scholarly journals Identification of Potential Biomarkers for Thyroid Cancer Using Bioinformatics Strategy: A Study Based on GEO Datasets

2020 ◽  
Vol 2020 ◽  
pp. 1-21 ◽  
Author(s):  
Yujie Shen ◽  
Shikun Dong ◽  
Jinhui Liu ◽  
Liqing Zhang ◽  
Jiacheng Zhang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Thyroid Cancer ◽  
Small Molecules ◽  
Signaling Pathway ◽  
Pathway Analysis ◽  
Molecular Mechanisms ◽  
Expression Profiles ◽  
Receptor Interaction ◽  
Tissue Samples ◽  
Key Genes

Background. The molecular mechanisms and genetic markers of thyroid cancer are unclear. In this study, we used bioinformatics to screen for key genes and pathways associated with thyroid cancer development and to reveal its potential molecular mechanisms. Methods. The GSE3467, GSE3678, GSE33630, and GSE53157 expression profiles downloaded from the Gene Expression Omnibus database (GEO) contained a total of 164 tissue samples (64 normal thyroid tissue samples and 100 thyroid cancer samples). The four datasets were integrated and analyzed by the RobustRankAggreg (RRA) method to obtain differentially expressed genes (DEGs). Using these DEGs, we performed gene ontology (GO) functional annotation, pathway analysis, protein-protein interaction (PPI) analysis and survival analysis. Then, CMap was used to identify the candidate small molecules that might reverse thyroid cancer gene expression. Results. By integrating the four datasets, 330 DEGs, including 154 upregulated and 176 downregulated genes, were identified. GO analysis showed that the upregulated genes were mainly involved in extracellular region, extracellular exosome, and heparin binding. The downregulated genes were mainly concentrated in thyroid hormone generation and proteinaceous extracellular matrix. Pathway analysis showed that the upregulated DEGs were mainly attached to ECM-receptor interaction, p53 signaling pathway, and TGF-beta signaling pathway. Downregulation of DEGs was mainly involved in tyrosine metabolism, mineral absorption, and thyroxine biosynthesis. Among the top 30 hub genes obtained in PPI network, the expression levels of FN1, NMU, CHRDL1, GNAI1, ITGA2, GNA14 and AVPR1A were associated with the prognosis of thyroid cancer. Finally, four small molecules that could reverse the gene expression induced by thyroid cancer, namely ikarugamycin, adrenosterone, hexamethonium bromide and clofazimine, were obtained in the CMap database. Conclusion. The identification of the key genes and pathways enhances the understanding of the molecular mechanisms for thyroid cancer. In addition, these key genes may be potential therapeutic targets and biomarkers for the treatment of thyroid cancer.

scholarly journals Identification of Key Genes and Pathways Associated with Mast Cells Resting in Meningioma

2020 ◽  
Author(s):  
Hui Xie ◽  
Xiao-hui Ding ◽  
Ce Yuan ◽  
Jin-jiang Li ◽  
Zhao-yang Li ◽  
...  
Keyword(s):  
Gene Expression ◽  
Mast Cells ◽  
Signaling Pathway ◽  
Molecular Mechanisms ◽  
Immune Cell ◽  
Risk Model ◽  
Kegg Pathway ◽  
Expression Profiles ◽  
Receptor Interaction ◽  
Key Genes

Abstract Background: To identify candidate key genes and pathways related to mast cells resting in meningioma and the underlying molecular mechanisms of meningioma.Methods: Gene expression profiles of GSE43290 and GSE16581 datasets were obtained from the Gene Expression Omnibus (GEO) database. GO and KEGG pathway enrichments of DEGs were analyzed using the ClusterProfiler package in R. The protein-protein interaction network (PPI), and TF-miRNA- mRNA co-expression networks were constructed. Further, the difference in immune infiltration was investigated using the CIBERSORT algorithm.Results: A total of 1499 DEGs were identified between tumor and normal controls. The analysis of the immune cell infiltration landscape showed that the probability of distribution of memory B cells, regulatory T cells (Tregs), and resting mast cells in tumor samples were significantly higher than those in the controls. Moreover, through WGCNA analysis, the module related to mast cells resting contained 158 DEGs, and KEGG pathway analysis revealed that the DEGs were dominant in the TNF signaling pathway, cytokine-cytokine receptor interaction, and IL-17 signaling pathway. Survival analysis of hub genes related to mast cells resting showed that the risk model was constructed based on 9 key genes. The TF-miRNA- mRNA co-regulation network, including MYC-miR-145-5p, TNFAIP3-miR-29c-3p, and TNFAIP3-hsa-miR-335-3p, were obtained. Further, 36 nodes and 197 interactions in the PPI network were identified. Conclusions: The results of this study revealed candidate key genes, miRNAs, and pathways related to mast cells resting involved in meningioma development, providing potential therapeutic targets for meningioma treatment.

scholarly journals Identification of key genes and pathways associated with resting mast cells in meningioma

BMC Cancer ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Hui Xie ◽  
Ce Yuan ◽  
Xiao-hui Ding ◽  
Jin-jiang Li ◽  
Zhao-yang Li ◽  
...  
Keyword(s):  
Gene Expression ◽  
Mast Cells ◽  
Signaling Pathway ◽  
Molecular Mechanisms ◽  
Immune Cell ◽  
Risk Model ◽  
Kegg Pathway ◽  
Expression Profiles ◽  
Receptor Interaction ◽  
Key Genes

Abstract Background To identify candidate key genes and pathways related to resting mast cells in meningioma and the underlying molecular mechanisms of meningioma. Methods Gene expression profiles of the used microarray datasets were obtained from the Gene Expression Omnibus (GEO) database. GO and KEGG pathway enrichments of DEGs were analyzed using the ClusterProfiler package in R. The protein-protein interaction network (PPI), and TF-miRNA- mRNA co-expression networks were constructed. Further, the difference in immune infiltration was investigated using the CIBERSORT algorithm. Results A total of 1499 DEGs were identified between tumor and normal controls. The analysis of the immune cell infiltration landscape showed that the probability of distribution of memory B cells, regulatory T cells (Tregs), and resting mast cells in tumor samples were significantly higher than those in the controls. Moreover, through WGCNA analysis, the module related to resting mast cells contained 158 DEGs, and KEGG pathway analysis revealed that the DEGs were dominant in the TNF signaling pathway, cytokine-cytokine receptor interaction, and IL-17 signaling pathway. Survival analysis of hub genes related to resting mast cells showed that the risk model was constructed based on 9 key genes. The TF-miRNA- mRNA co-regulation network, including MYC-miR-145-5p, TNFAIP3-miR-29c-3p, and TNFAIP3-hsa-miR-335-3p, were obtained. Further, 36 nodes and 197 interactions in the PPI network were identified. Conclusion The results of this study revealed candidate key genes, miRNAs, and pathways related to resting mast cells involved in meningioma development, providing potential therapeutic targets for meningioma treatment.

scholarly journals Identification of Key Genes And Pathways Associated With Mast Cells Resting In Meningioma

2021 ◽  
Author(s):  
Hui Xie ◽  
Ce Yuan ◽  
Xiao-hui Ding ◽  
Jin-jiang Li ◽  
Zhao-yang Li ◽  
...  
Keyword(s):  
Gene Expression ◽  
Mast Cells ◽  
Signaling Pathway ◽  
Molecular Mechanisms ◽  
Immune Cell ◽  
Risk Model ◽  
Kegg Pathway ◽  
Expression Profiles ◽  
Receptor Interaction ◽  
Key Genes

Abstract Background: To identify candidate key genes and pathways related to mast cells resting in meningioma and the underlying molecular mechanisms of meningioma. Methods: Gene expression profiles of GSE43290 and GSE16581 datasets were obtained from the Gene Expression Omnibus (GEO) database. GO and KEGG pathway enrichments of DEGs were analyzed using the ClusterProfiler package in R. The protein-protein interaction network (PPI), and TF-miRNA- mRNA co-expression networks were constructed. Further, the difference in immune infiltration was investigated using the CIBERSORT algorithm. Results: A total of 1499 DEGs were identified between tumor and normal controls. The analysis of the immune cell infiltration landscape showed that the probability of distribution of memory B cells, regulatory T cells (Tregs), and resting mast cells in tumor samples were significantly higher than those in the controls. Moreover, through WGCNA analysis, the module related to mast cells resting contained 158 DEGs, and KEGG pathway analysis revealed that the DEGs were dominant in the TNF signaling pathway, cytokine-cytokine receptor interaction, and IL-17 signaling pathway. Survival analysis of hub genes related to mast cells resting showed that the risk model was constructed based on 9 key genes. The TF-miRNA- mRNA co-regulation network, including MYC-miR-145-5p, TNFAIP3-miR-29c-3p, and TNFAIP3-hsa-miR-335-3p, were obtained. Further, 36 nodes and 197 interactions in the PPI network were identified. Conclusion: The results of this study revealed candidate key genes, miRNAs, and pathways related to mast cells resting involved in meningioma development, providing potential therapeutic targets for meningioma treatment.

Identification of key biomarkers for thyroid cancer by integrative gene expression profiles

2021 ◽  
pp. 153537022110088
Author(s):  
Jinyi Tian ◽  
Yizhou Bai ◽  
Anyang Liu ◽  
Bin Luo
Keyword(s):  
Gene Expression ◽  
Thyroid Cancer ◽  
Differentially Expressed Genes ◽  
Molecular Mechanisms ◽  
Expression Profiles ◽  
Thyroid Tissue ◽  
Gene Signature ◽  
Gene Expression Omnibus ◽  
Differentially Expressed ◽  
Tissue Samples

Thyroid cancer is a frequently diagnosed malignancy and the incidence has been increased rapidly in recent years. Despite the favorable prognosis of most thyroid cancer patients, advanced patients with metastasis and recurrence still have poor prognosis. Therefore, the molecular mechanisms of progression and targeted biomarkers were investigated for developing effective targets for treating thyroid cancer. Eight chip datasets from the gene expression omnibus database were selected and the inSilicoDb and inSilicoMerging R/Bioconductor packages were used to integrate and normalize them across platforms. After merging the eight gene expression omnibus datasets, we obtained one dataset that contained the expression profiles of 319 samples (188 tumor samples plus 131 normal thyroid tissue samples). After screening, we identified 594 significantly differentially expressed genes (277 up-regulated genes plus 317 down-regulated genes) between the tumor and normal tissue samples. The differentially expressed genes exhibited enrichment in multiple signaling pathways, such as p53 signaling. By building a protein–protein interaction network and module analysis, we confirmed seven hub genes, and they were all differentially expressed at all the clinical stages of thyroid cancer. A diagnostic seven-gene signature was established using a logistic regression model with the area under the receiver operating characteristic curve (AUC) of 0.967. Seven robust candidate biomarkers predictive of thyroid cancer were identified, and the obtained seven-gene signature may serve as a useful marker for thyroid cancer diagnosis and prognosis.

scholarly journals Identification of key candidate genes and biological pathways in bladder cancer

PeerJ ◽  
2018 ◽  
Vol 6 ◽  
pp. e6036 ◽  
Author(s):  
Xin Gao ◽  
Yinyi Chen ◽  
Mei Chen ◽  
Shunlan Wang ◽  
Xiaohong Wen ◽  
...  
Keyword(s):  
Bladder Cancer ◽  
Pathway Analysis ◽  
Molecular Mechanisms ◽  
Kegg Pathway ◽  
Aurora Kinase ◽  
Smooth Muscle Contraction ◽  
Ppi Network ◽  
Tissue Samples ◽  
Kegg Pathway Analysis ◽  
Key Genes

Background Bladder cancer is a malignant tumor in the urinary system with high mortality and recurrence rates. However, the causes and recurrence mechanism of bladder cancer are not fully understood. In this study, we used integrated bioinformatics to screen for key genes associated with the development of bladder cancer and reveal their potential molecular mechanisms. Methods The GSE7476, GSE13507, GSE37815 and GSE65635 expression profiles were downloaded from the Gene Expression Omnibus database, and these datasets contain 304 tissue samples, including 81 normal bladder tissue samples and 223 bladder cancer samples. The RobustRankAggreg (RRA) method was utilized to integrate and analyze the four datasets to obtain integrated differentially expressed genes (DEGs), and the gene ontology (GO) functional annotation and Kyoto encyclopedia of genes and genomes (KEGG) pathway analysis were performed. Protein-protein interaction (PPI) network and module analyses were performed using Cytoscape software. The OncoLnc online tool was utilized to analyze the relationship between the expression of hub genes and the prognosis of bladder cancer. Results In total, 343 DEGs, including 111 upregulated and 232 downregulated genes, were identified from the four datasets. GO analysis showed that the upregulated genes were mainly involved in mitotic nuclear division, the spindle and protein binding. The downregulated genes were mainly involved in cell adhesion, extracellular exosomes and calcium ion binding. The top five enriched pathways obtained in the KEGG pathway analysis were focal adhesion (FA), PI3K-Akt signaling pathway, proteoglycans in cancer, extracellular matrix (ECM)-receptor interaction and vascular smooth muscle contraction. The top 10 hub genes identified from the PPI network were vascular endothelial growth factor A (VEGFA), TOP2A, CCNB1, Cell division cycle 20 (CDC20), aurora kinase B, ACTA2, Aurora kinase A, UBE2C, CEP55 and CCNB2. Survival analysis revealed that the expression levels of ACTA2, CCNB1, CDC20 and VEGFA were related to the prognosis of patients with bladder cancer. In addition, a KEGG pathway analysis of the top 2 modules identified from the PPI network revealed that Module 1 mainly involved the cell cycle and oocyte meiosis, while the analysis in Module 2 mainly involved the complement and coagulation cascades, vascular smooth muscle contraction and FA. Conclusions This study identified key genes and pathways in bladder cancer, which will improve our understanding of the molecular mechanisms underlying the development and progression of bladder cancer. These key genes might be potential therapeutic targets and biomarkers for the treatment of bladder cancer.

scholarly journals Comparative Gene Expression Analysis Reveals Similarities and Differences of Chronic Myeloid Leukemia Phases

Cancers ◽  
2022 ◽  
Vol 14 (1) ◽  
pp. 256
Author(s):  
Annemarie Schwarz ◽  
Ingo Roeder ◽  
Michael Seifert
Keyword(s):  
Gene Expression ◽  
Chronic Myeloid Leukemia ◽  
Signaling Pathway ◽  
Myeloid Leukemia ◽  
Expression Profiles ◽  
Expression Patterns ◽  
Chronic Phase ◽  
Receptor Interaction ◽  
Similarities And Differences

Chronic myeloid leukemia (CML) is a slowly progressing blood cancer that primarily affects elderly people. Without successful treatment, CML progressively develops from the chronic phase through the accelerated phase to the blast crisis, and ultimately to death. Nowadays, the availability of targeted tyrosine kinase inhibitor (TKI) therapies has led to long-term disease control for the vast majority of patients. Nevertheless, there are still patients that do not respond well enough to TKI therapies and available targeted therapies are also less efficient for patients in accelerated phase or blast crises. Thus, a more detailed characterization of molecular alterations that distinguish the different CML phases is still very important. We performed an in-depth bioinformatics analysis of publicly available gene expression profiles of the three CML phases. Pairwise comparisons revealed many differentially expressed genes that formed a characteristic gene expression signature, which clearly distinguished the three CML phases. Signaling pathway expression patterns were very similar between the three phases but differed strongly in the number of affected genes, which increased with the phase. Still, significant alterations of MAPK, VEGF, PI3K-Akt, adherens junction and cytokine receptor interaction signaling distinguished specific phases. Our study also suggests that one can consider the phase-wise CML development as a three rather than a two-step process. This is in accordance with the phase-specific expression behavior of 24 potential major regulators that we predicted by a network-based approach. Several of these genes are known to be involved in the accumulation of additional mutations, alterations of immune responses, deregulation of signaling pathways or may have an impact on treatment response and survival. Importantly, some of these genes have already been reported in relation to CML (e.g., AURKB, AZU1, HLA-B, HLA-DMB, PF4) and others have been found to play important roles in different leukemias (e.g., CDCA3, RPL18A, PRG3, TLX3). In addition, increased expression of BCL2 in the accelerated and blast phase indicates that venetoclax could be a potential treatment option. Moreover, a characteristic signaling pathway signature with increased expression of cytokine and ECM receptor interaction pathway genes distinguished imatinib-resistant patients from each individual CML phase. Overall, our comparative analysis contributes to an in-depth molecular characterization of similarities and differences of the CML phases and provides hints for the identification of patients that may not profit from an imatinib therapy, which could support the development of additional treatment strategies.

Identification of Potential Key Genes in Anaplastic Thyroid Cancer Using Bioinformatics AnalysisIdentification of Potential Key Genes in Anaplastic Thyroid Cancer using Bioinformatics Analysis

2021 ◽  
Author(s):  
Zhenzhen Li ◽  
Chaoliang Xiong ◽  
Jin Wei ◽  
Ping Chen ◽  
Yanping Zhang ◽  
...  
Keyword(s):  
Survival Analysis ◽  
Thyroid Cancer ◽  
Collagen Type ◽  
Expression Profiles ◽  
Thyroid Tissue ◽  
Anaplastic Thyroid Cancer ◽  
Type I ◽  
Tissue Samples ◽  
Key Genes ◽  
Km Plotter

Abstract BackgroundAnaplastic thyroid cancer (ATC) has a high degree of malignancy and a poor prognosis. Its incidence accounts for approximately 10-15% of all thyroid cancers. The purpose of this study was to determine the differentially expressed genes (DEGs) of ATC through biometric analysis technology, clarify the potential interactions between them, and screen genes related to the prognosis of ATC.MethodsThe GSE29265, GSE65144, GSE33630, and GSE85457 expression profiles downloaded from the Gene Expression Omnibus database (GEO) contained a total of 117 tissue samples (81 normal thyroid tissue samples and 36 ATC samples). The four datasets were integrated and analyzed by the limma packages to obtain DEGs. With these DEGs, we performed gene ontology functional annotation and Kyoto Encyclopedia of Genes and Genomes pathway analyses using the Database for Annotation, Visualization and Integrated Discovery, protein-protein interaction (PPI) analysis using Cytoscape, and survival analysis using the Kaplan-Meier (KM) plotter.Results.After R integration analysis of the four datasets, 764 DEGs were obtained, i.e., 314 upregulated and 450 downregulated genes. Among the hub DEGs obtained in the PPI network, the expression levels of thymidylate synthase (TYMS), fibronectin 1, chordin-like 1, syndecan 2, integrin alpha 2, collagen type I alpha 1 chain, collagen type IX alpha 3 chain (COL9A3), and collagen type XXIII alpha 1 chain (COL23A1) were associated with ATC prognosis. These results showed that the overall survival and recurrence-free survival of TYMS, COL9A3, and COL23A1 were statistically significant in our KM plotter survival analysis; thus, these DEGs may be used as potential biomarkers of ATC.ConclusionThis study identified several potential target genes and pathways that may affect the development of ATC. These findings provide new insights for the detection of novel diagnostic and therapeutic biomarkers for ATC.

Microarray-Based Gene Expression Analysis Identifies Potential Diagnostic and Prognostic Biomarkers for Waldenström Macroglobulinemia

2018 ◽  
Vol 140 (2) ◽  
pp. 87-96
Author(s):  
Haitao Xu ◽  
Fusheng Yao
Keyword(s):  
Gene Expression ◽  
B Cell ◽  
Signaling Pathway ◽  
Molecular Mechanisms ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Mitogen Activated Protein Kinase ◽  
Ppi Network ◽  
Waldenström Macroglobulinemia ◽  
Waldenstrom Macroglobulinemia

Waldenström macroglobulinemia (WM), also known as lymphoplasmacytic lymphoma, is rare but a clinicopathologically distinct B-cell malignancy. This study assessed differentially expressed genes (DEGs) to identify potential WM biomarkers and uncover the underlying the molecular mechanisms of WM progression using gene expression profiles from the Gene Expression Omnibus database. DEGs were identified using the LIMMA package and their potential functions were then analyzed by using the gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses and the protein-protein interaction (PPI) network analysis by using the Search Tool for the Retrieval of Interacting Genes/Proteins database. Data showed that among 1,756 DEGs, 926 were upregulated and 830 were downregulated by comparing WM BM CD19+ with normal PB CD19+ B cell samples, whereas 241 DEGs (95 upregulated and 146 downregulated) were identified by comparing WM BM CD138+ with normal BM CD138+ plasma cell samples. The DEGs were enriched in different GO terms and pathways, including the apoptotic process, cell cycle arrest, immune response, cell adhesion, mitogen-activated protein kinase signaling pathway, toll-like receptor signaling pathway, and the gonadotropin-releasing hormone signaling pathway. Hub nodes in the PPI network included CDK1, JUN, CREBBP, EP300, CAD, CDK2, and MAPK14. Bioinformatics analysis of the GSE9656 dataset identified 7 hub genes that might play an important role in WM development and progression. Some of the candidate genes and pathways may serve as promising therapeutic targets for WM.

scholarly journals Network analyses of circular RNAs expression profiles and their hub genes in pancreatic ductal adenocarcinoma

2019 ◽  
Author(s):  
Yanyan Tang ◽  
Ping Zhang
Keyword(s):  
Gene Expression ◽  
Pancreatic Ductal Adenocarcinoma ◽  
Signaling Pathway ◽  
Pathway Analysis ◽  
Kegg Pathway ◽  
Expression Profiles ◽  
Ductal Adenocarcinoma ◽  
Ppi Network ◽  
Hub Genes ◽  
Kegg Pathway Analysis

Abstract Pancreatic ductal adenocarcinoma (PDAC) is one of the most common malignant tumor in digestive system. CircRNAs involve in lots of biological processes through interacting with miRNAs and their targeted mRNA. We obtained the circRNA gene expression profiles from Gene Expression Omnibus (GEO) and identified differentially expressed genes (DEGs) between PDAC samples and paracancerous tissues. Bioinformatics analyses, including GO analysis, KEGG pathway analysis and PPI network analysis, were conducted for further investigation. We also constructed circRNA‑microRNA-mRNA co-expression network. A total 291 differentially expressed circRNAs were screened out. The GO enrichment analysis revealed that up-regulated DEGs were mainly involved metabolic process, biological regulation, and gene expression, and down-regulated DEGs were involved in cell communication, single-organism process, and signal transduction. The KEGG pathway analysis, the upregulated circRNAs were enriched cGMP-PKG signaling pathway, and HTLV-I infection, while the downregulated circRNAs were enriched in protein processing in endoplasmic reticulum, insulin signaling pathway, regulation of actin cytoskeleton, etc. Four genes were identified from PPI network as both hub genes and module genes, and their circRNA‑miRNA-mRNA regulatory network also be constructed. Our study indicated possible involvement of dysregulated circRNAs in the development of PDAC and promoted our understanding of the underlying molecular mechanisms.

scholarly journals Meta-Analyses of Multiple Gene Expression Profiles to Screen Hub Genes Related to Osteoarthritis

2021 ◽  
Vol 24 (5-6) ◽  
pp. 267-279
Author(s):  
Xianyang Zhu ◽  
Wen Guo
Keyword(s):  
Gene Expression ◽  
Molecular Mechanisms ◽  
Expression Profiles ◽  
Meta Analysis ◽  
Wnt Signaling Pathway ◽  
Gene Expression Profiles ◽  
Enrichment Analysis ◽  
Receptor Interaction ◽  
Ppi Network ◽  
Hub Genes

<b><i>Background:</i></b> This study aimed to screen and validate the crucial genes involved in osteoarthritis (OA) and explore its potential molecular mechanisms. <b><i>Methods:</i></b> Four expression profile datasets related to OA were downloaded from the Gene Expression Omnibus (GEO). The differentially expressed genes (DEGs) from 4 microarray patterns were identified by the meta-analysis method. The weighted gene co-expression network analysis (WGCNA) method was used to investigate stable modules most related to OA. In addition, a protein-protein interaction (PPI) network was built to explore hub genes in OA. Moreover, OA-related genes and pathways were retrieved from Comparative Toxicogenomics Database (CTD). <b><i>Results:</i></b> A total of 1,136 DEGs were identified from 4 datasets. Based on these DEGs, WGCNA further explored 370 genes included in the 3 OA-related stable modules. A total of 10 hub genes were identified in the PPI network, including <i>AKT1</i>, <i>CDC42</i>, <i>HLA-DQA2</i>, <i>TUBB</i>, <i>TWISTNB</i>, <i>GSK3B</i>, <i>FZD2</i>, <i>KLC1</i>, <i>GUSB</i>, and <i>RHOG</i>. Besides, 5 pathways including “Lysosome,” “Pathways in cancer,” “Wnt signaling pathway,” “ECM-receptor interaction” and “Focal adhesion” in CTD and enrichment analysis and 5 OA-related hub genes (including <i>GSK3B, CDC42, AKT1, FZD2</i>, and <i>GUSB</i>) were identified. <b><i>Conclusion:</i></b> In this study, the meta-analysis was used to screen the central genes associated with OA in a variety of gene expression profiles. Three OA-related modules (green, turquoise, and yellow) containing 370 genes were identified through WGCNA. It was discovered through the gene-pathway network that <i>GSK3B, CDC42, AKT1, FZD2</i>, <i>and GUSB</i> may be key genes related to the progress of OA and may become promising therapeutic targets.

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