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.

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 Identification of potential hub genes associated with the pathogenesis and prognosis of hepatocellular carcinoma via integrated bioinformatics analysis

2020 ◽  
Vol 48 (7) ◽  
pp. 030006052091001
Author(s):  
Ziqi Meng ◽  
Jiarui Wu ◽  
Xinkui Liu ◽  
Wei Zhou ◽  
Mengwei Ni ◽  
...  
Keyword(s):  
Gene Expression ◽  
Hepatocellular Carcinoma ◽  
Interaction Network ◽  
Gene Expression Omnibus ◽  
Molecular Therapy ◽  
Integrated Analysis ◽  
Hub Genes ◽  
Protein Protein Interaction ◽  
Interactive Analysis ◽  
Kaplan Meier

Objective The objective was to identify potential hub genes associated with the pathogenesis and prognosis of hepatocellular carcinoma (HCC). Methods Gene expression profile datasets were downloaded from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) between HCC and normal samples were identified via an integrated analysis. A protein–protein interaction network was constructed and analyzed using the STRING database and Cytoscape software, and enrichment analyses were carried out through DAVID. Gene Expression Profiling Interactive Analysis and Kaplan–Meier plotter were used to determine expression and prognostic values of hub genes. Results We identified 11 hub genes ( CDK1, CCNB2, CDC20, CCNB1, TOP2A, CCNA2, MELK, PBK, TPX2, KIF20A, and AURKA) that might be closely related to the pathogenesis and prognosis of HCC. Enrichment analyses indicated that the DEGs were significantly enriched in metabolism-associated pathways, and hub genes and module 1 were highly associated with cell cycle pathway. Conclusions In this study, we identified key genes of HCC, which indicated directions for further research into diagnostic and prognostic biomarkers that could facilitate targeted molecular therapy for HCC.

Decoding Psoriasis: Integrated Bioinformatics Approach to Understand Hub Genes and Involved Pathways

2020 ◽  
Vol 26 (29) ◽  
pp. 3619-3630
Author(s):  
Saumya Choudhary ◽  
Dibyabhaba Pradhan ◽  
Noor S. Khan ◽  
Harpreet Singh ◽  
George Thomas ◽  
...  
Keyword(s):  
Gene Expression ◽  
Differentially Expressed Gene ◽  
Therapeutic Targets ◽  
Interaction Network ◽  
Gene Expression Omnibus ◽  
Differentially Expressed ◽  
Keratinocyte Differentiation ◽  
Hub Genes ◽  
Lesional Skin ◽  
Ncbi Gene Expression Omnibus

Background: Psoriasis is a chronic immune mediated skin disorder with global prevalence of 0.2- 11.4%. Despite rare mortality, the severity of the disease could be understood by the accompanying comorbidities, that has even led to psychological problems among several patients. The cause and the disease mechanism still remain elusive. Objective: To identify potential therapeutic targets and affecting pathways for better insight of the disease pathogenesis. Method: The gene expression profile GSE13355 and GSE14905 were retrieved from NCBI, Gene Expression Omnibus database. The GEO profiles were integrated and the DEGs of lesional and non-lesional psoriasis skin were identified using the affy package in R software. The Kyoto Encyclopaedia of Genes and Genomes pathways of the DEGs were analyzed using clusterProfiler. Cytoscape, V3.7.1 was utilized to construct protein interaction network and analyze the interactome map of candidate proteins encoded in DEGs. Functionally relevant clusters were detected through Cytohubba and MCODE. Results: A total of 1013 genes were differentially expressed in lesional skin of which 557 were upregulated and 456 were downregulated. Seven dysregulated genes were extracted in non-lesional skin. The disease gene network of these DEGs revealed 75 newly identified differentially expressed gene that might have a role in development and progression of the disease. GO analysis revealed keratinocyte differentiation and positive regulation of cytokine production to be the most enriched biological process and molecular function. Cytokines -cytokine receptor was the most enriched pathways. Among 1013 identified DEGs in lesional group, 36 DEGs were found to have altered genetic signature including IL1B and STAT3 which are also reported as hub genes. CCNB1, CCNA2, CDK1, IL1B, CXCL8, MKI 67, ESR1, UBE2C, STAT1 and STAT3 were top 10 hub gene. Conclusion: The hub genes, genomic altered DEGs and other newly identified differentially dysregulated genes would improve our understanding of psoriasis pathogenesis, moreover, the hub genes could be explored as potential therapeutic targets for psoriasis.

scholarly journals Association Between Depression and Breast Cancer: TNF/TNFRSF1β and LEP/LEPR Axis

2022 ◽  
Author(s):  
Jiaying Lin ◽  
Guangman Cui ◽  
Wenwei Jiang ◽  
Zhousheng Lin ◽  
Xinyue Lan ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Interaction Network ◽  
Epidemiological Studies ◽  
Gene Expression Omnibus ◽  
Inflammatory Factors ◽  
Biological Mechanism ◽  
Breast Cancer Patients ◽  
Protein Protein Interaction ◽  
Genes Encoding ◽  
Pathway Analyses

Abstract Depression contributes to enhanced initiation, development and metastasis of breast cancer. Despite epidemiological studies and experimental data suggest that depression and breast cancer may share a common biological mechanism, the results from these studies remain inconsistent. Here, we fully focus on the underlying biological mechanism behind the adverse effects of depression against breast cancer patients, and highlight the practical therapeutic intervention and improving quality of life. Publicly available datasets deposited in the Gene Expression Omnibus (GEO) were downloaded. Gene ontology and Kyoto Encyclopedia of Genes and Genomes pathway analyses of the differentially expressed genes (DEGs), which were extracted by using R tools, were performed. The protein-protein interaction network of the target DEGs was constructed using Cytoscape software and the hub genes were identified. In our study, we found that genes encoding proinflammatory cytokine, such as IL-1β and TNF, had significantly increased expression in depression. Following chronically stimulated by TNFα and IL-1β (usually for 14-18 days), inflammatory cancer-associated fibroblasts (CAFs) had elevated expression of inflammatory genes. Furthermore, the TNF/TNFRSF1β and LEP/LEPR regulatory axes were proven to be hub pathways of the crosstalk between depression and breast cancer. Our findings demonstrate that inflammatory factors are messengers linking depression and breast cancer, and provided further guidance in clinical medication.

scholarly journals Identification of key miRNA and hub genes in lung adenocarcinoma by bioinformatics and functional analysis

2020 ◽  
Author(s):  
Jiayao Zhu ◽  
Yan Zhang ◽  
Jingjing Lu ◽  
Le Wang ◽  
Xiaoren Zhu ◽  
...  
Keyword(s):  
Survival Analysis ◽  
Lung Adenocarcinoma ◽  
Target Gene ◽  
Target Genes ◽  
Gene Expression Omnibus ◽  
Differentially Expressed ◽  
Hub Genes ◽  
Protein Protein Interaction ◽  
Kaplan Meier ◽  
Differentially Expressed Mirnas

Abstract Background: lung adenocarcinoma is the main subtype of lung cancer and the most fatal malignant disease in the world. However, the pathogenesis of lung adenocarcinoma has not been fully elucidated.Methods: Three LUAD-associated datesets (GSE118370, GSE43767 and GSE74190) were downloaded from the Gene Expression Omnibus (GEO) datebase and the differentially expressed miRNAs (DEMs) and genes (DEGs) were screened by GEO2R. The prediction of target gene of differentially expressed miRNA were used miRWALK. Metascape was used to enrich the overlapped genes of DEGs and target genes. Then, the protein-protein interaction(PPI) and DEMs-DEGs regulatory network were created via String datebase and Cytoscape. Finally, overall survival analysis was established via the Kaplan–Meier curve and look for the possible prognostic biomarkers.Result: In this study, 433 differential genes were identified. There were 267 genes overlapped with the target gene of Dems, and eight hub genes (CDH1, CDH5, CAV1, MMP9, PECAM1, CD24, ENG, MME) were screened out. There were 85 different miRNAs in total, among which 16 miRNA target genes intersect with DEGs, 12 miRNAs with the highest interaction were screened out, and survival analysis of miRNA and hub genes was carried out.Conclusion: we found that miRNA-940, miRNA-125a-3p, miRNA-140-3p, miRNA-542-5p, CDH1, CDH5, CAV1, MMP9, PECAM1 may be related to the development of LUAD.

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 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.

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