CD19, ALDH18A1, and CACNA1G as Significant Hub Genes in End-Stage Osteoarthritis
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ABSTRACT:
SUBMITTER: Malakootian M
PROVIDER: S-EPMC10903304 | biostudies-literature | 2023 Dec
REPOSITORIES: biostudies-literature
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Project description:Osteoarthritis (OA) is a progressive cartilage degradation disease, concomitant with synovitis, osteophyte formation, and subchondral bone sclerosis. Over 37% of the elderly population is affected by OA, and the number of cases is increasing as the global population ages. Therefore, the objective of this study was to identify and analyze the hub genes of OA combining with comprehensive bioinformatics analysis tools to provide theoretical basis in further OA effective therapies. Two sample sets of GSE46750 contained 12 pairs OA synovial membrane and normal samples harvested from patients as well as GSE98918 including 12 OA and non-OA patients were downloaded from the Gene Expression Omnibus database (GEO) database. Differentially expressed genes (DEGs) were identified using Gene Expression Omnibus 2R (GEO2R), followed by functional enrichment analysis, protein-protein interaction networks construction. The hub genes were identified and evaluated. An OA rat model was constructed, hematoxylin and eosin staining, safranin O/fast green staining, cytokines concentrations of serum were used to verify the model. The hub genes expression level in the knee OA samples were verified using RT-qPCR. The top 20 significantly up-regulated and down-regulated DEGs were screened out from the two datasets, respectively. The top 18 GO terms and 10 KEGG pathways were enriched. Eight hub genes were identified, namely MS4A6A, C1QB, C1QC, CD74, CSF1R, HLA-DPA1, HLA-DRA and ITGB2. Among them, the hub genes were all up-regulated in in vivo OA rat model, compared with healthy controls. The eight hub genes identified (MS4A6A, C1QB, C1QC, CD74, CSF1R, HLA-DPA1, HLA-DRA and ITGB2) were shown to be associated with OA. These genes can serve as disease markers to discriminate OA patients from healthy controls.
| S-EPMC10739719 | biostudies-literature
Project description:Background Osteoarthritis (OA) is the most common type of arthritis. OA can cause joint pain, stiffness, and loss of function. The pathogenesis of OA is not completely clear. Moreover, there is no effective treatment, and clinical management is limited to symptomatic relief or joint surgery. This study utilized bioinformatics to analyze normal and OA articular cartilage samples to find biomarkers and therapeutic targets for OA. Methods The GSE169077 gene chip dataset was downloaded from the public gene chip data platform of the National Biotechnology Information Center. The dataset included 6 samples of OA tissues and 5 samples of healthy cartilage tissues. Differentially expressed genes (DEGs) were screened using the R language “limma” function package under the threshold of log2[fold change (FC)] ≥2 and a P value <0.05. The Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) signal pathways of the target genes were enriched and analyzed using the database for annotation, visualization, and integrated discovery (DAVID), and a protein-protein interaction (PPI) network was further constructed using the search tool for the retrieval of interacting genes/proteins (STRING) database. The coexpression relationship of the genes in the module was visualized and screened with Cytoscape. Results A total of 27 DEGs were identified, including 9 downregulated genes and 18 upregulated genes. GO signal pathway enrichment analysis showed involvement in hypoxic response, fibrous collagen trimer, and extracellular matrix structural components. KEGG analysis demonstrated associations with protein digestion and absorption, extracellular matrix receptor interaction, and the peroxisome proliferator-activated receptor signal pathway, among several other pathways. A PPI network was obtained through STRING analysis, and the results were imported into Cytoscape software. The 27 DEGs were sequenced by the cytoHubba plug-in by various calculation methods, and 5 hub genes (COL1A1, COL1A2, POSTN, BMP1, and MMP13) were finally selected. These genes were analyzed by PPI again and annotated with GO and KEGG in different colors. Conclusions Bioinformatics technology effectively identified differential genes in the knee cartilage tissue of healthy controls and patients with OA, providing opportunities to further explore the mechanism and treatment of OA on a transcriptional level.
| S-EPMC9929772 | biostudies-literature
Project description:BackgroundOsteoarthritis (OA) is the most common degenerative disease in orthopedics. However, the cause and underlying molecular mechanisms are not clear. This study aims to identify the hub genes and pathways involved in the occurrence of osteoarthritis.MethodsThe raw data of GSE89408 were downloaded from the Gene Expression Omnibus (GEO) database, and the differentially expressed genes (DEGs) were identified by R software. The DAVID database was used for pathway and gene ontology analysis, and p<0.05 and gene count >2 were set as the cut-off point. Moreover, protein-protein interaction (PPI) network construction was applied for exploring the hub genes in osteoarthritis. The expression levels of the top ten hub genes in knee osteoarthritis synovial membranes and controls were detected by quantitative real-time PCR system.ResultsA total of 229 DEGs were identified in osteoarthritis synovial membranes compared with normal synovial membranes, including 145 upregulated and 84 downregulated differentially expressed genes. The KEGG pathway analysis results showed that up-DEGs were enriched in proteoglycans in cytokine-cytokine receptor interaction, chemokine signaling pathway, rheumatoid arthritis, and TNF signaling pathway, whereas down-DEGs were enriched in the PPAR signaling pathway and AMPK signaling pathway. The qRT-PCR results showed that the expression levels of ADIPOQ, IL6, and CXCR1 in the synovium of osteoarthritis were significantly increased (p <0.05).
| S-EPMC6500622 | biostudies-literature
Project description:ObjectiveWe aimed to describe the natural history leading to end-stage knee osteoarthritis (esKOA), focusing on knee symptoms, radiographic severity, and the presence of limited mobility or instability.MethodsWe performed knee-based analyses of Osteoarthritis Initiative data from 7691 knees (4165 participants). We used a validated definition of esKOA that relied on meeting one of two criteria: (1) severe radiographic knee osteoarthritis (Kellgren-Lawrence [KL] grade=4) with moderate-to-intense pain (Likert WOMAC pain+function>11/88) or (2) KL grade<4 with intense or severe pain (WOMAC pain+function>22) and limited mobility (flexion contracture≥5°) or instability (based on a varus and valgus stress test). We also introduced an alternate definition of esKOA that relied on meeting one of two criteria that omitted physical exam findings:(1) severe radiographic knee osteoarthritis (KL grade=4) with at least moderate symptoms or (2) KL grade=2 or 3 with intense or severe symptoms and persistent knee pain (frequent knee pain during three or more months in the past year). We used descriptive statistics to explore the frequency of components of esKOA at the index visit when they had incident esKOA, at the annual visit before developing esKOA, and the interval change between those visits.ResultsOur analytic sample was mostly female (58%), without radiographic knee osteoarthritis (KL grade=0 or 1; 60%), without stability or mobility concerns (91%), and without persistent knee pain (77%). At the visit before incident esKOA, most knees already had moderate-to-severe radiographic osteoarthritis using the original (62%) or alternate (50%) definition (versus <15% for either definition of no esKOA). Over 80% of knees that reached the criteria for esKOA achieved this based on increased knee symptom severity - typically without worsening radiographic severity (80%).ConclusionRadiographic severity predisposed a knee to esKOA. However, worsening knee symptoms led to the development of incident esKOA. If investigators want to increase the chance of identifying incident esKOA as an outcome, they should enrich their study samples with people with moderate-to-severe radiographic osteoarthritis. Our findings also highlight the potential reversibility of esKOA (a knee that is classified with esKOA but later is not classified with esKOA). Reversibility is not a flaw of an outcome defining esKOA but rather a desirable clinical outcome to demonstrate a therapeutic intervention can help people with esKOA improve their knee symptoms and delay a knee replacement.
| S-EPMC9840689 | biostudies-literature
Project description:The present study was performed to explore the underlying molecular mechanisms and screen hub genes of osteoarthritis (OA) via bioinformatics analysis. In total, twenty-five OA synovial tissue samples and 25 normal synovial tissue samples were derived from three datasets, namely, GSE55457, GSE55235, and GSE1919, and were used to identify the differentially expressed genes (DEGs) of OA by R language. The Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis of DEGs were conducted using the Database for Annotation, Visualization, and Integrated Discovery (DAVID). A Venn diagram was built to show the potential hub genes identified in all three datasets. The STRING database was used for constructing the protein-protein interaction (PPI) networks and submodules of DEGs. We identified 507 upregulated and 620 downregulated genes. Upregulated DEGs were significantly involved in immune response, MHC class II receptor activity, and presented in the extracellular region, while downregulated DEGs were mainly enriched in response to organic substances, extracellular region parts, and cadmium ion binding. Results of KEGG analysis indicated that the upregulated DEGs mainly existed in cell adhesion molecules (CAMs), while downregulated DEGs were significantly involved in the MAPK signaling pathway. A total of eighteen intersection genes were identified across the three datasets. These include Nell-1, ATF3, RhoB, STC1, and VEGFA. In addition, 10 hub genes including CXCL12, CXCL8, CCL20, and CCL4 were found in the PPI network and module construction. Identification of DEGs and hub genes associated with OA may be helpful for revealing the molecular mechanisms of OA and further promotes the development of relevant biomarkers and drug targets.
| S-EPMC9207392 | biostudies-literature
Project description:Osteoarthritis (OA) is a common cause of morbidity and disability worldwide. However, the pathogenesis of OA is unclear. Therefore, this study was conducted to characterize the pathogenesis and implicated genes of OA. The gene expression profiles of GSE82107 and GSE55235 were downloaded from the Gene Expression Omnibus database. Altogether, 173 differentially expressed genes including 68 upregulated genes and 105 downregulated genes in patients with OA were selected based on the criteria of ∣log fold-change | >1 and an adjusted p value < 0.05. Protein-protein interaction network analysis showed that FN1, COL1A1, IGF1, SPP1, TIMP1, BGN, COL5A1, MMP13, CLU, and SDC1 are the top ten genes most closely related to OA. Quantitative reverse transcription-polymerase chain reaction showed that the expression levels of COL1A1, COL5A1, TIMP1, MMP13, and SDC1 were significantly increased in OA. This study provides clues for the molecular mechanism and specific biomarkers of OA.
| S-EPMC7428874 | biostudies-literature
Project description:ObjectiveThis study constructs a risk score for patients' progression to end-stage knee osteoarthritis (OA) within 4 years.DesignThe Osteoarthritis Initiative (OAI) was a longitudinal study of the onset and progression of knee OA. Using a recent definition of end-stage knee OA, we implement interval-censored survival forests to select predictors of this endpoint. We fit an interval-censored Cox model for time to end-stage knee OA, using the selected predictors. The risk score is the Cox model's fitted linear combination of the nine selected baseline structural and symptomatic knee OA variables.ResultsWe fit our models on a training set of 2,701 patients, and we evaluate on an independent test set of 1,436 patients. On the test sample, we observe a concordance index of 0.86 between risk score and time to end-stage, AUC of 0.87 for predicting end-stage within 24, 36, and 48 months, and positive predictive values that increase with the risk score. This risk stratification algorithm could enrich clinical trial patient enrollment. By enrolling test sample patients with scores above a threshold, a trial could have included 91% of test set patients who reach end-stage within 4 years while only enrolling 45% of the test sample.ConclusionUsing statistical methods, we construct and validate an interpretable risk score for time to end-stage knee OA. This score can help disease-modifying OA treatment developers to select candidates with the highest risk of fast-progressing knee OA.
| S-EPMC7575033 | biostudies-literature
Project description:BackgroundOxidative stress is proposed as an important factor in osteoarthritis (OA).ObjectiveTo investigate the expression of the three superoxide dismutase (SOD) antioxidant enzymes in OA.MethodsSOD expression was determined by real-time PCR and immunohistochemistry using human femoral head cartilage. SOD2 expression in Dunkin-Hartley guinea pig knee articular cartilage was determined by immunohistochemistry. The DNA methylation status of the SOD2 promoter was determined using bisulphite sequencing. RNA interference was used to determine the consequence of SOD2 depletion on the levels of reactive oxygen species (ROS) using MitoSOX and collagenases, matrix metalloproteinase 1 (MMP-1) and MMP-13, gene expression.ResultsAll three SOD were abundantly expressed in human cartilage but were markedly downregulated in end-stage OA cartilage, especially SOD2. In the Dunkin-Hartley guinea pig spontaneous OA model, SOD2 expression was decreased in the medial tibial condyle cartilage before, and after, the development of OA-like lesions. The SOD2 promoter had significant DNA methylation alterations in OA cartilage. Depletion of SOD2 in chondrocytes increased ROS but decreased collagenase expression.ConclusionThis is the first comprehensive expression profile of all SOD genes in cartilage and, importantly, using an animal model, it has been shown that a reduction in SOD2 is associated with the earliest stages of OA. A decrease in SOD2 was found to be associated with an increase in ROS but a reduction of collagenase gene expression, demonstrating the complexities of ROS function.
| S-EPMC3789136 | biostudies-literature
Project description:Osteoarthritis (OA) is one of the most common causes of disability and its development is associated with numerous factors. A major challenge in the treatment of OA is the lack of early diagnosis. In the present study, a bioinformatics method was employed to filter key genes that may be responsible for the pathogenesis of OA. From the Gene Expression Omnibus database, the datasets GSE55457, GSE12021 and GSE55325 were downloaded, which comprised 59 samples. Of these, 30 samples were from patients diagnosed with osteoarthritis and 29 were normal. Differentially expressed genes (DEGs) were obtained by downloading and analyzing the original data using bioinformatics. The Gene Ontology enrichment and Kyoto Encyclopedia of Genes and Genomes pathways were analyzed using the Database for Annotation, Visualization and Integrated Discovery online database. Protein-protein interaction network analysis was performed using the Search Tool for the Retrieval of Interacting Genes/proteins online database. BSCL2 lipid droplet biogenesis associated, seipin, FOS-like 2, activator protein-1 transcription factor subunit (FOSL2), cyclin-dependent kinase inhibitor 1A (CDKN1A) and kinectin 1 (KTN1) genes were identified as key genes by using Cytoscape software. Functional enrichment revealed that the DEGs were mainly accumulated in the ErbB, MAPK and PI3K-Akt pathways. Reverse transcription-quantitative PCR analysis confirmed a significant reduction in the expression levels of FOSL2, CDKN1A and KTN1 in OA samples. These genes have the potential to become novel diagnostic and therapeutic targets for OA.
| S-EPMC7903481 | biostudies-literature
Project description:Glutamine metabolism is pivotal in cancer biology, profoundly influencing tumor growth, proliferation, and resistance to therapies. Cancer cells often exhibit an elevated dependence on glutamine for essential functions such as energy production, biosynthesis of macromolecules, and maintenance of redox balance. Moreover, altered glutamine metabolism can contribute to the formation of an immune-suppressive tumor microenvironment characterized by reduced immune cell infiltration and activity. In this study on lung adenocarcinoma, we employed consensus clustering and applied 101 types of machine learning methods to systematically identify key genes associated with glutamine metabolism and develop a risk model. This comprehensive approach provided a clearer understanding of how glutamine metabolism associates with cancer progression and patient outcomes. Notably, we constructed a robust nomogram based on clinical information and patient risk scores, which achieved a stable area under the curve (AUC) greater than 0.8 for predicting patient survival across four datasets, demonstrating high predictive accuracy. This nomogram not only enhances our ability to stratify patient risk but also offers potential targets for therapeutic intervention aimed at disrupting glutamine metabolism and sensitizing tumors to existing treatments. Moreover, we identified ALDH18A1 as a prognostic hub gene of glutamine metabolism, characterized by high expression levels in glutamine cluster 3, which is associated with poor clinical outcomes and worse survival, and is included in the risk model. Such insights underscore the critical role of glutamine metabolism in cancer and highlight avenues for personalized medicine in oncology research.
| S-EPMC11695527 | biostudies-literature