components, 53 active molecules, and 224 targets. 3.4 Key differentially expressed genes RELA ted to osteoarthritis The Combined GEO Datasets were categorized into osteoarthritis and control samples, leading to the identification of 2,759 DEGs through the application of the R package limma, with criteria set at |logFC| > 0 and adjusted p-value < 0.05. This cohort comprised 1,428 genes exhibiting up-regulation and 1,331 genes displaying down-regulation, as depicted in a volcano plot (Fig 4A). To derive Key Differentially Expressed Genes (KDGs), DEGs meeting the aforementioned criteria, in conjunction with Osteoarthritis-Associated Regulatory Genes (OARGs), Traditional Chinese Medicine Specific Regulatory Genes (TCMSPRGs), and Reinforced Bioinformatics Interaction Regulatory Genes (RBIRGs), were subjected to Venn diagram analysis, culminating in the identification of 16 KDGs: CDKN2A , CDK1 , MMP9 , NR3C1 , MYC , CDKN1A , VEGFA , HSP90AA1 , FASN , EGFR , APOB , PTEN , CDK2 , HSPA5 , IL10 , and RELA . Subsequently, the expression variations of these KDGs across different sample groups within the Combined GEO Datasets were assessed, and a heatmap was generated utilizing the R package pheatmap. 3.5 Gene ontology (GO) and pathway (KEGG) enrichment analysis The GO and KEGG enrichment analyses conducted on 16 pivotal differential genes highlighted their relevance to muscle cell proliferation, glandular development, and a spectrum of biological processes, cellular components, and pathways pertinent to osteoarthritis, as elaborated in Table 3 and illustrated through bubble plots (Fig. 5A). A network diagram was generated from the GO and KEGG enrichment analyses, effectively depicting biological processes, cellular components, molecular functions, and pathways, with nodes of larger size representing a greater number of associated molecules and annotations. 3.6 Construction of protein interaction (PPI) network and regulatory network A protein-protein interaction analysis using the STRING database identified a PPI network consisting of 16 KDGs, which revealed 13 RELAted KDGs (Fig6A). The scores for these genes were calculated using five CytoHubba algorithms: MCC (Fig6B), MNC (Fig6C), Degree (Fig6D), EPC (Fig6E), and Closeness (Fig6F), which were then used to rank the top 10 KDGs for network visualization. The colors of the circles represent the scores, with red indicating high scores and yellow indicating low scores. A Venn diagram (Fig6G) illustrated the intersection of genes identified by the algorithms, highlighting nine hub genes: MYC , CDKN1A , EGFR , CDKN2A , PTEN , HSP90AA1 , CDK1 , CDK2 , and RELA . 3.7 Differential expression verification and ROC curve analysis of Hub genes The group comparison figure (Fig 7A) illustrates the differential expression of 9 hub genes between OA samples and control samples in the combined GEO datasets. The analysis indicated that the expression levels of 6 hub genes were highly statistically significant (p < 0.001) between OA and control samples (Fig 7A). These genes include CDK2 , CDKN1A , CDKN2A , EGFR , HSP90AA1 , and MYC . The expression levels of three additional hub genes— CDK1 , PTEN , and RELA —were also highly statistically significant (p < 0.001) in OA compared to control samples. Finally, ROC curves were generated using the R package pROC to assess the expression of hub genes in the combined GEO datasets. The ROC curve (Fig7B-D) showed that the expression levels of hub genes ( HSP90AA1 , MYC and CDKN1A ) had high accuracy (AUC > 0.9) in the classification of OA samples and Control samples; The expression levels of CDK1 , CDK2 , CDKN2A , EGFR , PTEN and RELA showed certain accuracy (0.7 < AUC < 0.9) in the classification of OA samples and Control samples. 3.8 Cor RELA tion analysis and Friends analysis We calculated the corRELAtions among the nine hub genes in the Combined GEO Datasets and presented the results using corRELAtion and chord diagrams (Fig 8A).The results indicated that the hub genes were predominantly positively corRELAted with one another. Finally, we used the scores from the functional similarity analysis to identify genes that play significant roles in the biological processes of Osteoarthritis (Fig 8B). The results showed that RELA plays a crucial role in Osteoarthritis, being the gene with the highest score approaching the critical cut-off value of 0.65. 3.9 Immune Infiltration Analysis of Osteoarthritis (CIBERSORT) The CIBERSORT algorithm assessed the abundance of infiltration for 22 immune cells in the Combined GEO Datasets. This analysis produced a bar chart of immune cell proportions (Fig9A) and group comparison plots (Fig9B), which illustrate the differences in immune cell infiltration between OA samples and control samples. The results showed significant expression levels (p < 0.05) for four immune cells in OA and control samples: eosinophils, M0 macrophages, plasma cells, and Tregs. One immune cell that demonstrated highly statistically significant expression (p < 0.01) in both OA and control samples was activated NK cells. Three immune cells exhibited highly significant expression (p < 0.001): activated mast cells, activated NK cells, and resting CD4 memory T cells. The corRELAtion heat map (Fig9C) illustrated the infiltration abundance of eight immune cells. It showed a strong positive corRELAtion between activated mast cells and resting CD4 memory T cells (r = 0.63), and a strong negative corRELAtion between resting and activated mast cells (r = -0.74). The corRELAtion bubble plots indicated a significant positive corRELAtion between the hub gene MYC and activated mast cells (r > 0.0, p < 0.05), while CDKN1A showed a significant negative corRELAtion with resting mast cells (r < 0.0, p < 0.05)(Fig9D). Discussion OA is a common degenerative joint disorder that profoundly affects the quality of life and daily activities of those diagnosed with it. This condition is marked by the degradation of joint cartilage and the bone beneath it, resulting in symptoms such as pain, stiffness, and reduced mobility. The impact of OA is considerable, especially among the elderly, as it ranks among the foremost causes of disability on a global scale. Furthermore, OA is acknowledged as a critical public health concern, with its incidence rising at a concerning rate in recent decades. Recent studies indicate that approximately 250 million individuals worldwide are affected by knee OA, with a notable rise in incidence attributed to aging populations and increasing obesity rates[ 28] . Moreover, it is anticipated that the worldwide prevalence of knee OA will increase, especially in low- and middle-income nations where shifts in lifestyle and urban development lead to an elevated risk of factors such as reduced physical activity and alterations in diet[ 29] . The existing treatment strategies predominantly emphasize analgesic medications, which, although successful in relieving discomfort, can lead to negative side effects, including gastrointestinal and cardiovascular issues stemming from prolonged usage. Therefore, there is an urgent necessity to investigate alternative treatment strategies that reduce adverse effects while improving therapeutic effectiveness. This study aims to clarify the molecular mechanisms of OA by combining bioinformatics methods with TCM analysis. We systematically downloaded and processed OA-related datasets from the GEO database, including GSE55235 and GSE55457, which contained synovial tissue samples from OA patients and healthy controls. After normalizing the data and removing batch effects, we identified a comprehensive set of ribosome biogenesis-related and osteoarthritis-related genes from various databases, including GeneCards and CTD. Additionally, we examined the active components of different TCM herbs and their targets, creating a network of herbal components and their interactions with OA-related genes. Identifying KDGs and hub genes through PPI networks, along with analyses such as GO and KEGG pathway enrichment, offered insights into the biological processes and pathways involved in OA. This multifaceted approach not only highlights the potential therapeutic targets within TCM for OA but also sets the stage for a deeper discussion on the implications of our findings in the context of OA pathogenesis and treatment strategies. The differential expression analysis conducted on the integrated GEO datasets revealed a total of 2,759 DEGs with a significance threshold of |logFC| > 0 and adj.p < 0.05. Among these, 1,428 genes were upregulated, while 1,331 were downregulated. This substantial number of DEGs underscores the complexity of osteoarthritis pathology and highlights the potential for identifying novel therapeutic targets. The intersection of these DEGs with OARGs, TCMSRGs, and RBIRGs yielded 16 KDGs. The identification of these KDGs is critical, as they may play pivotal roles in the molecular mechanisms underlying osteoarthritis and could serve as biomarkers for disease progression or therapeutic response. The enrichment analysis revealed that the KGDs were predominantly associated with biological processes including the proliferation of muscle cells and the regulation of DNA metabolic processes, in addition to pathways such as the PI3K-Akt signaling pathway. This particular signaling pathway is vital for the survival and proliferation of chondrocytes, which are critical for preserving the structural integrity of cartilage. Activation of this pathway promotes cell survival by inhibiting apoptosis and enhancing cell proliferation. Research has indicated that the heightened expression of Tra2β, a protein known to stimulate the PI3K/Akt signaling pathway, results in a substantial upregulation of extracellular matrix constituents, particularly collagen II. Concurrently, this overexpression leads to a reduction in the levels of inflammatory and apoptotic markers in chondrocytes that are exposed to interleukin-1β (IL-1β) stimulation[ 30] . Furthermore, the utilization of growth factors like insulin-like growth factor-1 (IGF-1) has been linked to a reduction in matrix metalloproteinases (MMPs) and apoptotic indicators, thereby reinforcing the notion that the PI3K/Akt signaling pathway is essential for the survival of chondrocytes in inflammatory environments[ 31] . Overall, the PI3K-Akt pathway is integral to the maintenance of chondrocyte health, particularly in the context of OA, where its dysregulation can lead to increased apoptosis and cartilage degradation. The analysis of the PPI network revealed nine central hub genes, such as MYC , CDKN1A , and PTEN , that may function as critical regulatory points within the disease context. The hub genes play important roles in maintaining cell homeostasis and responding to external stimuli, and could be potential diagnostic markers or therapeutic targets. Future studies could explore whether there is a synergistic effect among the hub genes in promoting disease progression, and whether existing drugs targeting these hub genes have shown efficacy. The hub genes identified exhibited markedly significant disparities in expression between OA samples and control groups within the integrated GEO dataset. Notably, the expression levels of HSP90AA1 , MYC , and CDKN1A demonstrated a high degree of accuracy in distinguishing OA samples from control samples, while the expression levels of CDK1 , CDK2 , CDKN2A , EGFR , PTEN , and RELA displayed a moderate level of accuracy. The strong statistical significance and classification accuracy of hub genes like HSP90AA1, MYC, and CDKN1A in distinguishing osteoarthritis samples from control samples highlight their potential as biomarkers or therapeutic targets for the disease. MYC is a well-known oncogene that plays a key role in regulating cell growth, differentiation, and death. Its dysregulation is linked to several cancers, including osteoarthritis. In a similar vein, MYC , recognized as a transcription factor that plays a significant role in cell growth and programmed cell death, has been associated with the advancement of OA. Elevated levels of MYC in inflammatory conditions suggest its involvement in chondrocyte hypertrophy and apoptosis, marking it as a potential biomarker for disease severity[ 32] . HSP90AA1 , a critical chaperone protein, has been shown to be down-regulated in the blood and cartilage of OA patients, indicating its potential role in the disease's pathogenesis. Specifically, research demonstrates that HSP90AA1 deficiency corRELAtes with increased inflammation, oxidative stress, and chondrocyte apoptosis, suggesting that it is vital for maintaining chondrocyte homeostasis and could serve as a therapeutic target for OA[ 33] . CDKN1A , also known as p21, is another crucial player that regulates the cell cycle and has been linked to the senescence of chondrocytes in OA. The expression levels of this biomarker exhibit substantial variations in OA tissues, suggesting its promise as both a diagnostic indicator and a therapeutic target[ 34] . Collectively, these molecular markers not only enhance our understanding of the pathophysiological processes underlying OA but also pave the way for the development of targeted therapeutic strategies that focus on the regulation of their expression and functional activity. RELA plays a crucial role in Osteoarthritis, being the gene with the highest score approaching the critical cut-off value of 0.65.In the context of OA, PTEN and RELA have emerged as significant molecular players in the disease's pathophysiology. PTEN is known to regulate cell survival and apoptosis, with its dysregulation contributing to chondrocyte death and cartilage degradation in OA. Studies have shown that downregulation of miR-29a-3p can lead to increased PTEN expression, promoting chondrocyte apoptosis and exacerbating OA progression[ 35] . Conversely, RELA plays a significant role in the inflammatory response linked to OA. The activation of the NF-κB signaling pathway, in which RELA is pivotal, results in the upregulation of several pro-inflammatory cytokines and matrix metalloproteinases. These factors contribute to the degradation of cartilage. Understanding the interplay between PTEN and RELA in OA could provide insights into novel therapeutic targets aimed at modulating inflammation and promoting cartilage repair. Overall, ongoing research into these molecular pathways holds promise for developing more effective treatments for knee osteoarthritis. The analysis of immune infiltration indicated notable alterations in the prevalence of eight distinct immune cell types, with activated mast cells and resting CD4 memory T cells exhibiting the strongest positive corRELAtion. Mast cells (MCs) have been identified as crucial contributors to the pathophysiology of OA, playing a vital role in the inflammatory environment that typifies this degenerative joint condition. Research has demonstrated that MCs are prevalent in the synovial tissue of OA patients, where they interact with various immune cells and contribute to synovial inflammation. A study employing CIBERSORT for gene expression analysis revealed that the proportion of resting mast cells is significantly altered in OA compared to healthy joint tissues, highlighting their potential role in disease progression[ 36] . Moreover, mast cells (MCs) are associated with the breakdown of the extracellular matrix, predominantly via the secretion of proteolytic enzymes, including tryptase. This enzyme has the capability to cleave proteoglycan-4 (PRG4), an essential component for the lubrication of joints[ 37] . This cleavage not only reduces the lubricating properties of PRG4 but also activates inflammatory pathways, further exacerbating joint damage. The interaction of MCs with other immune cells, such as macrophages, suggests a complex network of signaling that promotes chronic inflammation and tissue remodeling in OA [ 38] . Thus, the presence and activation of mast cells in the OA-affected joint may serve as a catalyst for the disease's inflammatory processes, making them a potential target for therapeutic interventions.These findings suggest that immune microenvironment alterations might contribute to the pathological process of osteoarthritis, providing a basis for immune therapy. Further studies are needed to understand how different immune cell types affect the OA process and whether modulating immune infiltration could improve symptoms. This study uses several strong methodologies that improve the robustness and reliability of its findings. First, integrating multiple datasets from the GEO database, specifically GSE55235 and GSE55457, enables a comprehensive analysis of OA. This larger sample size enhances the statistical power of the differential expression analysis. The use of advanced bioinformatics tools, such as the R packages limma and sva, for normalization and batch effect removal helps to process the data rigorously, minimizing potential biases that could affect the results. Additionally, the systematic approach to identifying key differential genes involves intersecting various gene sets, including those related to osteoarthritis, ribosome biogenesis, and traditional Chinese medicine targets. This method offers a multifaceted perspective on the molecular mechanisms underlying OA. The construction of a PPI network using STRING and the subsequent identification of hub genes through multiple algorithms in Cytoscape further strengthens the biological relevance of the identified targets. Finally, validating hub gene expression through ROC curve analysis confirms their diagnostic potential and highlights the findings' relevance in clinical settings. Collectively, these methodological rigor and comprehensive analyses contribute significantly to the understanding of osteoarthritis and the potential therapeutic implications of TCM. This study has several limitations that should be noted. First, relying on publicly available datasets like GEO may introduce biases. These biases can arise from variations in sample collection, processing, and annotation in different studies. Although we employed rigorous filtering criteria to select relevant datasets, the inherent heterogeneity of the samples could affect the generalizability of our findings. Second, integrating multiple databases for gene and protein interaction analysis strengthens our results. However, it also raises concerns about the accuracy and completeness of the data. The potential for missing or incorrectly labeled genes in the databases could lead to incomplete networks and misinterpretation of their biological significance. Furthermore, the computational methods used for differential expression analysis and hub gene identification, while widely accepted, are subject to limitations in sensitivity and specificity, which may impact the identification of truly significant genes. Finally, this study focuses mainly on bioinformatics analyses. The absence of experimental validation for the identified hub genes and their interactions, either in vitro or in vivo, limits our ability to draw firm conclusions about their roles in osteoarthritis pathology. Future studies should aim to address these limitations by incorporating experimental approaches to validate the computational predictions and enhance the understanding of the underlying biological mechanisms. Conclusion In conclusion, our study elucidates the intricate molecular mechanisms underlying the therapeutic effects of the BSHLD in OA through a comprehensive network pharmacology approach. The identification of KDEGs and their subsequent analysis revealed significant biological processes and pathways that are potentially modulated by BSHLD, particularly those RELAted to inflammation and cartilage metabolism. Notably, the hub genes MYC , CDKN1A , and HSP90AA1 demonstrated high accuracy in classifying OA samples from controls, as evidenced by their AUC values exceeding 0.9. Furthermore, the corRELAtion analysis revealed significant RELAtionships among the hub genes and their association with immune cell infiltration, suggesting a complex interplay between immune responses and OA pathology. These findings offer promising molecular targets for osteoarthritis treatment. Looking forward, integrating these results with clinical studies and wet lab experiments could enhance the understanding and therapeutic strategies for osteoarthritis, paving the way for more effective interventions. Table 1 GEO Microarray Chip Information GSE55235 GSE55457 Platform GPL96 GPL96 Species Homo sapiens Homo sapiens Tissue Osteoarthritis Osteoarthritis Samples in Osteoarthritis group 10 10 Samples in Control group 10 10 Reference PMID: 24690414 PMID: 24690414 GEO, Gene Expression Omnibus. Table 2 ADME Parameters of Compounds MOL_ID molecule_name ob dl MOL001910 11 alpha, 12 alpha - epoxy - 3 beta - 30 - norolean - 20-23 - dihydroxy - en - 28, 12 beta - olide 64.77 0.38 MOL001918 paeoniflorgenone 87.59 0.37 MOL001919 (9 8 r, 3 s, 5 r, r, s, s) of 14-3 - dihydroxy in 2-4,4,8,10,14 -,3,5,6,7,9 - hexahydro pentamethyl - 2-1 h - cyclopenta [a] phenanthrene - 15 - di one 43.56 0.53 MOL001921 Lactiflorin 49.12 0.80 MOL001924 paeoniflorin 53.87 0.79 MOL001925 paeoniflorin_qt 68.18 0.40 MOL001928 albiflorin_qt 66.64 0.33 MOL000211 Mairin 55.38 0.78 MOL012286 Betavulgarin 68.75 0.39 MOL000098 quercetin 46.43 0.28 MOL003608 O-Acetylcolumbianetin 60.04 0.26 MOL004778 [(1R,2R)-2,3-dihydroxy-1-(7-methoxy-2-oxochromen-6-yl)-3-methylbutyl] (Z)-2-methylbut-2-enoate 46.03 0.34 MOL004792 nodakenin 57.12 0.69 MOL002058 40957-99-1 57.20 0.62 MOL004782 [(1R,2R)-2,3-dihydroxy-1-(7-methoxy-2-oxochromen-6-yl)-3-methylbutyl] 3-methylbutanoate 45.19 0.34 MOL000211 Mairin 55.38 0.78 MOL004367 olivil 62.23 0.41 MOL000443 Erythraline 49.18 0.55 MOL005922 Acanthoside B 43.35 0.77 MOL006709 AIDS214634 92.43 0.55 MOL007563 Yangambin 57.53 0.81 MOL009009 (+)-medioresinol 87.19 0.62 MOL009015 (-)-Tabernemontanine 58.67 0.61 MOL009027 Cyclopamine 55.42 0.82 MOL009029 Dehydrodiconiferyl alcohol 4,gamma'-di-O-beta-D-glucopyanoside_qt 51.44 0.40 MOL009031 Cinchonan-9-al, 6'-methoxy-, (9R)- 68.22 0.40 MOL009038 GBGB 45.58 0.83 MOL009053 4 - [(2 s, 3 r) - 5 - [(E) - 3 - hydroxyprop - 1 - enyl] - 7 - methoxy - 3 - methylol - 2, 3 - dihydrobenzofuran - 2 - yl] - 2 - methoxy - phenol 50.76 0.39 MOL009055 hirsutin_qt 49.81 0.37 MOL009057 liriodendrin_qt 53.14 0.80 MOL000098 quercetin 46.43 0.28 MOL008240 (E)-3-[4-[(1R,2R)-2-hydroxy-2-(4-hydroxy-3-methoxy-phenyl)-1-methylol-ethoxy]-3-methoxy-phenyl]acrolein 56.32 0.36 MOL000011 (2 r, 3 r) - 3 - (4 - hydroxy - 3 - methoxy - phenyl) - 5-2 - methylol - 2, 3 - methoxy - dihydropyrano [5, 6 - h] [1, 4] benzodioxin - 9 - one 68.83 0.66 MOL011730 11-hydroxy-sec-o-beta-d-glucosylhamaudol_qt 50.24 0.27 MOL011732 anomalin 59.65 0.66 MOL011737 divaricatacid 87.00 0.32 MOL011749 phelloptorin 43.39 0.28 MOL002644 Phellopterin 40.19 0.28 MOL001736 (-)-taxifolin 60.51 0.27 MOL004576 taxifolin 57.84 0.27 MOL011169 Peroxyergosterol 44.39 0.82 MOL000211 Mairin 55.38 0.78 MOL000098 quercetin 46.43 0.28 MOL000449 Stigmasterol 43.83 0.76 MOL005419 E (6, 8 e, 10 z, z, 12 of 14 e, e, e, z, 20 to 22 z, e, 24, 26 e) - 2,6,10,14,19,23,27,31,6,8,10,12,14,16,18,20,22,24,26 octamethyldotriaconta - 2 ,30-tridecaene 45.51 0.51 MOL000449 Stigmasterol 43.83 0.76 MOL005603 Heptyl phthalate 42.26 0.31 MOL001460 Cryptopin 78.74 0.72 MOL001558 sesamin 56.55 0.83 MOL002501 [(1S)-3-[(E)-but-2-enyl]-2-methyl-4-oxo-1-cyclopent-2-enyl] (1 r, 3 r) - 3 - [(E) - 3 - methoxy - 2 - methyl - 3 - oxoprop - 1 - enyl] - 2, 2-1 - carboxylate dimethylcyclopropane - 62.52 0.31 MOL002962 (3 s) - 7 - hydroxy - 3 - (2, 4 - trimethoxyphenyl) chroman - 4 - one 48.23 0.33 MOL001323 Sitosterol alpha1 43.28 0.78 MOL000449 Stigmasterol 43.83 0.76 ADME, Absorption, Distribution, Metabolism and Excretion; OB, Oral intake; DL, Drug-likeness; WM, Molecular Weight. Table 3 Results of GO and KEGG Enrichment Analysis for KDGs Ontology ID Description GeneRatio BgRatio pvalue p.adjust qvalue BP GO:0033002 muscle cell proliferation 6/15 233/18800 1.55e-08 1.3e-05 4.67375E-06 BP GO:0048732 gland development 7/15 431/18800 1.74e-08 1.3e-05 4.67375E-06 BP GO:0051052 regulation of DNA metabolic process 7/15 472/18800 3.25e-08 1.3e-05 4.67375E-06 BP GO:0007346 regulation of mitotic cell cycle 7/15 478/18800 3.55e-08 1.3e-05 4.67375E-06 BP GO:1902895 positive regulation of miRNA transcription 4/15 45/18800 3.83e-08 1.3e-05 4.67375E-06 CC GO:0000307 cyclin-dependent protein kinase holoenzyme complex 3/15 49/19594 6.55e-06 0.0006 0.000310135 CC GO:1902554 serine/threonine protein kinase complex 3/15 99/19594 5.45e-05 0.0015 0.000709639 CC GO:0042470 melanosome 3/15 109/19594 7.26e-05 0.0015 0.000709639 CC GO:0048770 pigment granule 3/15 109/19594 7.26e-05 0.0015 0.000709639 CC GO:1902911 protein kinase complex 3/15 115/19594 8.51e-05 0.0015 0.000709639 MF GO:0030332 cyclin binding 3/15 34/18410 2.58e-06 0.0002 7.26329E-05 MF GO:0031625 ubiquitin protein ligase binding 5/15 298/18410 2.83e-06 0.0002 7.26329E-05 MF GO:0044389 ubiquitin-like protein ligase binding 5/15 317/18410 3.82e-06 0.0002 7.26329E-05 MF GO:0001046 core promoter sequence-specific DNA binding 3/15 42/18410 4.93e-06 0.0002 7.26329E-05 MF GO:0035173 histone kinase activity 2/15 16/18410 7.39e-05 0.0020 0.000870859 KEGG hsa05215 Prostate cancer 7/15 97/8164 1.6e-10 2.43e-08 1.19505E-08 KEGG hsa05219 Bladder cancer 5/15 41/8164 7.19e-09 5.46e-07 2.68518E-07 KEGG hsa04151 PI3K-Akt signaling pathway 8/15 354/8164 5.7e-08 2.89e-06 1.41886E-06 KEGG hsa04218 Cellular senescence 6/15 156/8164 1.92e-07 7.29e-06 3.58685E-06 KEGG hsa05222 Small cell lung cancer 5/15 92/8164 4.47e-07 1.36e-05 6.68875E-06 GO, Gene Ontology; BP, Biological Process; CC, Cellular Component; MF, Molecular Function; KEGG, Kyoto Encyclopedia of Genes and Genomes; KDGS, Key Difference Genes. Declarations Data availability statement: The data sets presented in this study can be found in online repositories. Ethics Approval: This study does not contain any studies with hunman participants or animals performed by any of authors. GEO belong to public databases. The patients involved in the database have obtained ethical approval. Users can download relevant data for free for research and publish relevant articles. Our study is based on open source data, so there are no ethical issues and other conflicts of interest. Acknowledgement: None.We acknowledge TCGA and GEO database for providing their platforms and contributors for uploading their meaningful datasets. Declaration of Conflict of Interest: The authors declare that there are no conflicts of interestregarding the publication of this paper. Funding Source Declaration ： None. References Zhang Y, Wang Y, Xu J, Wang Z, Zhao W, Zhao C. Visceral adipose tissue and osteoarthritis, a two-sample Mendelian randomized study. 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Additional Declarations No competing interests reported. Supplementary Files TableS1RBIRGs.xlsx TableS2OARGs.csv TableS3TCMnodes.xlsx Cite Share Download PDF Status: Posted Version 1 posted 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-5765774","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":399133429,"identity":"96ac363a-e7e7-4178-9b50-a03bc6e5702b","order_by":0,"name":"Min Zhao","email":"","orcid":"","institution":"Wuhan Hospital of Traditional Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Min","middleName":"","lastName":"Zhao","suffix":""},{"id":399133430,"identity":"7e237b12-dfc7-4d36-b867-a74e1abc096a","order_by":1,"name":"Jing Huang","email":"","orcid":"","institution":"Wuhan Hospital of Traditional Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Jing","middleName":"","lastName":"Huang","suffix":""},{"id":399133431,"identity":"df3a6954-9240-4783-85c2-36253efca817","order_by":2,"name":"Lei Cao","email":"","orcid":"","institution":"Wuhan Hospital of Traditional Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Lei","middleName":"","lastName":"Cao","suffix":""},{"id":399133432,"identity":"24eb4c9e-8b7d-41dd-bcc7-aa7d693ca7a7","order_by":3,"name":"Xiang Zhou","email":"","orcid":"","institution":"Wuhan Hospital of Traditional Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Xiang","middleName":"","lastName":"Zhou","suffix":""},{"id":399133433,"identity":"a1b393e9-1254-486f-853e-4bf585889ec5","order_by":4,"name":"Peng Wang","email":"","orcid":"","institution":"Wuhan Hospital of Traditional Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Peng","middleName":"","lastName":"Wang","suffix":""},{"id":399133434,"identity":"dfb3d915-1312-4592-9bf2-a0a84ed61520","order_by":5,"name":"Keqin Li","email":"","orcid":"","institution":"Wuhan Hospital of Traditional Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Keqin","middleName":"","lastName":"Li","suffix":""},{"id":399133435,"identity":"a64b0cf6-ccd1-40b7-84d9-d008be838f25","order_by":6,"name":"Lei Zhang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAyUlEQVRIiWNgGAWjYBACfv72gw8//rNh5mdvIFKL5IwzycYSbGnskj0HiNRicCDBTICH7TC/wY0EYl124EAagwTPYWnJmY833mCosYkmqIOxufHYgwKJdGN+6bRiC4ZjabkNhLQwMxxIN5AwsE6WnJ1jJsHYcJiwFjaGBDMJngTm+g03zxCphQes5YAzs8ENHiK1SEgAA1myIY1ZsgfolwRi/GJ/HhSVDaCoPLzxxocaG8JakIGBRAIpyiFaSNUxCkbBKBgFIwMAAPSrPnCjP/yJAAAAAElFTkSuQmCC","orcid":"","institution":"Wuhan Hospital of Traditional Chinese Medicine","correspondingAuthor":true,"prefix":"","firstName":"Lei","middleName":"","lastName":"Zhang","suffix":""}],"badges":[],"createdAt":"2025-01-05 02:53:05","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5765774/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5765774/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":73416712,"identity":"ed0b9b27-ce58-4c8d-9ffb-5e4fb625ddb6","added_by":"auto","created_at":"2025-01-09 17:20:41","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":251214,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFlow Chart\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-5765774/v1/2f1720707b8dfcbbce5bc8fb.png"},{"id":73416717,"identity":"eac25176-f048-425e-be6d-3a10b8519083","added_by":"auto","created_at":"2025-01-09 17:20:41","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":232409,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eBatch Effects Removal of GSE55235 and GSE55457\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA. Go to the box plots of the Combined GEO Datasets distribution before batch processing. B. Go to the post-batch integrated GEO Datasets (Combined Datasets) distribution boxplots. C. PCA plot of the datasets before debatching. D. Go to the PCA map of the Combined GEO Datasets after batch processing. The osteoarthritis (OA) dataset GSE55235 is shown in blue and the osteoarthritis (OA) dataset GSE55457 is shown in purple.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-5765774/v1/2c80f8e97d9fbed8474e6a74.png"},{"id":73416707,"identity":"f0a8a67e-9320-49ce-b811-1706913ffb51","added_by":"auto","created_at":"2025-01-09 17:20:41","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":747547,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTraditional Chinese Medicine, Ingredients and Targets Network\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFIG. 3 Traditional Chinese Medicine, ingredients and targets network diagram. The orange oval is the TCM, the blue triangle is the ingredient, and the pink diamond is the target.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-5765774/v1/d5e8d4d468d8c63185961c0d.png"},{"id":73416720,"identity":"49f1fc88-4bd7-4d4a-866b-f97e4a2f6cea","added_by":"auto","created_at":"2025-01-09 17:20:42","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":124616,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDifferential Gene Expression Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA. Volcano plot of differentially expressed gene analysis of osteoarthritis (OA) samples and Control samples in the Combined GEO Datasets. B. Differentially expressed genes (DEGs) and Venn diagram of osteoarthritis-RELAted genes (OARGs), traditional Chinese medicine target genes (TCMSPRGs), ribosome generating genes (RBIRGs) in the integrated GEO Datasets (Combined Datasets). C. Heat map of key differentially expressed genes (KDGs) in the integrated GEO Datasets (Combined Datasets). Osteoarthritis (OA) samples are shown in blue, and Control (Control) samples are shown in pink. The orange-yellow color in the heat map represents high expression and purple represents low expression.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-5765774/v1/5117d5af7d8977189f044ab6.png"},{"id":73417436,"identity":"8ac3a2b2-4a8b-42cb-aaad-296e5c1206b3","added_by":"auto","created_at":"2025-01-09 17:28:41","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":269223,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGO Enrichment Analysis for KDGs\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA. Bar chart of gene ontology (GO) and pathway (KEGG) enrichment analysis results of key differential genes (KDGs) : biological process (BP), cellular component (CC), molecular function (MF) and biological pathway (KEGG). GO terms and KEGG terms are shown on the abscissa. B-e. Network diagram of gene ontology (GO) and pathway (KEGG) enrichment analysis results of key differential genes (KDGs) : BP (B), CC (C), MF (D) and KEGG (E). Purple nodes represent items, pink nodes represent molecules, and lines represent the RELAtionship between items and molecules. The screening criteria for gene ontology (GO) and pathway (KEGG) enrichment analysis were adj.p \u0026lt; 0.05 and FDR value (q value) \u0026lt; 0.25, and the p value correction method was Benjamini-Hochberg(BH).\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-5765774/v1/5f89f42ee52d2c53e4721390.png"},{"id":73417440,"identity":"52fdd625-74b9-49ee-a795-b73088fbc6b9","added_by":"auto","created_at":"2025-01-09 17:28:42","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":342931,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePPI Network and Hub Genes Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eProtein-protein interaction Network (PPI Network) of key differential genes (KDGs) calculated by A. TRING database. B-f. Protein-protein interaction Network (PPI Network) of Top10 key differential genes (KDGs) calculated by 5 algorithms of cytoHubba plug-in, including MCC (B), MNC (C), Degree (D), EPC (E), Closeness (F). G. Venn diagram of key differential genes (KDGs) of TOP10 by 5 algorithms of CytoHubba plugin.\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-5765774/v1/48ff74de8358b68fcb8c65cb.png"},{"id":73416729,"identity":"35e4aa45-57c1-47ac-8b84-0b17c5e05e06","added_by":"auto","created_at":"2025-01-09 17:20:42","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":911878,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDifferential Expression Validation and ROC Curve Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA. Group comparison plots of hub genes in osteoarthritis (OA) samples and Control samples from the Combined GEO Datasets. B-d ROC curves of hub genes (hub genes) \u003cem\u003eCDK1\u003c/em\u003e, \u003cem\u003eCDK2\u003c/em\u003eand \u003cem\u003eRELA\u003c/em\u003e (B), \u003cem\u003eCDKN2A\u003c/em\u003e, \u003cem\u003ePTEN\u003c/em\u003eand \u003cem\u003eHSP90AA1\u003c/em\u003e (C), \u003cem\u003eMYC\u003c/em\u003e, \u003cem\u003eCDKN1A\u003c/em\u003eand \u003cem\u003eEGFR\u003c/em\u003e (D) in the integrated GEO Datasets (Combined Datasets). ** stands for p value \u0026lt; 0.01, highly statistically significant; *** represents p value \u0026lt; 0.001 and highly statistically significant. When AUC \u0026gt; 0.5, it indicates that the expression of the molecule is a trend to promote the occurrence of the event, and the closer the AUC is to 1, the better the diagnostic effect. AUC between 0.7 and 0.9 had a certain accuracy, and AUC above 0.9 had a high accuracy. Pink represents Control (Control) samples and blue represents osteoarthritis (OA) samples.\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-5765774/v1/0c0023ab573504e2dc2c1549.png"},{"id":73416711,"identity":"b0ee928c-8e05-4848-865a-5d2f6001138e","added_by":"auto","created_at":"2025-01-09 17:20:41","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":156765,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCorRELAtion and Friends Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA. CorRELAtion and chord diagram of hub genes in Combined GEO Datasets. B. Box plot of the results of functional similarity (Friends) analysis of hub genes. The absolute value of corRELAtion coefficient (r value) below 0.3 was weak or no corRELAtion, between 0.3 and 0.5 was weak corRELAtion, between 0.5 and 0.8 was moderate corRELAtion, and above 0.8 was strong corRELAtion. The light blue is negative corRELAtion and the brown is positive corRELAtion.\u003c/p\u003e","description":"","filename":"floatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-5765774/v1/386b5dc0919341ebd0efc2bf.png"},{"id":73417438,"identity":"129b5bdc-b210-4992-8e48-8d1e8ba19bde","added_by":"auto","created_at":"2025-01-09 17:28:41","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":333682,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCombined Datasets Immune Infiltration Analysis by CIBERSORT Algorithm\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA-B. Bar plot of the proportion of immune cells in the Combined GEO Datasets (A) and group comparison plot (B). C. CorRELAtion heatmap of immune cell infiltration abundance in the integrated GEO Datasets (Combined Datasets). D. Bubble plot of the corRELAtion between hub genes and immune cell infiltration abundance in the integrated GEO Datasets (Combined Datasets). OA, Osteoarthritis. ns stands for p value ≥ 0.05, not statistically significant; * represents p value \u0026lt; 0.05, statistically significant; ** represents p value \u0026lt; 0.01, highly statistically significant; *** represents p value \u0026lt; 0.001 and highly statistically significant. The absolute value of corRELAtion coefficient (r value) below 0.3 was weak or no corRELAtion, between 0.3 and 0.5 was weak corRELAtion, and between 0.5 and 0.8 was moderate corRELAtion. Osteoarthritis (OA) samples are blue, and Control samples are pink. Light blue is negative corRELAtion, brown is positive corRELAtion, and the depth of the color represents the strength of the corRELAtion.\u003c/p\u003e","description":"","filename":"floatimage9.png","url":"https://assets-eu.researchsquare.com/files/rs-5765774/v1/d91c9e9cf8abb9a52db20e66.png"},{"id":74700640,"identity":"725de897-9c6c-4b7f-8af1-d1352d81a5bd","added_by":"auto","created_at":"2025-01-24 23:16:24","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4788642,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5765774/v1/13325d1e-27dc-4a97-9881-994781c9235d.pdf"},{"id":73416709,"identity":"d6d9ea41-17f6-4309-ba67-9651fce74688","added_by":"auto","created_at":"2025-01-09 17:20:41","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":40050,"visible":true,"origin":"","legend":"","description":"","filename":"TableS1RBIRGs.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-5765774/v1/6722e90f484fcde892514c47.xlsx"},{"id":73416715,"identity":"30419e42-0320-4433-8730-5fbea53dc272","added_by":"auto","created_at":"2025-01-09 17:20:41","extension":"csv","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":7790,"visible":true,"origin":"","legend":"","description":"","filename":"TableS2OARGs.csv","url":"https://assets-eu.researchsquare.com/files/rs-5765774/v1/7d9a7c962a9aef07f2fd6c19.csv"},{"id":73416708,"identity":"295ef587-bb3c-42aa-935a-6248e1c4778d","added_by":"auto","created_at":"2025-01-09 17:20:41","extension":"xlsx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":13199,"visible":true,"origin":"","legend":"","description":"","filename":"TableS3TCMnodes.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-5765774/v1/bd706d578a0f84b9d3b668d0.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Study on the Molecular Mechanism amd Immune Cell Infiltration of Bushenhuoluo Decoction in Osteoarthritis Treatment Based on Network Pharmacology","fulltext":[{"header":"Introduction","content":"\u003cp\u003eOA is a widespread and incapacitating disorder marked by the deterioration of articular cartilage and the adjacent bone, primarily impacting the knees, hips, and hands. This condition represents the most prevalent type of arthritis, influencing millions globally. In the United States, it is estimated that around 32.5 million adults are impacted by OA[\u003csup\u003e1]\u003c/sup\u003e. The condition notably affects patients\u0026apos; quality of life by inducing pain, stiffness, and limitations in mobility, positioning it as a predominant factor contributing to disability in older adults[\u003csup\u003e2]\u003c/sup\u003e. Existing therapeutic strategies for OA predominantly emphasize alleviating symptoms rather than altering the disease\u0026apos;s progression. These strategies encompass non-pharmacological methods, including physical therapy and weight management, alongside pharmacological options such as NSAIDs, and surgical procedures, such as joint replacement, reserved for advanced cases[\u003csup\u003e3]\u003c/sup\u003e. Long-term use of NSAIDs could lead to gastrointestinal, cardiovascular, and renal complications. Consequently, there is a growing interest in alternative therapies, including Traditional Chinese Medicine (TCM) , which has been used for centuries to treat various ailments, including OA .\u003c/p\u003e\n\u003cp\u003eSeveral studies have investigated the efficacy of TCM in the management of OA. For example, a systematic review and meta-analysis reported that TCM formulations, including herbs such as Du Huo (Angelica pubescens), Mu Gua (Chaenomeles speciosa), and Chuan Niu Xi (Cyathula officinalis), showed promising results in reducing pain and improving function in OA patients. Another study highlighted the anti-inflammatory and chondroprotective effects of TCM herbs, suggesting potential mechanisms by which these treatments might alleviate OA symptoms . Notwithstanding these encouraging results, the molecular pathways that elucidate the effects of TCM on OA remain substantially uninvestigated. TCM has been employed for the management of knee osteoarthritis for an extended period. Originating from its local and systemic applications within the realm of knee joint therapy, TCM has demonstrated efficacy in alleviating pain, enhancing joint functionality, and safeguarding joint integrity. The utilization of traditional Chinese medicine has undergone rigorous validation through extensive clinical practice. Nonetheless, the intricate \u0026quot;multi-component\u0026quot; and \u0026quot;multi-functional\u0026quot; nature of TCM has resulted in its specific mechanisms of action not being universally acknowledged. Network pharmacology offers a means to elucidate the intricate interconnections among drugs, targets, and diseases, employing high-throughput screenings, network visualizations, and analyses of network topology to accurately predict and evaluate the mechanisms through which TCM compounds exert their effects. This approach is characterized by its \u0026quot;multi-components, multi-targets, and multi-pathways\u0026quot; framework, thereby streamlining the understanding of complex substances. We propose that the therapeutic effects of the Huoluo prescription on knee osteoarthritis are mediated by its inherent characteristics of \u0026quot;multi-components, multi-targets, and multi-pathways.\u0026quot; BSHLD is an empirical formula for treating osteoarthritis with the syndrome of \u0026quot;kidney deficiency and meridian obstruction\u0026quot;. This formula has achieved good results in treating knee osteoarthritis. However, the specific molecular biological mechanisms are not yet fully understood and require further research.\u003c/p\u003e\n\u003cp\u003eNetwork pharmacology represents a burgeoning discipline that merges systems biology with bioinformatics to elucidate the intricate interactions between pharmaceuticals and biological systems. Through the construction of networks that illustrate drug-target interactions, this methodology offers the potential to pinpoint prospective therapeutic targets and clarify the mechanisms underlying the actions of multi-component drugs, particularly those utilized in TCM . The main aim of this study was to clarify the molecular mechanisms through which TCM herbs exert their therapeutic effects on OA. By employing network pharmacology alongside bioinformatics methodologies, we sought to identify critical target genes and pathways associated with OA, thereby providing a scientific foundation for the application of TCM in the treatment of this condition. This investigation holds promise for revealing novel therapeutic targets and facilitating the creation of more effective and safer therapeutic options for OA.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003e\u003cstrong\u003e2.1 Data Download\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe R package GEOquery[\u003csup\u003e4]\u003c/sup\u003e (Version 2.70.0) downloaded datasets GSE55235 and GSE55457[\u003csup\u003e5]\u003c/sup\u003e from the GEO database[\u003csup\u003e6]\u003c/sup\u003e for osteoarthritis research, both from Homo sapiens with synovial tissue on GPL96, including 10 OA and 10 Control samples each.\u0026nbsp;The specific information is shown in Table 1.\u0026nbsp;The GeneCards[\u003csup\u003e7]\u003c/sup\u003e and MSigDB[\u003csup\u003e8]\u003c/sup\u003e databases identified 2262 and 319 ribosome generation-related genes (RBIRGs), respectively, yielding a total of 2306 unique RBIRGs after merging and removing duplicates, as detailed in Table\u0026nbsp;S1.\u003c/p\u003e\n\u003cp\u003eThe R package sva[\u003csup\u003e9]\u003c/sup\u003e (3.50.0) debatched Datasets GSE55235 and GSE55457 into Combined GEO datasets with 20 OA and 20 Control samples, which were then standardized using limma[\u003csup\u003e10]\u003c/sup\u003e (3.58.1) and normalized. PCA was performed on expression matrices pre- and post-batch effect removal to verify the effectiveness of the debatching[\u003csup\u003e11]\u003c/sup\u003e, allowing for dimensionality reduction and visualization in 2D or 3D graphs.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2 Osteoarthria-target gene acquisition and screening\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOsteoarthritis target genes were collected from the OMIM[\u003csup\u003e12]\u003c/sup\u003e and CTD[\u003csup\u003e13]\u003c/sup\u003e databases using \u0026quot;Osteoarthritis\u0026quot; as a keyword, yielding 30 genes from OMIM and 1116 from CTD with an Inference Score \u0026gt;19, resulting in 1132 osteoarthritis target-related genes (OARGs) after de-duplication, detailed in Table S2.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.3 Acquisition and screening of traditional Chinese medicine components and targets\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDuhuo, Mugau, Chuanniuxi, Yiyiren, Xixin, Baishao, and She were downloaded from TCMSP[\u003csup\u003e14]\u003c/sup\u003e, along with njincao, Duzhong, Guizhi, Weilingxian, and Fangfeng\u0026apos;s composition data. Components with Oral Bioavailability (OB) \u0026gt; 40% and drug-likeness (DL) \u0026gt; 0.25 were screened, and their targets predicted via TCMSP[\u003csup\u003e15]\u003c/sup\u003e. UniProtKB[\u003csup\u003e16]\u003c/sup\u003e converted target names to gene names, and Cytoscape[\u003csup\u003e17]\u003c/sup\u003e created the traditional Chinese medicine-ingredient-target network diagram, detailed in Table S3.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.4 Key differential genes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSamples were categorized into OA and Control, with differential gene analysis using the limma R package to identify DEGs based on |logFC| \u0026gt; 0 and adj.p \u0026lt; 0.05, visualized with ggplot2. KDGs from OARGs, TCMSRGs, RBIRGs, and DEGs were identified via Venn diagrams, and heatmaps were created with pheatmap.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.5 Gene ontology (GO) and pathway (KEGG) enrichment analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe used the R package clusterProfiler[\u003csup\u003e18]\u003c/sup\u003e (version 4.10.0) to perform GO[\u003csup\u003e19]\u003c/sup\u003e and KEGG enrichment analysis[\u003csup\u003e20]\u003c/sup\u003e on key differential genes. We set the criteria of adj.p \u0026lt; 0.05 and FDR \u0026lt; 0.25, applying the Benjamini-Hochberg correction method.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.6 Protein interaction (PPI) network and Hub gene screening\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe STRING database[\u003csup\u003e21]\u003c/sup\u003e was utilized to build a PPI network using KDGs with high confidence scores (\u0026ge;0.700). The genes that interact within the PPI network were analyzed using five algorithms from the CytoHubba[\u003csup\u003e22]\u003c/sup\u003e plug-in in Cytoscape[\u003csup\u003e23]\u003c/sup\u003e: MCC, MNC, Degree, EPC, and Closeness[\u003csup\u003e24]\u003c/sup\u003e.After calculating the KDG scores, the top 10 KDGs were identified for further analysis. A Venn diagram was used to analyze the overlap of genes identified by the five algorithms, helping to pinpoint hub genes among the key differential genes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.7 Validation of differential expression of Hub genes and ROC curve analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo investigate the differences in hub gene expression between OA and control samples, a comparative analysis was conducted. The R package pROC[\u003csup\u003e25]\u003c/sup\u003e was utilized to plot the ROC curve. Additionally, it was used to calculate the AUC value, which indicates diagnostic effectiveness. The AUC values range from 0.5 to 1, with higher values indicating superior diagnostic performance. AUC values between 0.5 and 0.7 reflect low accuracy, those from 0.7 to 0.9 indicate moderate accuracy, and values exceeding 0.9 signify high accuracy.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.8\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eHub gene cor\u003c/strong\u003e\u003cstrong\u003eRELA\u003c/strong\u003e\u003cstrong\u003etion analysis and Friends analysis were performed\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Spearman algorithm analyzed hub gene expression corRELAtions in Combined GEO Datasets, with results visualized through corRELAtion and chord diagrams using R packages igraph and ggraph. CorRELAtion coefficients indicated weak (r \u0026lt; 0.3), moderate (0.5-0.8), or strong (r \u0026gt; 0.8) corRELAtions. GO semantic comparison quantitatively assesses gene similarity, aiding bioinformatics analyses, while functional similarity of hub genes was analyzed using GOSemSim[\u003csup\u003e26]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.9 Immune Infiltration analysis of Osteoarthritis (CIBERSORT)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCIBERSORT[\u003csup\u003e27]\u003c/sup\u003e utilizes linear support vector regression to analyze transcriptome data for immune cell composition in mixed samples. It applies the LM22 gene matrix and filters for positive immune cell scores to create both an infiltration matrix and a proportion bar chart. The R package ggplot2 (Version 3.4.4) is used to show differences in immune cell expression between OA and Control samples in the combined GEO Datasets, helping to identify significant immune cells for further analysis. Spearman corRELAtion calculates RELAtionships among immune cells, which are visualized with the pheatmap package (Version 1.0.12). Additionally, corRELAtions between hub genes and immune cells are analyzed, retaining results with p \u0026lt; 0.05, and displayed using ggplot2.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.10 Statistical analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data analysis was conducted using R software (Version 4.2.2). Continuous variable comparisons used Student\u0026apos;s T-Test for normal distributions and Mann-Whitney U Test for non-normal distributions. Kruskal-Wallis test compared three or more groups, while Spearman corRELAtion analyzed molecule corRELAtions. P values were two-sided, with significance at p \u0026lt; 0.05.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003e3.1 Technology Roadmap\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e(Fig1)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2 Merging of osteoarthritis datasets\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe R package sva removed batch effects from OA datasets GSE55235 and GSE55457, creating Combined GEO datasets. Boxplots (Fig2A-B) compared expression values pre- and post-removal, while PCA plots (Fig2C-D) assessed low-dimensional feature distribution. Results indicated effective elimination of batch effects in the OA dataset.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.3 Target prediction of traditional Chinese medicine components\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe TCMSP database provided chemical components of 11 traditional Chinese medicines, screening 53 active molecules with OB \u0026gt; 40% and drug-likeness \u0026gt; 0.25. The detailed information is shown in Table 2. Protein targets for these components were predicted, yielding 224 targets converted to gene names via UniProtKB. The detailed information is shown in Table S3. Cytoscape software illustrated the TCM-component-target network (Fig3) with 11 components, 53 active molecules, and 224 targets.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.4 Key differentially expressed genes\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eRELA\u003c/strong\u003e\u003cstrong\u003eted to osteoarthritis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Combined GEO Datasets were categorized into osteoarthritis and control samples, leading to the identification of 2,759 DEGs through the application of the R package limma, with criteria set at |logFC| \u0026gt; 0 and adjusted p-value \u0026lt; 0.05. This cohort comprised 1,428 genes exhibiting up-regulation and 1,331 genes displaying down-regulation, as depicted in a volcano plot (Fig 4A). To derive Key Differentially Expressed Genes (KDGs), DEGs meeting the aforementioned criteria, in conjunction with Osteoarthritis-Associated Regulatory Genes (OARGs), Traditional Chinese Medicine Specific Regulatory Genes (TCMSPRGs), and Reinforced Bioinformatics Interaction Regulatory Genes (RBIRGs), were subjected to Venn diagram analysis, culminating in the identification of 16 KDGs: \u003cem\u003eCDKN2A\u003c/em\u003e, \u003cem\u003eCDK1\u003c/em\u003e, \u003cem\u003eMMP9\u003c/em\u003e, \u003cem\u003eNR3C1\u003c/em\u003e, \u003cem\u003eMYC\u003c/em\u003e, \u003cem\u003eCDKN1A\u003c/em\u003e, \u003cem\u003eVEGFA\u003c/em\u003e, \u003cem\u003eHSP90AA1\u003c/em\u003e, \u003cem\u003eFASN\u003c/em\u003e, \u003cem\u003eEGFR\u003c/em\u003e, \u003cem\u003eAPOB\u003c/em\u003e, \u003cem\u003ePTEN\u003c/em\u003e, \u003cem\u003eCDK2\u003c/em\u003e, \u003cem\u003eHSPA5\u003c/em\u003e, \u003cem\u003eIL10\u003c/em\u003e, and \u003cem\u003eRELA\u003c/em\u003e. Subsequently, the expression variations of these KDGs across different sample groups within the Combined GEO Datasets were assessed, and a heatmap was generated utilizing the R package pheatmap.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.5 Gene ontology (GO) and pathway (KEGG) enrichment analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe GO and KEGG enrichment analyses conducted on 16 pivotal differential genes highlighted their relevance to muscle cell proliferation, glandular development, and a spectrum of biological processes, cellular components, and pathways pertinent to osteoarthritis, as elaborated in Table 3 and illustrated through bubble plots (Fig. 5A). A network diagram was generated from the GO and KEGG enrichment analyses, effectively depicting biological processes, cellular components, molecular functions, and pathways, with nodes of larger size representing a greater number of associated molecules and annotations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.6 Construction of protein interaction (PPI) network and regulatory network\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA protein-protein interaction analysis using the STRING database identified a PPI network consisting of 16 KDGs, which revealed 13 RELAted KDGs (Fig6A). The scores for these genes were calculated using five CytoHubba algorithms: MCC (Fig6B), MNC (Fig6C), Degree (Fig6D), EPC (Fig6E), and Closeness (Fig6F), which were then used to rank the top 10 KDGs for network visualization. The colors of the circles represent the scores, with red indicating high scores and yellow indicating low scores. A Venn diagram (Fig6G) illustrated the intersection of genes identified by the algorithms, highlighting nine hub genes: \u003cem\u003eMYC\u003c/em\u003e, \u003cem\u003eCDKN1A\u003c/em\u003e, \u003cem\u003eEGFR\u003c/em\u003e, \u003cem\u003eCDKN2A\u003c/em\u003e, \u003cem\u003ePTEN\u003c/em\u003e, \u003cem\u003eHSP90AA1\u003c/em\u003e, \u003cem\u003eCDK1\u003c/em\u003e, \u003cem\u003eCDK2\u003c/em\u003e, and \u003cem\u003eRELA\u003c/em\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.7 Differential expression verification and ROC curve analysis of Hub genes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe group comparison figure (Fig 7A) illustrates the differential expression of 9 hub genes between OA samples and control samples in the combined GEO datasets. The analysis indicated that the expression levels of 6 hub genes were highly statistically significant (p \u0026lt; 0.001) between OA and control samples (Fig 7A). These genes include \u003cem\u003eCDK2\u003c/em\u003e, \u003cem\u003eCDKN1A\u003c/em\u003e, \u003cem\u003eCDKN2A\u003c/em\u003e, \u003cem\u003eEGFR\u003c/em\u003e, \u003cem\u003eHSP90AA1\u003c/em\u003e, and \u003cem\u003eMYC\u003c/em\u003e. The expression levels of three additional hub genes\u0026mdash;\u003cem\u003eCDK1\u003c/em\u003e, \u003cem\u003ePTEN\u003c/em\u003e, and \u003cem\u003eRELA\u003c/em\u003e\u0026mdash;were also highly statistically significant (p \u0026lt; 0.001) in OA compared to control samples. Finally, ROC curves were generated using the R package pROC to assess the expression of hub genes in the combined GEO datasets. The ROC curve (Fig7B-D) showed that the expression levels of hub genes (\u003cem\u003eHSP90AA1\u003c/em\u003e, \u003cem\u003eMYC\u003c/em\u003e and \u003cem\u003eCDKN1A\u003c/em\u003e) had high accuracy (AUC \u0026gt; 0.9) in the classification of OA samples and Control samples; The expression levels of \u003cem\u003eCDK1\u003c/em\u003e, \u003cem\u003eCDK2\u003c/em\u003e, \u003cem\u003eCDKN2A\u003c/em\u003e, \u003cem\u003eEGFR\u003c/em\u003e, \u003cem\u003ePTEN\u003c/em\u003e and \u003cem\u003eRELA\u003c/em\u003e showed certain accuracy (0.7 \u0026lt; AUC \u0026lt; 0.9) in the classification of OA samples and Control samples.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.8 Cor\u003c/strong\u003e\u003cstrong\u003eRELA\u003c/strong\u003e\u003cstrong\u003etion analysis and Friends analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe calculated the corRELAtions among the nine hub genes in the Combined GEO Datasets and presented the results using corRELAtion and chord diagrams (Fig 8A).The results indicated that the hub genes were predominantly positively corRELAted with one another. Finally, we used the scores from the functional similarity analysis to identify genes that play significant roles in the biological processes of Osteoarthritis (Fig 8B). The results showed that \u003cem\u003eRELA\u003c/em\u003e plays a crucial role in Osteoarthritis, being the gene with the highest score approaching the critical cut-off value of 0.65.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.9 Immune Infiltration Analysis of Osteoarthritis (CIBERSORT)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe CIBERSORT algorithm assessed the abundance of infiltration for 22 immune cells in the Combined GEO Datasets. This analysis produced a bar chart of immune cell proportions (Fig9A) and group comparison plots (Fig9B), which illustrate the differences in immune cell infiltration between OA samples and control samples. The results showed significant expression levels (p \u0026lt; 0.05) for four immune cells in OA and control samples: eosinophils, M0 macrophages, plasma cells, and Tregs. One immune cell that demonstrated highly statistically significant expression (p \u0026lt; 0.01) in both OA and control samples was activated NK cells. Three immune cells exhibited highly significant expression (p \u0026lt; 0.001): activated mast cells, activated NK cells, and resting CD4 memory T cells. The corRELAtion heat map (Fig9C) illustrated the infiltration abundance of eight immune cells. It showed a strong positive corRELAtion between activated mast cells and resting CD4 memory T cells (r = 0.63), and a strong negative corRELAtion between resting and activated mast cells (r = -0.74). The corRELAtion bubble plots indicated a significant positive corRELAtion between the hub gene\u0026nbsp;\u003cem\u003eMYC\u003c/em\u003e and activated mast cells (r \u0026gt; 0.0, p \u0026lt; 0.05), while\u0026nbsp;\u003cem\u003eCDKN1A\u003c/em\u003e showed a significant negative corRELAtion with resting mast cells (r \u0026lt; 0.0, p \u0026lt; 0.05)(Fig9D).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eOA is a common degenerative joint disorder that profoundly affects the quality of life and daily activities of those diagnosed with it. This condition is marked by the degradation of joint cartilage and the bone beneath it, resulting in symptoms such as pain, stiffness, and reduced mobility. The impact of OA is considerable, especially among the elderly, as it ranks among the foremost causes of disability on a global scale. Furthermore, OA is acknowledged as a critical public health concern, with its incidence rising at a concerning rate in recent decades. Recent studies indicate that approximately 250 million individuals worldwide are affected by knee OA, with a notable rise in incidence attributed to aging populations and increasing obesity rates[\u003csup\u003e28]\u003c/sup\u003e. Moreover, it is anticipated that the worldwide prevalence of knee OA will increase, especially in low- and middle-income nations where shifts in lifestyle and urban development lead to an elevated risk of factors such as reduced physical activity and alterations in diet[\u003csup\u003e29]\u003c/sup\u003e. The existing treatment strategies predominantly emphasize analgesic medications, which, although successful in relieving discomfort, can lead to negative side effects, including gastrointestinal and cardiovascular issues stemming from prolonged usage. Therefore, there is an urgent necessity to investigate alternative treatment strategies that reduce adverse effects while improving therapeutic effectiveness.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis study aims to clarify the molecular mechanisms of OA by combining bioinformatics methods with TCM analysis. We systematically downloaded and processed OA-related datasets from the GEO database, including GSE55235 and GSE55457, which contained synovial tissue samples from OA patients and healthy controls. After normalizing the data and removing batch effects, we identified a comprehensive set of ribosome biogenesis-related and osteoarthritis-related genes from various databases, including GeneCards and CTD. Additionally, we examined the active components of different TCM herbs and their targets, creating a network of herbal components and their interactions with OA-related genes. \u0026nbsp;Identifying KDGs and hub genes through PPI networks, along with analyses such as GO and KEGG pathway enrichment, offered insights into the biological processes and pathways involved in OA. This multifaceted approach not only highlights the potential therapeutic targets within TCM for OA but also sets the stage for a deeper discussion on the implications of our findings in the context of OA pathogenesis and treatment strategies.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe differential expression analysis conducted on the integrated GEO datasets revealed a total of 2,759 DEGs with a significance threshold of |logFC| \u0026gt; 0 and adj.p \u0026lt; 0.05. Among these, 1,428 genes were upregulated, while 1,331 were downregulated. This substantial number of DEGs underscores the complexity of osteoarthritis pathology and highlights the potential for identifying novel therapeutic targets. The intersection of these DEGs with OARGs, TCMSRGs, and RBIRGs yielded 16 KDGs. The identification of these KDGs is critical, as they may play pivotal roles in the molecular mechanisms underlying osteoarthritis and could serve as biomarkers for disease progression or therapeutic response.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe enrichment analysis revealed that the KGDs were predominantly associated with biological processes including the proliferation of muscle cells and the regulation of DNA metabolic processes, in addition to pathways such as the PI3K-Akt signaling pathway. This particular signaling pathway is vital for the survival and proliferation of chondrocytes, which are critical for preserving the structural integrity of cartilage. Activation of this pathway promotes cell survival by inhibiting apoptosis and enhancing cell proliferation. Research has indicated that the heightened expression of Tra2\u0026beta;, a protein known to stimulate the PI3K/Akt signaling pathway, results in a substantial upregulation of extracellular matrix constituents, particularly collagen II. Concurrently, this overexpression leads to a reduction in the levels of inflammatory and apoptotic markers in chondrocytes that are exposed to interleukin-1\u0026beta; (IL-1\u0026beta;) stimulation[\u003csup\u003e30]\u003c/sup\u003e. Furthermore, the utilization of growth factors like insulin-like growth factor-1 (IGF-1) has been linked to a reduction in matrix metalloproteinases (MMPs) and apoptotic indicators, thereby reinforcing the notion that the PI3K/Akt signaling pathway is essential for the survival of chondrocytes in inflammatory environments[\u003csup\u003e31]\u003c/sup\u003e. Overall, the PI3K-Akt pathway is integral to the maintenance of chondrocyte health, particularly in the context of OA, where its dysregulation can lead to increased apoptosis and cartilage degradation.\u003c/p\u003e\n\u003cp\u003eThe analysis of the PPI network revealed nine central hub genes, such as\u0026nbsp;\u003cem\u003eMYC\u003c/em\u003e,\u0026nbsp;\u003cem\u003eCDKN1A\u003c/em\u003e, and\u0026nbsp;\u003cem\u003ePTEN\u003c/em\u003e, that may function as critical regulatory points within the disease context. The hub genes play important roles in maintaining cell homeostasis and responding to external stimuli, and could be potential diagnostic markers or therapeutic targets. Future studies could explore whether there is a synergistic effect among the hub genes in promoting disease progression, and whether existing drugs targeting these hub genes have shown efficacy. The hub genes identified exhibited markedly significant disparities in expression between OA samples and control groups within the integrated GEO dataset. Notably, the expression levels of\u0026nbsp;\u003cem\u003eHSP90AA1\u003c/em\u003e,\u0026nbsp;\u003cem\u003eMYC\u003c/em\u003e, and\u0026nbsp;\u003cem\u003eCDKN1A\u003c/em\u003e demonstrated a high degree of accuracy in distinguishing OA samples from control samples, while the expression levels of\u0026nbsp;\u003cem\u003eCDK1\u003c/em\u003e,\u0026nbsp;\u003cem\u003eCDK2\u003c/em\u003e,\u0026nbsp;\u003cem\u003eCDKN2A\u003c/em\u003e,\u0026nbsp;\u003cem\u003eEGFR\u003c/em\u003e,\u0026nbsp;\u003cem\u003ePTEN\u003c/em\u003e, and\u0026nbsp;\u003cem\u003eRELA\u003c/em\u003e displayed a moderate level of accuracy. The strong statistical significance and classification accuracy of hub genes like HSP90AA1, MYC, and CDKN1A in distinguishing osteoarthritis samples from control samples highlight their potential as biomarkers or therapeutic targets for the disease.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eMYC\u003c/em\u003e is a well-known oncogene that plays a key role in regulating cell growth, differentiation, and death. Its dysregulation is linked to several cancers, including osteoarthritis. In a similar vein,\u0026nbsp;\u003cem\u003eMYC\u003c/em\u003e, recognized as a transcription factor that plays a significant role in cell growth and programmed cell death, has been associated with the advancement of OA. Elevated levels of\u0026nbsp;\u003cem\u003eMYC\u003c/em\u003e in inflammatory conditions suggest its involvement in chondrocyte hypertrophy and apoptosis, marking it as a potential biomarker for disease severity[\u003csup\u003e32]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eHSP90AA1\u003c/em\u003e, a critical chaperone protein, has been shown to be down-regulated in the blood and cartilage of OA patients, indicating its potential role in the disease\u0026apos;s pathogenesis. Specifically, research demonstrates that\u0026nbsp;\u003cem\u003eHSP90AA1\u003c/em\u003e deficiency corRELAtes with increased inflammation, oxidative stress, and chondrocyte apoptosis, suggesting that it is vital for maintaining chondrocyte homeostasis and could serve as a therapeutic target for OA[\u003csup\u003e33]\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cem\u003eCDKN1A\u003c/em\u003e, also known as p21, is another crucial player that regulates the cell cycle and has been linked to the senescence of chondrocytes in OA. The expression levels of this biomarker exhibit substantial variations in OA tissues, suggesting its promise as both a diagnostic indicator and a therapeutic target[\u003csup\u003e34]\u003c/sup\u003e. Collectively, these molecular markers not only enhance our understanding of the pathophysiological processes underlying OA but also pave the way for the development of targeted therapeutic strategies that focus on the regulation of their expression and functional activity.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eRELA\u003c/em\u003e plays a crucial role in Osteoarthritis, being the gene with the highest score approaching the critical cut-off value of 0.65.In the context of OA,\u0026nbsp;\u003cem\u003ePTEN\u003c/em\u003e and\u0026nbsp;\u003cem\u003eRELA\u003c/em\u003e have emerged as significant molecular players in the disease\u0026apos;s pathophysiology.\u0026nbsp;\u003cem\u003ePTEN\u003c/em\u003e is known to regulate cell survival and apoptosis, with its dysregulation contributing to chondrocyte death and cartilage degradation in OA. Studies have shown that downregulation of miR-29a-3p can lead to increased\u0026nbsp;\u003cem\u003ePTEN\u003c/em\u003e expression, promoting chondrocyte apoptosis and exacerbating OA progression[\u003csup\u003e35]\u003c/sup\u003e. Conversely,\u0026nbsp;\u003cem\u003eRELA\u003c/em\u003e plays a significant role in the inflammatory response linked to OA. The activation of the NF-\u0026kappa;B signaling pathway, in which\u0026nbsp;\u003cem\u003eRELA\u003c/em\u003e is pivotal, results in the upregulation of several pro-inflammatory cytokines and matrix metalloproteinases. These factors contribute to the degradation of cartilage. Understanding the interplay between\u0026nbsp;\u003cem\u003ePTEN\u003c/em\u003e and\u0026nbsp;\u003cem\u003eRELA\u003c/em\u003e in OA could provide insights into novel therapeutic targets aimed at modulating inflammation and promoting cartilage repair. Overall, ongoing research into these molecular pathways holds promise for developing more effective treatments for knee osteoarthritis.\u003c/p\u003e\n\u003cp\u003eThe analysis of immune infiltration indicated notable alterations in the prevalence of eight distinct immune cell types, with activated mast cells and resting CD4 memory T cells exhibiting the strongest positive corRELAtion. Mast cells (MCs) have been identified as crucial contributors to the pathophysiology of OA, playing a vital role in the inflammatory environment that typifies this degenerative joint condition. Research has demonstrated that MCs are prevalent in the synovial tissue of OA patients, where they interact with various immune cells and contribute to synovial inflammation. A study employing CIBERSORT for gene expression analysis revealed that the proportion of resting mast cells is significantly altered in OA compared to healthy joint tissues, highlighting their potential role in disease progression[\u003csup\u003e36]\u003c/sup\u003e. Moreover, mast cells (MCs) are associated with the breakdown of the extracellular matrix, predominantly via the secretion of proteolytic enzymes, including tryptase. This enzyme has the capability to cleave proteoglycan-4 (PRG4), an essential component for the lubrication of joints[\u003csup\u003e37]\u003c/sup\u003e. This cleavage not only reduces the lubricating properties of PRG4 but also activates inflammatory pathways, further exacerbating joint damage. The interaction of MCs with other immune cells, such as macrophages, suggests a complex network of signaling that promotes chronic inflammation and tissue remodeling in OA [\u003csup\u003e38]\u003c/sup\u003e. Thus, the presence and activation of mast cells in the OA-affected joint may serve as a catalyst for the disease\u0026apos;s inflammatory processes, making them a potential target for therapeutic interventions.These findings suggest that immune microenvironment alterations might contribute to the pathological process of osteoarthritis, providing a basis for immune therapy. Further studies are needed to understand how different immune cell types affect the OA process and whether modulating immune infiltration could improve symptoms.\u003c/p\u003e\n\u003cp\u003eThis study uses several strong methodologies that improve the robustness and reliability of its findings. First, integrating multiple datasets from the GEO database, specifically GSE55235 and GSE55457, enables a comprehensive analysis of OA. This larger sample size enhances the statistical power of the differential expression analysis. The use of advanced bioinformatics tools, such as the R packages limma and sva, for normalization and batch effect removal helps to process the data rigorously, minimizing potential biases that could affect the results. Additionally, the systematic approach to identifying key differential genes involves intersecting various gene sets, including those related to osteoarthritis, ribosome biogenesis, and traditional Chinese medicine targets. This method offers a multifaceted perspective on the molecular mechanisms underlying OA. The construction of a PPI network using STRING and the subsequent identification of hub genes through multiple algorithms in Cytoscape further strengthens the biological relevance of the identified targets. Finally, validating hub gene expression through ROC curve analysis confirms their diagnostic potential and highlights the findings\u0026apos; relevance in clinical settings. Collectively, these methodological rigor and comprehensive analyses contribute significantly to the understanding of osteoarthritis and the potential therapeutic implications of TCM. This study has several limitations that should be noted. First, relying on publicly available datasets like GEO may introduce biases. These biases can arise from variations in sample collection, processing, and annotation in different studies. Although we employed rigorous filtering criteria to select relevant datasets, the inherent heterogeneity of the samples could affect the generalizability of our findings. Second, integrating multiple databases for gene and protein interaction analysis strengthens our results. However, it also raises concerns about the accuracy and completeness of the data. The potential for missing or incorrectly labeled genes in the databases could lead to incomplete networks and misinterpretation of their biological significance. Furthermore, the computational methods used for differential expression analysis and hub gene identification, while widely accepted, are subject to limitations in sensitivity and specificity, which may impact the identification of truly significant genes. Finally, this study focuses mainly on bioinformatics analyses. The absence of experimental validation for the identified hub genes and their interactions, either in vitro or in vivo, limits our ability to draw firm conclusions about their roles in osteoarthritis pathology. Future studies should aim to address these limitations by incorporating experimental approaches to validate the computational predictions and enhance the understanding of the underlying biological mechanisms.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, our study elucidates the intricate molecular mechanisms underlying the therapeutic effects of the BSHLD in OA through a comprehensive network pharmacology approach. The identification of KDEGs and their subsequent analysis revealed significant biological processes and pathways that are potentially modulated by BSHLD, particularly those RELAted to inflammation and cartilage metabolism. Notably, the hub genes\u0026nbsp;\u003cem\u003eMYC\u003c/em\u003e,\u0026nbsp;\u003cem\u003eCDKN1A\u003c/em\u003e, and\u0026nbsp;\u003cem\u003eHSP90AA1\u003c/em\u003e demonstrated high accuracy in classifying OA samples from controls, as evidenced by their AUC values exceeding 0.9. Furthermore, the corRELAtion analysis revealed significant RELAtionships among the hub genes and their association with immune cell infiltration, suggesting a complex interplay between immune responses and OA pathology. These findings offer promising molecular targets for osteoarthritis treatment. Looking forward, integrating these results with clinical studies and wet lab experiments could enhance the understanding and therapeutic strategies for osteoarthritis, paving the way for more effective interventions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1\u003c/strong\u003e \u003cstrong\u003eGEO Microarray Chip Information\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"97%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 33px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 33px;\"\u003e\n \u003cp\u003eGSE55235\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 33px;\"\u003e\n \u003cp\u003eGSE55457\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 33px;\"\u003e\n \u003cp\u003ePlatform\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 33px;\"\u003e\n \u003cp\u003eGPL96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 33px;\"\u003e\n \u003cp\u003eGPL96\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 33px;\"\u003e\n \u003cp\u003eSpecies\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 33px;\"\u003e\n \u003cp\u003eHomo sapiens\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 33px;\"\u003e\n \u003cp\u003eHomo sapiens\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 33px;\"\u003e\n \u003cp\u003eTissue\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 33px;\"\u003e\n \u003cp\u003eOsteoarthritis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 33px;\"\u003e\n \u003cp\u003eOsteoarthritis\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 33px;\"\u003e\n \u003cp\u003eSamples in Osteoarthritis group\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 33px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 33px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 33px;\"\u003e\n \u003cp\u003eSamples in Control group\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 33px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 33px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 33px;\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 33px;\"\u003e\n \u003cp\u003ePMID: 24690414\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 33px;\"\u003e\n \u003cp\u003ePMID: 24690414\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;GEO, Gene Expression Omnibus.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eTable 2 ADME Parameters of Compounds\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"90%\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 153px;\"\u003e\n \u003cp\u003eMOL_ID\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 181px;\"\u003e\n \u003cp\u003emolecule_name\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003eob\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003edl\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 153px;\"\u003e\n \u003cp\u003eMOL001910\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 181px;\"\u003e\n \u003cp\u003e11 alpha, 12 alpha - epoxy - 3 beta - 30 - norolean - 20-23 - dihydroxy - en - 28, 12 beta - olide\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e64.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e0.38\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 153px;\"\u003e\n \u003cp\u003eMOL001918\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 181px;\"\u003e\n \u003cp\u003epaeoniflorgenone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e87.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e0.37\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 153px;\"\u003e\n \u003cp\u003eMOL001919\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 181px;\"\u003e\n \u003cp\u003e(9 8 r, 3 s, 5 r, r, s, s) of 14-3 - dihydroxy in 2-4,4,8,10,14 -,3,5,6,7,9 - hexahydro pentamethyl - 2-1 h - cyclopenta [a] phenanthrene - 15 - di one\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e43.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e0.53\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 153px;\"\u003e\n \u003cp\u003eMOL001921\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 181px;\"\u003e\n \u003cp\u003eLactiflorin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e49.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e0.80\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 153px;\"\u003e\n \u003cp\u003eMOL001924\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 181px;\"\u003e\n \u003cp\u003epaeoniflorin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e53.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e0.79\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 153px;\"\u003e\n \u003cp\u003eMOL001925\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 181px;\"\u003e\n \u003cp\u003epaeoniflorin_qt\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e68.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e0.40\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 153px;\"\u003e\n \u003cp\u003eMOL001928\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 181px;\"\u003e\n \u003cp\u003ealbiflorin_qt\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e66.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 153px;\"\u003e\n \u003cp\u003eMOL000211\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 181px;\"\u003e\n \u003cp\u003eMairin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e55.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e0.78\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 153px;\"\u003e\n \u003cp\u003eMOL012286\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 181px;\"\u003e\n \u003cp\u003eBetavulgarin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e68.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e0.39\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 153px;\"\u003e\n \u003cp\u003eMOL000098\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 181px;\"\u003e\n \u003cp\u003equercetin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e46.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 153px;\"\u003e\n \u003cp\u003eMOL003608\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 181px;\"\u003e\n \u003cp\u003eO-Acetylcolumbianetin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e60.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e0.26\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 153px;\"\u003e\n \u003cp\u003eMOL004778\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 181px;\"\u003e\n \u003cp\u003e[(1R,2R)-2,3-dihydroxy-1-(7-methoxy-2-oxochromen-6-yl)-3-methylbutyl] (Z)-2-methylbut-2-enoate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e46.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e0.34\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 153px;\"\u003e\n \u003cp\u003eMOL004792\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 181px;\"\u003e\n \u003cp\u003enodakenin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e57.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e0.69\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 153px;\"\u003e\n \u003cp\u003eMOL002058\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 181px;\"\u003e\n \u003cp\u003e40957-99-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e57.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e0.62\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 153px;\"\u003e\n \u003cp\u003eMOL004782\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 181px;\"\u003e\n \u003cp\u003e[(1R,2R)-2,3-dihydroxy-1-(7-methoxy-2-oxochromen-6-yl)-3-methylbutyl] 3-methylbutanoate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e45.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e0.34\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 153px;\"\u003e\n \u003cp\u003eMOL000211\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 181px;\"\u003e\n \u003cp\u003eMairin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e55.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e0.78\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 153px;\"\u003e\n \u003cp\u003eMOL004367\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 181px;\"\u003e\n \u003cp\u003eolivil\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e62.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e0.41\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 153px;\"\u003e\n \u003cp\u003eMOL000443\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 181px;\"\u003e\n \u003cp\u003eErythraline\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e49.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e0.55\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 153px;\"\u003e\n \u003cp\u003eMOL005922\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 181px;\"\u003e\n \u003cp\u003eAcanthoside B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e43.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e0.77\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 153px;\"\u003e\n \u003cp\u003eMOL006709\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 181px;\"\u003e\n \u003cp\u003eAIDS214634\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e92.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e0.55\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 153px;\"\u003e\n \u003cp\u003eMOL007563\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 181px;\"\u003e\n \u003cp\u003eYangambin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e57.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e0.81\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 153px;\"\u003e\n \u003cp\u003eMOL009009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 181px;\"\u003e\n \u003cp\u003e(+)-medioresinol\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e87.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e0.62\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 153px;\"\u003e\n \u003cp\u003eMOL009015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 181px;\"\u003e\n \u003cp\u003e(-)-Tabernemontanine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e58.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e0.61\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 153px;\"\u003e\n \u003cp\u003eMOL009027\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 181px;\"\u003e\n \u003cp\u003eCyclopamine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e55.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e0.82\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 153px;\"\u003e\n \u003cp\u003eMOL009029\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 181px;\"\u003e\n \u003cp\u003eDehydrodiconiferyl alcohol 4,gamma\u0026apos;-di-O-beta-D-glucopyanoside_qt\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e51.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e0.40\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 153px;\"\u003e\n \u003cp\u003eMOL009031\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 181px;\"\u003e\n \u003cp\u003eCinchonan-9-al, 6\u0026apos;-methoxy-, (9R)-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e68.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e0.40\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 153px;\"\u003e\n \u003cp\u003eMOL009038\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 181px;\"\u003e\n \u003cp\u003eGBGB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e45.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e0.83\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 153px;\"\u003e\n \u003cp\u003eMOL009053\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 181px;\"\u003e\n \u003cp\u003e4 - [(2 s, 3 r) - 5 - [(E) - 3 - hydroxyprop - 1 - enyl] - 7 - methoxy - 3 - methylol - 2, 3 - dihydrobenzofuran - 2 - yl] - 2 - methoxy - phenol\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e50.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e0.39\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 153px;\"\u003e\n \u003cp\u003eMOL009055\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 181px;\"\u003e\n \u003cp\u003ehirsutin_qt\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e49.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e0.37\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 153px;\"\u003e\n \u003cp\u003eMOL009057\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 181px;\"\u003e\n \u003cp\u003eliriodendrin_qt\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e53.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e0.80\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 153px;\"\u003e\n \u003cp\u003eMOL000098\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 181px;\"\u003e\n \u003cp\u003equercetin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e46.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 153px;\"\u003e\n \u003cp\u003eMOL008240\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 181px;\"\u003e\n \u003cp\u003e(E)-3-[4-[(1R,2R)-2-hydroxy-2-(4-hydroxy-3-methoxy-phenyl)-1-methylol-ethoxy]-3-methoxy-phenyl]acrolein\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e56.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e0.36\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 153px;\"\u003e\n \u003cp\u003eMOL000011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 181px;\"\u003e\n \u003cp\u003e(2 r, 3 r) - 3 - (4 - hydroxy - 3 - methoxy - phenyl) - 5-2 - methylol - 2, 3 - methoxy - dihydropyrano [5, 6 - h] [1, 4] benzodioxin - 9 - one\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e68.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e0.66\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 153px;\"\u003e\n \u003cp\u003eMOL011730\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 181px;\"\u003e\n \u003cp\u003e11-hydroxy-sec-o-beta-d-glucosylhamaudol_qt\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e50.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 153px;\"\u003e\n \u003cp\u003eMOL011732\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 181px;\"\u003e\n \u003cp\u003eanomalin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e59.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e0.66\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 153px;\"\u003e\n \u003cp\u003eMOL011737\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 181px;\"\u003e\n \u003cp\u003edivaricatacid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e87.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e0.32\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 153px;\"\u003e\n \u003cp\u003eMOL011749\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 181px;\"\u003e\n \u003cp\u003ephelloptorin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e43.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 153px;\"\u003e\n \u003cp\u003eMOL002644\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 181px;\"\u003e\n \u003cp\u003ePhellopterin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e40.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 153px;\"\u003e\n \u003cp\u003eMOL001736\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 181px;\"\u003e\n \u003cp\u003e(-)-taxifolin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e60.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 153px;\"\u003e\n \u003cp\u003eMOL004576\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 181px;\"\u003e\n \u003cp\u003etaxifolin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e57.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 153px;\"\u003e\n \u003cp\u003eMOL011169\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 181px;\"\u003e\n \u003cp\u003ePeroxyergosterol\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e44.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e0.82\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 153px;\"\u003e\n \u003cp\u003eMOL000211\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 181px;\"\u003e\n \u003cp\u003eMairin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e55.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e0.78\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 153px;\"\u003e\n \u003cp\u003eMOL000098\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 181px;\"\u003e\n \u003cp\u003equercetin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e46.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 153px;\"\u003e\n \u003cp\u003eMOL000449\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 181px;\"\u003e\n \u003cp\u003eStigmasterol\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e43.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e0.76\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 153px;\"\u003e\n \u003cp\u003eMOL005419\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 181px;\"\u003e\n \u003cp\u003eE (6, 8 e, 10 z, z, 12 of 14 e, e, e, z, 20 to 22 z, e, 24, 26 e) - 2,6,10,14,19,23,27,31,6,8,10,12,14,16,18,20,22,24,26 octamethyldotriaconta - 2 ,30-tridecaene\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e45.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e0.51\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 153px;\"\u003e\n \u003cp\u003eMOL000449\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 181px;\"\u003e\n \u003cp\u003eStigmasterol\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e43.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e0.76\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 153px;\"\u003e\n \u003cp\u003eMOL005603\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 181px;\"\u003e\n \u003cp\u003eHeptyl phthalate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e42.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e0.31\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 153px;\"\u003e\n \u003cp\u003eMOL001460\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 181px;\"\u003e\n \u003cp\u003eCryptopin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e78.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e0.72\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 153px;\"\u003e\n \u003cp\u003eMOL001558\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 181px;\"\u003e\n \u003cp\u003esesamin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e56.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e0.83\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 153px;\"\u003e\n \u003cp\u003eMOL002501\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 181px;\"\u003e\n \u003cp\u003e[(1S)-3-[(E)-but-2-enyl]-2-methyl-4-oxo-1-cyclopent-2-enyl] (1 r, 3 r) - 3 - [(E) - 3 - methoxy - 2 - methyl - 3 - oxoprop - 1 - enyl] - 2, 2-1 - carboxylate dimethylcyclopropane -\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e62.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e0.31\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 153px;\"\u003e\n \u003cp\u003eMOL002962\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 181px;\"\u003e\n \u003cp\u003e(3 s) - 7 - hydroxy - 3 - (2, 4 - trimethoxyphenyl) chroman - 4 - one\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e48.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 153px;\"\u003e\n \u003cp\u003eMOL001323\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 181px;\"\u003e\n \u003cp\u003eSitosterol alpha1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e43.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e0.78\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 153px;\"\u003e\n \u003cp\u003eMOL000449\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 181px;\"\u003e\n \u003cp\u003eStigmasterol\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e43.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e0.76\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eADME, Absorption, Distribution, Metabolism and Excretion; OB, Oral intake; DL, Drug-likeness; WM, Molecular Weight.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3 Results of GO and KEGG Enrichment Analysis for KDGs\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 112px;\"\u003e\n \u003cp\u003eOntology\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003eID\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 326px;\"\u003e\n \u003cp\u003eDescription\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003eGeneRatio\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003eBgRatio\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003epvalue\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003ep.adjust\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003eqvalue\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 112px;\"\u003e\n \u003cp\u003eBP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003eGO:0033002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 326px;\"\u003e\n \u003cp\u003emuscle cell proliferation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e6/15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e233/18800\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e1.55e-08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e1.3e-05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e4.67375E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 112px;\"\u003e\n \u003cp\u003eBP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003eGO:0048732\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 326px;\"\u003e\n \u003cp\u003egland development\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e7/15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e431/18800\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e1.74e-08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e1.3e-05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e4.67375E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 112px;\"\u003e\n \u003cp\u003eBP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003eGO:0051052\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 326px;\"\u003e\n \u003cp\u003eregulation of DNA metabolic process\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e7/15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e472/18800\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e3.25e-08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e1.3e-05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e4.67375E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 112px;\"\u003e\n \u003cp\u003eBP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003eGO:0007346\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 326px;\"\u003e\n \u003cp\u003eregulation of mitotic cell cycle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e7/15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e478/18800\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e3.55e-08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e1.3e-05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e4.67375E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 112px;\"\u003e\n \u003cp\u003eBP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003eGO:1902895\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 326px;\"\u003e\n \u003cp\u003epositive regulation of miRNA transcription\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e4/15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e45/18800\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e3.83e-08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e1.3e-05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e4.67375E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 112px;\"\u003e\n \u003cp\u003eCC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003eGO:0000307\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 326px;\"\u003e\n \u003cp\u003ecyclin-dependent protein kinase holoenzyme complex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e3/15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e49/19594\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e6.55e-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e0.0006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e0.000310135\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 112px;\"\u003e\n \u003cp\u003eCC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003eGO:1902554\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 326px;\"\u003e\n \u003cp\u003eserine/threonine protein kinase complex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e3/15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e99/19594\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e5.45e-05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e0.0015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e0.000709639\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 112px;\"\u003e\n \u003cp\u003eCC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003eGO:0042470\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 326px;\"\u003e\n \u003cp\u003emelanosome\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e3/15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e109/19594\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e7.26e-05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e0.0015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e0.000709639\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 112px;\"\u003e\n \u003cp\u003eCC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003eGO:0048770\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 326px;\"\u003e\n \u003cp\u003epigment granule\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e3/15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e109/19594\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e7.26e-05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e0.0015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e0.000709639\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 112px;\"\u003e\n \u003cp\u003eCC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003eGO:1902911\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 326px;\"\u003e\n \u003cp\u003eprotein kinase complex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e3/15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e115/19594\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e8.51e-05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e0.0015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e0.000709639\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 112px;\"\u003e\n \u003cp\u003eMF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003eGO:0030332\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 326px;\"\u003e\n \u003cp\u003ecyclin binding\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e3/15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e34/18410\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e2.58e-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e0.0002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e7.26329E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 112px;\"\u003e\n \u003cp\u003eMF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003eGO:0031625\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 326px;\"\u003e\n \u003cp\u003eubiquitin protein ligase binding\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e5/15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e298/18410\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e2.83e-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e0.0002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e7.26329E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 112px;\"\u003e\n \u003cp\u003eMF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003eGO:0044389\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 326px;\"\u003e\n \u003cp\u003eubiquitin-like protein ligase binding\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e5/15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e317/18410\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e3.82e-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e0.0002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e7.26329E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 112px;\"\u003e\n \u003cp\u003eMF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003eGO:0001046\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 326px;\"\u003e\n \u003cp\u003ecore promoter sequence-specific DNA binding\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e3/15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e42/18410\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e4.93e-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e0.0002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e7.26329E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 112px;\"\u003e\n \u003cp\u003eMF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003eGO:0035173\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 326px;\"\u003e\n \u003cp\u003ehistone kinase activity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e2/15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e16/18410\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e7.39e-05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e0.0020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e0.000870859\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 112px;\"\u003e\n \u003cp\u003eKEGG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003ehsa05215\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 326px;\"\u003e\n \u003cp\u003eProstate cancer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e7/15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e97/8164\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e1.6e-10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e2.43e-08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e1.19505E-08\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 112px;\"\u003e\n \u003cp\u003eKEGG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003ehsa05219\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 326px;\"\u003e\n \u003cp\u003eBladder cancer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e5/15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e41/8164\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e7.19e-09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e5.46e-07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e2.68518E-07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 112px;\"\u003e\n \u003cp\u003eKEGG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003ehsa04151\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 326px;\"\u003e\n \u003cp\u003ePI3K-Akt signaling pathway\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e8/15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e354/8164\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e5.7e-08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e2.89e-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e1.41886E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 112px;\"\u003e\n \u003cp\u003eKEGG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003ehsa04218\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 326px;\"\u003e\n \u003cp\u003eCellular senescence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e6/15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e156/8164\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e1.92e-07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e7.29e-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e3.58685E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 112px;\"\u003e\n \u003cp\u003eKEGG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003ehsa05222\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 326px;\"\u003e\n \u003cp\u003eSmall cell lung cancer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e5/15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e92/8164\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e4.47e-07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e1.36e-05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e6.68875E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eGO, Gene Ontology; BP, Biological Process; CC, Cellular Component; MF, Molecular Function; KEGG, Kyoto Encyclopedia of Genes and Genomes; KDGS, Key Difference Genes.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData availability statement:\u003c/strong\u003eThe data sets presented in this study can be found in online repositories.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eEthics Approval:\u0026nbsp;\u003c/strong\u003eThis study does not contain any studies with hunman participants or animals performed by any of authors. GEO belong to public databases. The patients involved in the database have obtained ethical approval. Users can download relevant data for free for research and publish relevant articles. Our study is based on open source data, so there are no ethical issues and other conflicts of interest.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eAcknowledgement:\u003c/strong\u003eNone.We acknowledge TCGA and GEO database for providing their platforms and contributors for uploading their meaningful datasets.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eDeclaration of Conflict of Interest:\u0026nbsp;\u003c/strong\u003eThe authors declare that there are no conflicts of interestregarding the publication of this paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding Source Declaration\u003c/strong\u003e\u003cstrong\u003e：\u003c/strong\u003eNone.\u003c/p\u003e\n"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eZhang Y, Wang Y, Xu J, Wang Z, Zhao W, Zhao C. Visceral adipose tissue and osteoarthritis, a two-sample Mendelian randomized study. Front Med (Lausanne). 2023;10:1324449.\u003c/li\u003e\n \u003cli\u003eGusho CA, Jenson M. Demographic tendencies and hospitalization outcomes among inpatient admissions of osteoarthritis in the Midwest: A 2016 state inpatient database study. Cureus. 2020;12(5):e7959.\u003c/li\u003e\n \u003cli\u003eChen W, Sun Z, Xiong X, Tan H, Hu J, Liu C, Chen C. Exploring the causal link among statin drugs and the osteoarthritis risk based on Mendelian randomization research. Front Genet. 2024;15:1390387.\u003c/li\u003e\n \u003cli\u003eDavis S MP. GEOquery: a bridge between the Gene Expression Omnibus (GEO) and BioConductor. Bioinformatics 2007 Jul 15; 23 (14) : 1846-7.\u003c/li\u003e\n \u003cli\u003eWoetzel D, Huber R, Kupfer P, Pohlers D, Pfaff M, Driesch D, et al. Identification of rheumatoid arthritis and osteoarthritis patients by transcriptome-based rule set generation. Arthritis research \u0026amp; therapy 2014; 16(2):R84\u003c/li\u003e\n \u003cli\u003eBarrett T, Wilhite SE, Ledoux P, Evangelista C, Kim IF, Tomashevsky M, et al. NCBI GEO: archive for functional genomics data sets--update. Nucleic Acids Res 2013; 41(Database issue):D991-995.\u003c/li\u003e\n \u003cli\u003eStelzer G, Rosen N, Plaschkes I, Zimmerman S, Twik M, Fishilevich S, et al. The GeneCards Suite: From Gene Data Mining to Disease Genome Sequence Analyses. Curr Protoc Bioinformatics 2016; 27. 30.31 31.30.33.\u003c/li\u003e\n \u003cli\u003eLiberzon A, Subramanian A, Pinchback R, Thorvaldsdottir H, Tamayo P, Molecular signatures database (MSigDB) 3.0. Bioinformatics 2011; 27 (12) : 1739-1740.\u003c/li\u003e\n \u003cli\u003eLeek JT, Johnson WE, Parker HS, Jaffe AE, Storey JD. The sva package for removing batch effects and other unwanted variation in high-throughput experiments. 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J Cheminform 2014; 13.\u003c/li\u003e\n \u003cli\u003eFan J, de Lannoy IA. Pharmacokinetics. Biochem Pharmacol 2014; 87 (1) : 93-120.\u003c/li\u003e\n \u003cli\u003eBoutet E, Lieberherr D, Tognolli M, Schneider M, Bairoch A. UniProtKB/Swiss-Prot. Methods Mol Biol 2007; 406:89-112.\u003c/li\u003e\n \u003cli\u003eShannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res 2003; 13 (11) : 2498-2504.\u003c/li\u003e\n \u003cli\u003eYu G, Wang LG, Han Y, He QY. clusterProfiler: an R package for comparing biological themes among gene clusters. Omics 2012; 16 (5) : 284-287.\u003c/li\u003e\n \u003cli\u003eMi H, Muruganujan A, Ebert D, Huang X, Thomas PD. PANTHER version 14: more genomes, a new PANTHER GO-slim and improvements in enrichment analysis tools. Nucleic Acids Res 2019; 47(D1):D419-d426.\u003c/li\u003e\n \u003cli\u003eKanehisa M, Goto S. KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Res 2000; 28 (1) : 27 to 30.\u003c/li\u003e\n \u003cli\u003eSzklarczyk D, Gable AL, Lyon D, Junge A, Wyder S, Huerta-Cepas J, et al. STRING v11: protein-protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic acids research 2019; 47(D1):D607-d613.\u003c/li\u003e\n \u003cli\u003eChin CH, Chen SH, Wu HH, Ho CW, Ko MT, Lin CY. cytoHubba: identifying hub objects and sub-networks from complex interactome. BMC systems biology 2014; 8 Suppl 4(Suppl 4):S11.\u003c/li\u003e\n \u003cli\u003eShannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, et al. Cytoscape: A Software Environment for Integrated Models of Biomolecular Interaction Networks. Genome Research 2003; 13 (11) : 2498-2504.\u003c/li\u003e\n \u003cli\u003eYang X, Li Y, Lv R, Qian H, Chen X, Yang CF. Study on the Multitarget Mechanism and Key Active Ingredients of Herba Siegesbeckiae and Volatile Oil against Rheumatoid Arthritis Based on Network Pharmacology. Evidence-based complementary and alternative medicine : eCAM 2019; 2019:8957245.\u003c/li\u003e\n \u003cli\u003eRobin X, Turck N, Hainard A, Tiberti N, Lisacek F, Sanchez JC, et al. pROC: an open-source package for R and S+ to analyze and compare ROC curves. BMC Bioinformatics 2011; \u0026quot;7.\u003c/li\u003e\n \u003cli\u003eYu G, Li F, Qin Y, Bo X, Wu Y, Wang S. GOSemSim: an R package for measuring semantic similarity among GO terms and gene products. Bioinformatics 2010; 26 (7) : 976-978.\u003c/li\u003e\n \u003cli\u003eNewman AM, Liu CL, Green MR, Gentles AJ, Feng W, Xu Y, et al. Robust enumeration of cell subsets from tissue expression profiles. Nat Methods 2015; 12 (5) : 453-457.\u003c/li\u003e\n \u003cli\u003eHaq SA, Davatchi F. Osteoarthritis of the knees in the COPCORD world. Int J Rheum Dis. 2011;14(2):122-9. doi:10.1111/j.1756-185X.2011.01615.x\u003c/li\u003e\n \u003cli\u003eAldosari AA, Majadah S, Amer KA, et al. The Association Between Physical Activity Level and Severity of Knee Osteoarthritis: A Single Centre Study in Saudi Arabia. Cureus. 2022;14(4):e24377. Published 2022 Apr. doi:10.7759/cureus.24377\u003c/li\u003e\n \u003cli\u003eZhou C, Wang KS, Peng W, Yuan FL, Si ZP. Tra2\u0026beta; protects against the degeneration of chondrocytes by inhibiting chondrocyte apoptosis via activating the PI3K/Akt signaling pathway. Eur Rev Med Pharmacol Sci. 2020;24(17):8665-8674.\u003c/li\u003e\n \u003cli\u003eWu RC, Young IC, Chen YF, Chuang ST, Toubaji A, Wu MY. Identification of the PTEN-ARID4B-PI3K pathway reveals the dependency on ARID4B by PTEN-deficient prostate cancer. Nat Commun. 2019;10(1):4332.\u003c/li\u003e\n \u003cli\u003eJin Q, Liu Y, Zhang Z, et al. MYC promotes fibroblast osteogenesis by regulating ALP and BMP2 to participate in ectopic ossification of ankylosing spondylitis. Arthritis Res Ther. 2023;25(1):28.\u003c/li\u003e\n \u003cli\u003eLorenzo-G\u0026oacute;mez I, Nogueira-Recalde U, Garc\u0026iacute;a-Dom\u0026iacute;nguez C, et al. Defective chaperone-mediated autophagy is a hallmark of joint disease in patients with knee osteoarthritis. Osteoarthritis Cartilage. 2023;31(7):919-933.\u003c/li\u003e\n \u003cli\u003eFang C, Zhu S, Zhong R, et al. CDKN1A regulation on chondrogenic differentiation of human chondrocytes in osteoarthritis through single-cell and bulk sequencing analysis. Heliyon. 2024;10(5):e27466. Published 2024 Mar 15. doi:10.1016/j.heliyon.2024.e27466\u003c/li\u003e\n \u003cli\u003eZhu K, Zhang Y, Li D, et al. MiR-29a-3p mediates phosphatase and tensin homolog and inhibits osteoarthritis progression. Funct Integr Genomics. 2024;24(2):54.\u003c/li\u003e\n \u003cli\u003eChen Z, Ma Y, Li X, Deng Z, Zheng M, Zheng Q. The Immune Cell Landscape in Different Anatomical Structures of Knee in Osteoarthritis: A Gene Expression-Based Study. Biomed Res Int. 2020:9647072.\u003c/li\u003e\n \u003cli\u003eDas N, de Almeida LGN, Derakhshani A, et al. Tryptase \u0026beta; regulation of joint lubrication and inflammation via proteoglycan-4 in osteoarthritis. Nat Commun. 2023;14(1):1910.\u003c/li\u003e\n \u003cli\u003eGuida F, Rocco M, Luongo L, et al. Targeting Neuroinflammation in Osteoarthritis with Intra-Articular Adelmidrol. Biomolecules. 2022;12(10).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Osteoarthritis, Traditional Chinese Medicine, Bioinformatics, Network pharmacology, Immune Infiltration Analysis","lastPublishedDoi":"10.21203/rs.3.rs-5765774/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5765774/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eOsteoarthritis (OA) is a common degenerative joint disorder marked by the deterioration of cartilage, joint discomfort, and inflammation. The Bushenhuoluo Decoction (BSHLD) is a traditional Chinese remedy utilized for the management of OA. The aim of this study is to examine and elucidate the mechanism of BSHLD and how the compound interacts with the target of OA.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe used a network pharmacology approach that integrates multiple bioinformatics techniques to study OA. First, we collected and analyzed OA datasets from the Gene Expression Omnibus with the R package GEOquery. To identify key differentially expressed genes (DEGs), we performed differential expression analysis using the limma package. We obtained target genes from the Online Mendelian Inheritance in Man (OMIM) and the Comparative Toxicogenomics Database (CTD). Additionally, we obtained components of traditional Chinese medicine and their targets from the Traditional Chinese Medicine Systems Pharmacology Database (TCMSP). To enhance our understanding of the biological processes and pathways involved, we conducted enrichment analyses using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). We constructed protein-protein interaction networks to pinpoint crucial genes, which we then validated using receiver operating characteristic curve analysis. Lastly, we assessed immune cell infiltration through the CIBERSORT algorithm.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eDifferential expression analysis using the limma package identified key difference genes (KDGs) by intersecting DEGs with OA-RELAted genes, TCM target genes, and RBIRGs, resulting in 16 KDGs. GO and KEGG pathway enrichment analyses revealed significant biological processes and pathways, including muscle cell proliferation and PI3K-Akt signaling. Protein-protein interaction (PPI) networks were constructed using STRING, and hub genes were identified through CytoHubba algorithms, highlighting nine hub genes, including MYC and CDKN1A. Expression validation and ROC curve analysis demonstrated the diagnostic potential of hub genes, with \u003cem\u003eHSP90AA1\u003c/em\u003e, \u003cem\u003eMYC\u003c/em\u003e, and \u003cem\u003eCDKN1A\u003c/em\u003e showing high accuracy (AUC\u0026thinsp;\u0026gt;\u0026thinsp;0.9). The immune infiltration analysis showed a significant positive corRELAtion between the hub gene \u003cem\u003eMYC\u003c/em\u003e and activated mast cells. There was a significant negative corRELAtion between hub gene \u003cem\u003eCDKN1A\u003c/em\u003e and immune cell Mast cells resting.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThese findings provide valuable insights into the molecular interactions of BSHLD in OA treatment, potentially revealing therapeutic targets and pathways for future studies.\u003c/p\u003e","manuscriptTitle":"Study on the Molecular Mechanism amd Immune Cell Infiltration of Bushenhuoluo Decoction in Osteoarthritis Treatment Based on Network Pharmacology","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-01-09 17:20:36","doi":"10.21203/rs.3.rs-5765774/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"d21ca719-67ff-43b7-90b1-419fa1a63991","owner":[],"postedDate":"January 9th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-02-16T15:23:11+00:00","versionOfRecord":[],"versionCreatedAt":"2025-01-09 17:20:36","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5765774","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5765774","identity":"rs-5765774","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}