scholarly journals Bioinformatics Analysis of C3 and CXCR4 act as Potential Prognostic Biomarkers in Clear Cell Renal Cell Carcinoma (ccRCC)

2021 ◽  
Author(s):  
Jing Quan ◽  
YUCHEN BAI ◽  
YunBei Yang ◽  
ErLei Han ◽  
Hong Bai ◽  
...  
Keyword(s):  
Candidate Genes ◽  
Interaction Network ◽  
Gene Expression Omnibus ◽  
High Expression ◽  
Individual Treatment ◽  
Hub Genes ◽  
Cell Renal Cell Carcinoma ◽  
Protein Protein Interaction ◽  
Microarray Datasets ◽  
Upregulated Genes

Abstract Background: The molecular pathogenesis of ccRCC was still unknown. Hence, the ccRCC-associated genes needs to explored.Methods: Three ccRCC expression microarray datasets (GSE14762, GSE66270 and GSE53757) downloaded from gene expression omnibus (GEO) database. The distinguish of expressed genes (DEGs) between ccRCC and normal tissue was discuss and explored. the function of our DEGs was analyzed by Gene Ontology (GO) ,Kyoto Encyclopedia of Genes and Genomes (KEGG) .Then the protein‑protein interaction network (PPI) was established in order to screen the hub genes. Then the expressions of hub genes were identified by oncomine database.The prognostic values of hub genes were analyzed by GEPIA database in ccRCC patients. Result: A total of 137 DREs were analyzed, which including 63 upregulated genes and 74 downregulated genes. According to our result,137 DREs were mainly enriched in 82 functional terms and 24 pathways. 14 highest-scoring genes were screened as hub gene in the PPI network which including 12 upregulated candidate genes and 2 downregulated candidate genes. The result reveals that patients with higher C3 expression related to poor OS, while patients with high expression of CTSS and TLR3 related to better OS. Patients with high C3 and CXCR4 expression had a poor DFS, while ccRCC patients with high expression of TLR3 had better DFS. At last, C3 and CXCR4 were selected to detect the prognosis of patients with ccRCC.Conclusion: The result identified the C3 and CXCR4 as candidate biomarkers and potential therapeutic targets in the molecular mechanism and individual treatment of ccRCC.

scholarly journals An in-silico method leads to recognition of hub genes and crucial pathways in survival of patients with breast cancer

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Sepideh Dashti ◽  
Mohammad Taheri ◽  
Soudeh Ghafouri-Fard
Keyword(s):  
Breast Cancer ◽  
Clinical Importance ◽  
Interaction Network ◽  
R Package ◽  
Gene Expression Omnibus ◽  
Hub Genes ◽  
Protein Protein Interaction ◽  
Ncbi Gene Expression Omnibus ◽  
Kaplan Meier ◽  
Microarray Datasets

Abstract Breast cancer is a highly heterogeneous disorder characterized by dysregulation of expression of numerous genes and cascades. In the current study, we aim to use a system biology strategy to identify key genes and signaling pathways in breast cancer. We have retrieved data of two microarray datasets (GSE65194 and GSE45827) from the NCBI Gene Expression Omnibus database. R package was used for identification of differentially expressed genes (DEGs), assessment of gene ontology and pathway enrichment evaluation. The DEGs were integrated to construct a protein–protein interaction network. Next, hub genes were recognized using the Cytoscape software and lncRNA–mRNA co-expression analysis was performed to evaluate the potential roles of lncRNAs. Finally, the clinical importance of the obtained genes was assessed using Kaplan–Meier survival analysis. In the present study, 887 DEGs including 730 upregulated and 157 downregulated DEGs were detected between breast cancer and normal samples. By combining the results of functional analysis, MCODE, CytoNCA and CytoHubba 2 hub genes including MAD2L1 and CCNB1 were selected. We also identified 12 lncRNAs with significant correlation with MAD2L1 and CCNB1 genes. According to The Kaplan–Meier plotter database MAD2L1, CCNA2, RAD51-AS1 and LINC01089 have the most prediction potential among all candidate hub genes. Our study offers a framework for recognition of mRNA–lncRNA network in breast cancer and detection of important pathways that could be used as therapeutic targets in this kind of cancer.

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>

scholarly journals Ten genes are considered as potential biomarkers for the diagnosis of dermatomyositis

PLoS ONE ◽  
2021 ◽  
Vol 16 (11) ◽  
pp. e0260511
Author(s):  
Lu Xiao ◽  
Wei Xiao ◽  
Shudian Lin
Keyword(s):  
Characteristic Curve ◽  
Area Under The Curve ◽  
Interaction Network ◽  
Enrichment Analysis ◽  
Gene Expression Omnibus ◽  
Type I ◽  
Hub Genes ◽  
Protein Protein Interaction ◽  
Healthy Control ◽  
Potential Biomarkers

Objective This study aimed to identify the biomarkers and mechanisms for dermatomyositis (DM) progression at the transcriptome level through a combination of microarray and bioinformatic analyses. Method Microarray datasets for skeletal muscle of DM and healthy control (HC) were downloaded from the Gene Expression Omnibus (GEO) database, and differentially expressed genes (DEGs) were identified by using GEO2R. Enrichment analyses were performed to understand the functions and enriched pathways of DEGs. A protein–protein interaction network was constructed to identify hub genes. The top 10 hub genes were validated by other GEO datasets. The diagnostic accuracy of the top 10 hub genes for DM was evaluated using the area under the curve of the receiver operating characteristic curve. Result A total of 63 DEGs were identified between 10 DM samples and 9 HC samples. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analysis indicated that DEGs are mostly enriched in response to virus, defense response to virus, and type I interferon signaling pathway. 10 hub genes and 3 gene cluster modules were identified by Cytoscape. The identified hub genes were verified by GSE1551 and GSE11971 datasets and proven to be potential biomarkers for the diagnosis of DM. Conclusion Our work identified 10 valuable genes as potential biomarkers for the diagnosis of DM and explored the potential underlying molecular mechanism of the disease.

scholarly journals Identification of Infertility-Associated Topologically Important Genes Using Weighted Co-expression Network Analysis

2021 ◽  
Vol 12 ◽  
Author(s):  
Jingni Wu ◽  
Xiaomeng Xia ◽  
Ye Hu ◽  
Xiaoling Fang ◽  
Sandra Orsulic
Keyword(s):  
Network Analysis ◽  
Topological Analysis ◽  
Interaction Network ◽  
Enrichment Analysis ◽  
Gene Expression Omnibus ◽  
Gene Set Enrichment Analysis ◽  
Messenger Rnas ◽  
Hub Genes ◽  
Protein Protein Interaction ◽  
Highly Correlated

Endometriosis has been associated with a high risk of infertility. However, the underlying molecular mechanism of infertility in endometriosis remains poorly understood. In our study, we aimed to discover topologically important genes related to infertility in endometriosis, based on the structure network mining. We used microarray data from the Gene Expression Omnibus (GEO) database to construct a weighted gene co-expression network for fertile and infertile women with endometriosis and to identify gene modules highly correlated with clinical features of infertility in endometriosis. Additionally, the protein–protein interaction network analysis was used to identify the potential 20 hub messenger RNAs (mRNAs) while the network topological analysis was used to identify nine candidate long non-coding RNAs (lncRNAs). Functional annotations of clinically significant modules and lncRNAs revealed that hub genes might be involved in infertility in endometriosis by regulating G protein-coupled receptor signaling (GPCR) activity. Gene Set Enrichment Analysis showed that the phospholipase C-activating GPCR signaling pathway is correlated with infertility in patients with endometriosis. Taken together, our analysis has identified 29 hub genes which might lead to infertility in endometriosis through the regulation of the GPCR network.

scholarly journals Identification of the Key Genes and Pathways in Esophageal Carcinoma

2016 ◽  
Vol 2016 ◽  
pp. 1-11 ◽  
Author(s):  
Peng Su ◽  
Shiwang Wen ◽  
Yuefeng Zhang ◽  
Yong Li ◽  
Yanzhao Xu ◽  
...  
Keyword(s):  
Cell Cycle ◽  
Esophageal Carcinoma ◽  
Gene Expression Omnibus ◽  
Expression Level ◽  
Ppi Network ◽  
Protein Protein Interaction ◽  
Go Enrichment ◽  
Key Genes ◽  
Microarray Datasets ◽  
Upregulated Genes

Objective. Esophageal carcinoma (EC) is a frequently common malignancy of gastrointestinal cancer in the world. This study aims to screen key genes and pathways in EC and elucidate the mechanism of it.Methods. 5 microarray datasets of EC were downloaded from Gene Expression Omnibus. Differentially expressed genes (DEGs) were screened by bioinformatics analysis. Gene Ontology (GO) enrichment, Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment, and protein-protein interaction (PPI) network construction were performed to obtain the biological roles of DEGs in EC. Quantitative real-time polymerase chain reaction (qRT-PCR) was used to verify the expression level of DEGs in EC.Results. A total of 1955 genes were filtered as DEGs in EC. The upregulated genes were significantly enriched in cell cycle and the downregulated genes significantly enriched in Endocytosis. PPI network displayed CDK4 and CCT3 were hub proteins in the network. The expression level of 8 dysregulated DEGs including CDK4, CCT3, THSD4, SIM2, MYBL2, CENPF, CDCA3, and CDKN3 was validated in EC compared to adjacent nontumor tissues and the results were matched with the microarray analysis.Conclusion. The significantly DEGs including CDK4, CCT3, THSD4, and SIM2 may play key roles in tumorigenesis and development of EC involved in cell cycle and Endocytosis.

scholarly journals Aberrantly methylated-differentially genes and pathways among Iranian patients with colorectal cancer

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Mahla Ghorbani ◽  
Marjan Azghandi ◽  
Mohammad Amin Kerachian
Keyword(s):  
Colorectal Cancer ◽  
Candidate Genes ◽  
Interaction Network ◽  
Gene Expression Omnibus ◽  
P Value ◽  
Gene Methylation ◽  
Protein Protein Interaction ◽  
Kegg Pathways ◽  
Differentially Methylated Genes ◽  
Methylation Profiling

Abstract Background Methylation plays an important role in colorectal cancer (CRC) pathogenesis. The goal of this study was to identify aberrantly differentially methylated genes (DMGs) and pathways through bioinformatics analysis among Iranian CRC patients using Methylation Next Generation Sequencing. Methods This study has integrated results of SureSelectXT Methyl-Seq Target with the potential key candidate genes and pathways in CRC. Six CRC and six samples of normal colon were integrated and deeply analyzed. In addition to this gene methylation profiling, several other gene methylation profiling datasets were obtained from Gene Expression Omnibus (GEO) and TCGA datasets. DMGs were sorted and candidate genes and enrichment pathways were analyzed. DMGs-associated protein–protein interaction network (PPI) was constructed based on the STRING online database. Results Totally, 320 genes were detected as common genes between our patients and selected GEO and TCGA datasets from the Agilent SureSelect analysis with selecting criteria of p-value < 0.05 and FC ≥ 1.5. DMGs were identified from hyper-DMGs PPI network complex and 10 KEGG pathways were identified. The most important modules were extracted from MCODE, as most of the corresponding genes were involved in cellular process and protein binding. Conclusions Hub genes including WNT2, SFRP2, ZNF726 and BMP2 were suggested as potentially diagnostic and therapeutic targets for CRC.

scholarly journals Hub biomarkers for the diagnosis and treatment of glioblastoma based on microarray technology

2021 ◽  
Vol 20 ◽  
pp. 153303382199036
Author(s):  
Kai Cui ◽  
Jin-hui Chen ◽  
Yang-fan Zou ◽  
Shu-yuan Zhang ◽  
Bing Wu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Interaction Network ◽  
Gene Expression Omnibus ◽  
Diagnosis And Treatment ◽  
Hub Genes ◽  
Protein Protein Interaction ◽  
Interactive Analysis ◽  
Public Data ◽  
The Brain ◽  
Protein Protein Interaction Network

Background: Glioblastoma (GBM) is the most common clinical intracranial malignancy worldwide, and the most common supratentorial tumor in adults. GBM mainly causes damage to the brain tissue, which can be fatal. This research explored potential gene targets for the diagnosis and treatment of GBM using bioinformatic technology. Methods: Public data from patients with GBM and controls were downloaded from the Gene Expression Omnibus database, and differentially expressed genes (DEGs) were identified by Gene Expression Profiling Interactive Analysis (GEPIA) and Gene Expression Omnibus 2R (GEO2R). Construction of the protein–protein interaction network and the identification of a significant module were performed. Subsequently, hub genes were identified, and their expression was examined and compared by real-time quantitative (RT-q)PCR between patients with GBM and controls. Results: GSE122498 (GPL570 platform), GSE104291 (GPL570 platform), GSE78703_DMSO (GPL15207 platform), and GSE78703_LXR (GPL15207 platform) datasets were obtained from the GEO. A total of 130 DEGs and 10 hub genes were identified by GEPIA and GEO2R between patients with GBM and controls. Of these, strong connections were identified in correlation analysis between CCNB1, CDC6, KIF23, and KIF20A. RT-qPCR showed that all 4 of these genes were expressed at significantly higher levels in patients with GBM compared with controls. Conclusions: The hub genes CCNB1, CDC6, KIF23, and KIF20A are potential biomarkers for the diagnosis and treatment of GBM.

scholarly journals FRI0584 SCREENING AND IDENTIFICATION OF BIOMARKERS IN MYOSITIS

2020 ◽  
Vol 79 (Suppl 1) ◽  
pp. 896-897
Author(s):  
W. Liu ◽  
X. Zhang
Keyword(s):  
Gene Expression ◽  
Interstitial Lung Disease ◽  
Lung Disease ◽  
Molecular Mechanisms ◽  
Interaction Network ◽  
Gene Expression Omnibus ◽  
Hub Genes ◽  
Protein Protein Interaction ◽  
Protein Protein Interaction Network ◽  
Geo Database

Background:Myositis, including dermatomyositis and polymyositis, is autoimmune disorders that is characterized by muscle degeneration in the proximal extremities, with the complications of weakness of muscles, interstitial lung disease and vascular lesions, even leading to death in an acute progressive process[1,2]. However, the molecular mechanisms of myositis are rarely understood.Objectives:Identify the candidate genes in myositis.Methods:Microarray datasets GSE128470, GSE48280 and GSE39454 were extracted from the Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) and function enrichment analyses were conducted. The protein-protein interaction network and the analyses of hub genes were performed with STRING and Cytoscape.Results:There were 98 DEGs, of which the function and pathways enrichment analyses showed defense response, immune response, response to virus, inflammatory response, response to wounding, cell adhesion, cell proliferation, cell death and macromolecule metabolic process. 20 hub genes were identified, of which 7 including IRF9 TRIM22 MX2 IFITM1 IFI6 IFI44 IFI44L had not been reported in the literature, related to the response to virus, immune response, transcription from RNA polymerase II promoter, cell apoptosis, cell death. The verification analysis about the 7 genes in GSE128314 showed significant differences in myositis.Conclusion:In conclusion, DEGs and hub genes identified in our study showed the potential molecular mechanisms in myositis, providing the helpful targets for diagnosis and clinical strategy of myositis.References:[1] Wu H, Geng D, Xu J. An approach to the development of interstitial lung disease in dermatomyositis: a study of 230 cases in China[J]. Journal of International Medical Research. 2013;41(2):493–501.[2] Fathi M, Dastmalchi M, Rasmussen E, Lundberg IE, Tornling G. Interstitial lung disease, a common manifestation of newly diagnosed polymyositis and dermatomyositis[J]. Annals of the Rheumatic Diseases. 2004;63(3):297–301.Figure 1.The protein-protein interaction network of 20 hub genesFigure 2.7 genes in GSE128314 showed significant differences in myositisAcknowledgments:The authors acknowledge the efforts of the Gene Expression Omnibus (GEO) database. The interpretation and reporting of these data are the sole responsibility of the authors.Disclosure of Interests:None declared

scholarly journals Bioinformatics analysis of high-throughput data to validate potential novel biomarkers and small molecule drugs for glioblastoma multiforme

2020 ◽  
Vol 48 (7) ◽  
pp. 030006052092454
Author(s):  
Fuwei Qi ◽  
Qing Li ◽  
Xiaojun Lu ◽  
Zhihua Chen
Keyword(s):  
Gene Expression ◽  
Glioblastoma Multiforme ◽  
Molecular Mechanisms ◽  
Interaction Network ◽  
Gene Expression Omnibus ◽  
Protein Protein Interaction ◽  
Diagnosis And Prognosis ◽  
Novel Biomarkers ◽  
Patient Prognosis ◽  
Microarray Datasets

Objective There have been no recent improvements in the glioblastoma multiforme (GBM) outcome, with median survival remaining 15 months. Consequently, the need to identify novel biomarkers for GBM diagnosis and prognosis, and to develop targeted therapies is high. This study aimed to establish biomarkers for GBM pathogenesis and prognosis. Methods In total, 220 overlapping differentially expressed genes (DEGs) were obtained by integrating four microarray datasets from the Gene Expression Omnibus database (GSE4290, GSE12657, GSE15824, and GSE68848). Then a 140-node protein–protein interaction network with 343 interactions was constructed. Results The immune response and cell adhesion molecules were the most significantly enriched functions and pathways, respectively, among DEGs. The designated hub genes ITGB5 and RGS4, which have a high degree of connectivity, were closely correlated with patient prognosis, and GEPIA database mining further confirmed their differential expression in GBM versus normal tissue. We also determined the 20 most appropriate small molecules that could potentially reverse GBM gene expression, Prestwick-1080 was the most promising and had the highest negative scores. Conclusions This study identified ITGB5 and RGS4 as potential biomarkers for GBM diagnosis and prognosis. Insights into molecular mechanisms governing GBM occurrence and progression will help identify alternative biomarkers for clinical practice.

scholarly journals MMP1 and MMP9 are potential prognostic biomarkers and targets for uveal melanoma

2021 ◽  
Author(s):  
Tianyu Wang ◽  
Yuanyuan Zhang ◽  
Jianhao Bai ◽  
Yawen Xue ◽  
Qing Peng
Keyword(s):  
Gene Expression ◽  
Uveal Melanoma ◽  
Protein Interaction ◽  
Disease Free Survival ◽  
Interaction Network ◽  
Gene Expression Omnibus ◽  
Receptor Interaction ◽  
Hub Genes ◽  
Protein Protein Interaction ◽  
Normal Tissues

Abstract Background: Uveal melanoma (UVM) is the leading cause of eye-related mortality worldwide. This study aimed to explore the expression and prognostic value of matrix metalloproteinases (MMPs) in UVM.Methods: Gene expression levels were obtained from the Gene Expression Omnibus (GEO) and Oncomine databases. Functional and pathway enrichment analyses were performed using the Metascape database. GeneMANIA was then applied to construct a protein-protein interaction network and identify the hub genes. Moreover, overall (OS) and disease-free survival (DFS) analysis for the hub genes was performed using the UALCAN and Gene Expression Profiling Interactive Analysis (GEPIA) online tool. Furthermore, TRRUST was used to predict the targets of the MMPs. Results: Our results revealed that the transcriptional levels of MMP1, MMP9, MMP10, MMP11, MMP13, MMP14, and MMP17 were upregulated in UVM tissues compared to normal tissues. A protein-protein interaction (PPI) network was constructed, and the top 50 hub genes were identified. The functions of MMPs and their neighboring proteins are mainly associated with ECM-receptor interaction, proteoglycans in cancer, the IL-17 signaling pathway, and microRNAs in cancer. Among the MMPs, MMP1/2/9/11/14/15/16/17/24 played significant roles in the progression of UVM from stage 3 to stage 4. We also found that the expression of MMP1, MMP 2, MMP 9, and MMP 16 was positively correlated with OS and DFS in patients with UVM. Additionally, 18 transcription factors associated with nine MMPs were identified.Conclusions: The results of this study may provide potential biomarkers and targets for UVM. However, further studies are required to confirm these results.

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