scholarly journals Differentially Expressed Genes Reveal the Biomarkers and Molecular Mechanism of Osteonecrosis

2022 ◽  
Vol 2022 ◽  
pp. 1-8
Author(s):  
Huanzhi Ma ◽  
Wei Zhang ◽  
Jun Shi
Keyword(s):  
Differentially Expressed Genes ◽  
High Throughput Sequencing ◽  
System Development ◽  
Gene Expression Omnibus ◽  
Osteonecrosis Of The Jaw ◽  
Differentially Expressed ◽  
Ppi Network ◽  
Old Patients ◽  
Orthopedic Diseases ◽  
Upregulated Genes

Osteonecrosis is one of the most refractory orthopedic diseases, which seriously threatens the health of old patients. High-throughput sequencing (HTS) and microarray analysis have confirmed as an effective way for investigating the pathological mechanism of disease. In this study, GSE7716, GSE74089, and GSE123568 were obtained from Gene Expression Omnibus (GEO) database and used to identify differentially expressed genes (DEGs) by R language. Subsequently, the DEGs were analyzed with Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment. Moreover, the protein-protein interaction (PPI) network of DEGs was analyzed by STRING database and Cytoscape. The results showed that 318 downregulated genes and 58 upregulated genes were observed in GSE7116; 690 downregulated genes and 1148 upregulated genes were screened from 34183 genes in GSE74089. The DEGs involved in progression of osteonecrosis involved inflammation, immunological rejection, and bacterial infection-related pathways. The GO enrichment showed that osteonecrosis was related with extracellular matrix, external encapsulating structure organization, skeletal system development, immune response activity, cell apoptosis, mononuclear cell differentiation, and serine/threonine kinase activity. Moreover, PPI network showed that the progression of osteonecrosis of the femoral head was related with CCND1, CDH1, ESR1, SPP1, LOX, JUN, ITGA, ABL1, and VEGF, and osteonecrosis of the jaw is related with ACTB, CXCR4, PTPRC, IL1B, CXCL8, TNF, JUN, PTGS2, FOS, and RHOA. In conclusion, this study identified the hub factors and pathways which might play important roles in progression of osteonecrosis and could be used as potential biomarkers for diagnosis and treatment of osteonecrosis.

scholarly journals Differentially expressed genes SNRPC and PRPF38A are potential biomarkers candidates for osteosarcoma

2020 ◽  
Author(s):  
Sheng Chang ◽  
Yang Cao
Keyword(s):  
Signaling Pathway ◽  
Differentially Expressed Genes ◽  
Osteogenic Sarcoma ◽  
Enrichment Analysis ◽  
Gene Expression Omnibus ◽  
Differentially Expressed ◽  
Pathway Enrichment Analysis ◽  
Ppi Network ◽  
Protein Protein Interaction ◽  
Potential Biomarkers

Abstract Background: Osteosarcoma (osteogenic sarcoma, OS) is a primary cause of morbidity and mortality and is associated with poor prognosis in the field of orthopedic. Globally, rates of OS are highest among 15 to 25-year-old adolescent. However, the mechanism of gene regulation and signaling pathway is unknown. Material and Methods: GSE9508, including 34 OS samples and 5 non-malignant bone samples, was gained from Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) were picked out by GEO2R online R soft tool. Furthermore, the protein-protein interaction (PPI) network between the DEGs was molded utilizing STRING online software. Afterward, PPI network of DEGs was constructed. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of DEGs were carried out on DAVID online tool and visualized via cytoscape software. Subsequently, module analysis of PPI was performed by using MCODE app. What’s more, prognosis-related genes were screened by using online databases including GEPIA, UALCAN and cBioPortal databases. Results: Totally, 671 DEGs were picked out, including 501 up-regulated genes and 170 down-regulated genes. Moreover, 22 hub genes were identified to be significantly expressed in PPI network (16 up-regulated and 6 down-regulated). We found that spliceosome signaling pathway may provide a potential target in OS. Furthermore, on the basis of common crucial pathway, PRPF38A and SNRPC were closely associated with spliceosome. Conclusion: This study showed that SNRPC and PRPF38A are potential biomarkers candidates for osteosarcoma.

scholarly journals Identification of Hub Genes Associated with Lung Adenocarcinoma Based on Bioinformatics Analysis

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Shuaiqun Wang ◽  
Xiaoling Xu ◽  
Wei Kong
Keyword(s):  
Gene Expression ◽  
Lung Adenocarcinoma ◽  
Differentially Expressed Genes ◽  
Gene Expression Omnibus ◽  
Differentially Expressed ◽  
Ppi Network ◽  
Hub Genes ◽  
Protein Protein Interaction ◽  
Limma Package ◽  
Differential Genes

Lung adenocarcinoma (LUAD) is one of the malignant lung tumors. However, its pathology has not been fully understood. The purpose of this study is to identify the hub genes associated with LUAD by bioinformatics methods. Three gene expression datasets including GSE116959, GSE74706, and GSE85841 downloaded from the Gene Expression Omnibus (GEO) database were used in this study. The differentially expressed genes (DEGs) related to LUAD were screened by using the limma package. Gene Ontology (GO) and KEGG analysis of DEGs were carried out through the DAVID website. The protein-protein interaction (PPI) of differentially expressed genes was drawn by the STRING website, and the results were imported into Cytoscape for visualization. Then, the PPI network was analyzed by using MCODE, and the modules with a score greater than 5 were found by using cytoHubba. Finally, the GEPIA database and UALCAN database were used to verify and analyze the survival of hub genes. We identified 67 upregulated genes and 277 downregulated genes from three LUAD datasets. The results of GO analysis showed that the downregulated genes were significantly enriched in matrix adhesion and angiogenesis and upregulated differential genes were significantly enriched in cell adhesion and vascular development. KEGG pathway analysis showed that the differential genes of LUAD were significantly enriched in viral carcinogenesis and adhesion spots. The PPI network of differentially expressed genes consists of 269 nodes and 625 interactions. In addition, three modules with scores greater than 5 and seven hub genes, namely, MCM4, BIRC5, CDC20, CDC25C, FOXM1, GTSE1, and RFC4, playing an important role in the PPI network were screened out. In this study, we obtained the hub genes and pathways related to LUAD, revealing the molecular mechanism and pathogenesis of LUAD, which is helpful for the early detection of LUAD and provides a new idea for the treatment of LUAD.

The Screening and Identification of Key Biomarkers in Adrenocortical Carcinoma: Evidence from a Bioinformatics Analysis

2022 ◽  
Vol 12 (3) ◽  
pp. 523-532
Author(s):  
Xin Yan ◽  
Chunfeng Liang ◽  
Xinghuan Liang ◽  
Li Li ◽  
Zhenxing Huang ◽  
...  
Keyword(s):  
Cell Cycle ◽  
Gene Ontology ◽  
Protein Interaction ◽  
Adrenocortical Carcinoma ◽  
Gene Expression Omnibus ◽  
Heparin Binding ◽  
Ppi Network ◽  
Hub Genes ◽  
Protein Protein Interaction ◽  
Upregulated Genes

<sec> <title>Objective:</title> This study aimed to identify the potential key genes associated with the progression and prognosis of adrenocortical carcinoma (ACC). </sec> <sec> <title>Methods:</title> Differentially expressed genes (DEGs) in ACC cells and normal adrenocortical cells were assessed by microarray from the Gene Expression Omnibus database. The biological functions of the classified DEGs were examined by Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes pathway (KEGG) enrichment analyses and a protein–protein interaction (PPI) network was mapped using Cytoscape software. MCODE software was also used for the module analysis and then 4 algorithms of cytohubba software were used to screen hub genes. The overall survival (OS) examination of the hub genes was then performed by the ualcan online tool. </sec> <sec> <title>Results:</title> Two GSEs (GSE12368, GSE33371) were downloaded from GEO including 18 and 43 cases, respectively. One hundred and sixty-nine DEGs were identified, including 57 upregulated genes and 112 downregulated genes. The Gene Ontology (GO) analyses showed that the upregulated genes were significantly enriched in the mitotic cytokines is, nucleus and ATP binding, while the downregulated genes were involved in the positive regulation of cardiac muscle contraction, extracellular space, and heparin-binding (P < 0.05). The Kyoto Encyclopedia of Genes and Genomes pathway (KEGG) pathway examination showed significant pathways including the cell cycle and the complement and coagulation cascades. The protein– protein interaction (PPI) network consisted of 162 nodes and 847 edges, including mitotic nuclear division, cytoplasmic, protein kinase binding, and cell cycle. All 4 identified hub genes (FOXM1, UBE2C, KIF11, and NDC80) were associated with the prognosis of adrenocortical carcinoma (ACC) by survival analysis. </sec> <sec> <title>Conclusions:</title> The present study offered insights into the molecular mechanism of adrenocortical carcinoma (ACC) that may be beneficial in further analyses. </sec>

Integrated Bioinformatics and Machine Learning Algorithms Analyses Highlight Related Pathways and Genes Associated with Alzheimer's Disease

2021 ◽  
Vol 17 ◽  
Author(s):  
Hui Zhang ◽  
Qidong Liu ◽  
Xiaoru Sun ◽  
Yaru Xu ◽  
Yiling Fang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Network Analysis ◽  
Predictive Model ◽  
Differentially Expressed Genes ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Differentially Expressed ◽  
Diagnosis And Treatment ◽  
Ppi Network ◽  
Key Genes

Background: The pathophysiology of Alzheimer's disease (AD) is still not fully studied. Objective: This study aimed to explore the differently expressed key genes in AD and build a predictive model of diagnosis and treatment. Methods: Gene expression data of the entorhinal cortex of AD, asymptomatic AD, and control samples from the GEO database were analyzed to explore the relevant pathways and key genes in the progression of AD. Differentially expressed genes between AD and the other two groups in the module were selected to identify biological mechanisms in AD through KEGG and PPI network analysis in Metascape. Furthermore, genes with a high connectivity degree by PPI network analysis were selected to build a predictive model using different machine learning algorithms. Besides, model performance was tested with five-fold cross-validation to select the best fitting model. Results: A total of 20 co-expression gene clusters were identified after the network was constructed. Module 1 (in black) and module 2 (in royal blue) were most positively and negatively correlated with AD, respectively. Total 565 genes in module 1 and 215 genes in module 2, respectively, overlapped in two differentially expressed genes lists. They were enriched in the G protein-coupled receptor signaling pathway, immune-related processes, and so on. 11 genes were screened by using lasso logistic regression, and they were considered to play an important role in predicting AD samples. The model built by the support vector machine algorithm with 11 genes showed the best performance. Conclusion: This result shed light on the diagnosis and treatment of AD.

Comprehensive Analysis of Differentially Expressed Genes and Key Pathways in Neuropathic Pain by Spinal Nerve Ligation

2020 ◽  
Author(s):  
Chao Xu ◽  
HuiFang Li ◽  
YunPeng Zhang ◽  
TianYu Liu ◽  
Yi Feng
Keyword(s):  
Functional Analysis ◽  
Neuropathic Pain ◽  
Differentially Expressed Genes ◽  
Enrichment Analysis ◽  
Gene Expression Omnibus ◽  
Spinal Nerve ◽  
Spinal Nerve Ligation ◽  
Differentially Expressed ◽  
Hub Genes ◽  
Long Term Treatment

Abstract Background: Neuropathic pain can cause significant physical and economic burden to people, and there are no effective long-term treatment methods for this condition. We conducted a bioinformatics analysis of microarray data to identify related mechanisms to determine strategies for more effective treatments of neuropathic pain.Methods: GSE24982 and GSE63442 microarray datasets were extracted from the Gene Expression Omnibus (GEO) database to analyze transcriptome differences of neuropathic pain in the dorsal root ganglions caused by spinal nerve ligation. We filtered the differentially expressed genes (DEGs) in the two datasets and Webgestalt was applied to conduct GeneOntology (GO) functional analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis of the shared DEGs. String Database and Cytoscape software were used to construct the Protein-Protein Interaction (PPI) network to determine the hub genes, which were subsequently verified in the GSE30691 dataset. Finally, miRDB and miRWalk Databases were used to predict potential miRNA of the selected DEGs.Results: A total of 182 overlapped DEGs were found between GSE24982 and GSE63442 datasets. The GO functional analysis and KEGG enrichment analysis showed that the selected DEGs were mainly enriched in infection, transmembrane transport of ion channels, and synaptic transmission. Combining the results of PPI analysis and the verification of the GSE30691 dataset, we identified seven hub genes related to neuropathic pain (Atf3, Aif1, Ctss, Gfap, Scg2, Jun, and Vgf). Predicted miRNA targeting each selected hub genes were identified.Conclusion: Seven hub genes related to the pathogenesis of neuropathic pain and potential targeting miRNA were identified, expanding understanding of the mechanism of neuropathic pain and facilitating treatment development.

scholarly journals Identification of differentially expressed genes between the colon and ileum of patients with inflammatory bowel disease by gene co-expression analysis

2019 ◽  
Vol 48 (5) ◽  
pp. 030006051988726
Author(s):  
Yuting Zhang ◽  
Bo Shen ◽  
Liya Zhuge ◽  
Yong Xie
Keyword(s):  
Inflammatory Bowel Disease ◽  
Differentially Expressed Genes ◽  
Bowel Disease ◽  
Gene Expression Pattern ◽  
Functional Enrichment ◽  
Wilcoxon Test ◽  
Differentially Expressed ◽  
Ppi Network ◽  
Hub Genes ◽  
Inflammatory Bowel

Objective We aimed to identify differentially expressed genes (DEG) in patients with inflammatory bowel disease (IBD). Methods RNA-seq data were obtained from the Array Express database. DEG were identified using the edgeR package. A co-expression network was constructed and key modules with the highest correlation with IBD inflammatory sites were identified for analysis. The Cytoscape MCODE plugin was used to identify key sub-modules of the protein–protein interaction (PPI) network. The genes in the sub-modules were considered hub genes, and functional enrichment analysis was performed. Furthermore, we constructed a drug–gene interaction network. Finally, we visualized the hub gene expression pattern between the colon and ileum of IBD using the ggpubr package and analyzed it using the Wilcoxon test. Results DEG were identified between the colon and ileum of IBD patients. Based on the co-expression network, the green module had the highest correlation with IBD inflammatory sites. In total, 379 DEG in the green module were identified for the PPI network. Nineteen hub genes were differentially expressed between the colon and ileum. The drug–gene network identified these hub genes as potential drug targets. Conclusion Nineteen DEG were identified between the colon and ileum of IBD patients.

scholarly journals Identification of key genes, pathways and potential therapeutic agents for IgA nephropathy using an integrated bioinformatics analysis

2020 ◽  
Vol 21 (2) ◽  
pp. 147032032091963
Author(s):  
Xiaoxue Chen ◽  
Mindan Sun
Keyword(s):  
Differentially Expressed Genes ◽  
Immunoglobulin A ◽  
Therapeutic Targets ◽  
Gene Expression Omnibus ◽  
Functional Enrichment ◽  
Differentially Expressed ◽  
Signal Pathways ◽  
Immunoglobulin A Nephropathy ◽  
Hub Genes ◽  
Limma Package

Purpose: This study aims to identify immunoglobulin-A-nephropathy-related genes based on microarray data and to investigate novel potential gene targets for immunoglobulin-A-nephropathy treatment. Methods: Immunoglobulin-A-nephropathy chip data was obtained from the Gene Expression Omnibus database, which included 10 immunoglobulin-A-nephropathy and 22 normal samples. We used the limma package of R software to screen differentially expressed genes in immunoglobulin-A-nephropathy and normal glomerular compartment tissues. Functional enrichment (including cellular components, molecular functions, biological processes) and signal pathways were performed for the differentially expressed genes. The online analysis database (STRING) was used to construct the protein-protein interaction networks of differentially expressed genes, and Cytoscape software was used to identify the hub genes of the signal pathway. In addition, we used the Connectivity Map database to predict possible drugs for the treatment of immunoglobulin-A-nephropathy. Results: A total of 348 differentially expressed genes were screened including 107 up-regulated and 241 down-regulated genes. Functional analysis showed that up-regulated differentially expressed genes were mainly concentrated on leukocyte migration, and the down-regulated differentially expressed genes were significantly enriched in alpha-amino acid metabolic process. A total of six hub genes were obtained: JUN, C3AR1, FN1, AGT, FOS, and SUCNR1. The small-molecule drugs thapsigargin, ciclopirox and ikarugamycin were predicted therapeutic targets against immunoglobulin-A-nephropathy. Conclusion: Differentially expressed genes and hub genes can contribute to understanding the molecular mechanism of immunoglobulin-A-nephropathy and providing potential therapeutic targets and drugs for the diagnosis and treatment of immunoglobulin-A-nephropathy.

scholarly journals Transcriptome profiling of posterior kidney of brown trout, Salmo trutta, during proliferative kidney disease

2019 ◽  
Vol 12 (1) ◽  
Author(s):  
Arun Sudhagar ◽  
Reinhard Ertl ◽  
Gokhlesh Kumar ◽  
Mansour El-Matbouli
Keyword(s):  
Immune System ◽  
Kidney Disease ◽  
Differentially Expressed Genes ◽  
Brown Trout ◽  
Salmo Trutta ◽  
Differentially Expressed ◽  
Rna Seq ◽  
Proliferative Kidney Disease ◽  
Immune System Process ◽  
Upregulated Genes

Abstract Background Tetracapsuloides bryosalmonae is a myxozoan parasite which causes economically important and emerging proliferative kidney disease (PKD) in salmonids. Brown trout, Salmo trutta is a native fish species of Europe, which acts as asymptomatic carriers for T. bryosalmonae. There is only limited information on the molecular mechanism involved in the kidney of brown trout during T. bryosalmonae development. We employed RNA sequencing (RNA-seq) to investigate the global transcriptome changes in the posterior kidney of brown trout during T. bryosalmonae development. Methods Brown trout were exposed to the spores of T. bryosalmonae and posterior kidneys were collected from both exposed and unexposed control fish. cDNA libraries were prepared from the posterior kidney and sequenced. Bioinformatics analysis was performed using standard pipeline of quality control, reference mapping, differential expression analysis, gene ontology, and pathway analysis. Quantitative real time PCR was performed to validate the transcriptional regulation of differentially expressed genes, and their correlation with RNA-seq data was statistically analyzed. Results Transcriptome analysis identified 1169 differentially expressed genes in the posterior kidney of brown trout, out of which 864 genes (74%) were upregulated and 305 genes (26%) were downregulated. The upregulated genes were associated with the regulation of immune system process, vesicle-mediated transport, leucocyte activation, and transport, whereas the downregulated genes were associated with endopeptidase regulatory activity, phosphatidylcholine biosynthetic process, connective tissue development, and collagen catabolic process. Conclusion To our knowledge, this is the first RNA-seq based transcriptome study performed in the posterior kidney of brown trout during active T. bryosalmonae development. Most of the upregulated genes were associated with the immune system process, whereas the downregulated genes were associated with other metabolic functions. The findings of this study provide insights on the immune responses mounted by the brown trout on the developing parasite, and the host molecular machineries modulated by the parasite for its successful multiplication and release.

scholarly journals Bayesian Mixture Model Analysis for Detecting Differentially Expressed Genes

2008 ◽  
Vol 2008 ◽  
pp. 1-12 ◽  
Author(s):  
Zhenyu Jia ◽  
Shizhong Xu
Keyword(s):  
Mixture Model ◽  
Differentially Expressed Genes ◽  
Differential Expression Analysis ◽  
Differentially Expressed ◽  
Control Treatment ◽  
Microarray Gene Expression ◽  
Bayesian Mixture Model ◽  
Bayesian Mixture ◽  
Truncated Normal ◽  
Upregulated Genes

Control-treatment design is widely used in microarray gene expression experiments. The purpose of such a design is to detect genes that express differentially between the control and the treatment. Many statistical procedures have been developed to detect differentially expressed genes, but all have pros and cons and room is still open for improvement. In this study, we propose a Bayesian mixture model approach to classifying genes into one of three clusters, corresponding to clusters of downregulated, neutral, and upregulated genes, respectively. The Bayesian method is implemented via the Markov chain Monte Carlo (MCMC) algorithm. The cluster means of down- and upregulated genes are sampled from truncated normal distributions whereas the cluster mean of the neutral genes is set to zero. Using simulated data as well as data from a real microarray experiment, we demonstrate that the new method outperforms all methods commonly used in differential expression analysis.

scholarly journals Coiled-Coil Domain Containing 80 Suppresses Nonylphenol-Induced Colorectal Cancer Cell Proliferation by Inhibiting the Activation of ERK1/2

2021 ◽  
Vol 9 ◽  
Author(s):  
Jing Wang ◽  
Yuan-wei Zhang ◽  
Nian-jie Zhang ◽  
Shuo Yin ◽  
Du-ji Ruan ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Cell Proliferation ◽  
Cell Growth ◽  
Differentially Expressed Genes ◽  
Endocrine Disrupting Chemicals ◽  
Coiled Coil ◽  
Underlying Mechanism ◽  
Differentially Expressed ◽  
Coiled Coil Domain ◽  
Upregulated Genes

Recently, the effect of endocrine-disrupting chemicals on the cancer procession has been a concern. Nonylphenol (NP) is a common environmental estrogen that has been shown to enhance the proliferation of colorectal cancer (CRC) cells in our previous studies; however, the underlying mechanism remains unclear. In this study, we confirmed the increased concentration of NP in the serum of patients with CRC. RNA sequencing was used to explore the differentially expressed genes after NP exposure. We found 16 upregulated genes and 12 downregulated genes in COLO205 cells after NP treatment. Among these differentially expressed genes, we found that coiled-coil domain containing 80 (CCDC80) was downregulated by NP treatment and was associated with CRC progression. Further experiments revealed that the overexpression of CCDC80 significantly suppressed NP-induced cell proliferation and recovered the reduced cell apoptosis. Meanwhile, the overexpression of CCDC80 significantly inhibited the activation of ERK1/2 induced by NP treatment. ERK1/2 inhibitor (PD98059) treatment also suppressed NP-induced CRC cell growth, but the overexpression of CCDC80 did not enhance the effect of ERK1/2 inhibitor. Taken together, NP treatment significantly inhibited the expression of CCDC80, and the overexpression of CCDC80 suppressed NP-induced CRC cell growth by inhibiting the activation of ERK1/2. These results suggest that NP could induce CRC cell growth by influencing the expression of multiple genes. CCDC80 and ERK1/2 inhibitors may be suitable therapeutic targets in NP-related CRC progression.

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