scholarly journals Identification of key genes and their microRNAs in glioma: Evidence from bioinformatic analysis

2020 ◽  
Author(s):  
Xichao Wen ◽  
Meijuan Fu ◽  
Wensong Wu ◽  
Zhaomu Zeng ◽  
Kebin Zheng
Keyword(s):  
Survival Analysis ◽  
Molecular Mechanisms ◽  
Process Analysis ◽  
Interaction Network ◽  
Tumor Grade ◽  
Bioinformatic Analysis ◽  
Sh3 Domain ◽  
Gene Expression Omnibus ◽  
Hub Genes ◽  
Condensed Chromosome

Abstract Background Glioma is one of the most common primary intracranial tumors. Although a lot of studies have been conducted to elucidate the pathogeny of glioma, the molecular mechanisms are still unclear because of its complex biological functions. Methods To identify the candidate genes in the carcinogenesis and progression of glioma, microarray datasets GSE4290, GSE122498 and GSE2223 were downloaded from Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) were identified, and performed function enrichment of DEGs by Gene ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses. The protein-protein interaction network (PPI) was constructed using STRING and Cytoscape to find hub genes. Survival analysis and GEPIA database was conducted to screen and validate critical genes. Analysis of miRNA and genetic alteration was used to explore and predict the molecule mechanism. Results A total of 150 DEGs were identified, consisting of 54 downregulated genes and 96 upregulated genes. The enriched functions and pathways of the DEGs include regulation of transportation, synaptic transmission and SH3 domain binding. Fifteen hub genes were identified and biological process analysis revealed that these genes were mainly enriched in SH3 domain binding, neuron projection terminus, mitotic nuclear division, condensed chromosome and affected the brain development. Survival analysis showed that VAMP2, PPP3CA, DLGAP5, KIF14, REPS2, CENPU, KNTC1 and SMC4, may be involved in the carcinogenesis, invasion or recurrence of glioma. These 8 hub genes, which were related miRNAs and genetic changes were commonly involved in the development of glioma, were closely associated with tumor grade. Conclusion DEGs and hub genes identified in the present study help us understand the molecular mechanisms of carcinogenesis and progression of glioma, and provide candidate targets for diagnosis and treatment of glioma.

scholarly journals Overexpression of CCNE1 confers a poorer prognostic in triple negative breast cancer identified by bioinformatic analysis

2020 ◽  
Author(s):  
Qianqian Yuan ◽  
Lewei Zheng ◽  
Yiqin Liao ◽  
Gaosong Wu
Keyword(s):  
Breast Cancer ◽  
Survival Analysis ◽  
Triple Negative Breast Cancer ◽  
Molecular Mechanisms ◽  
Triple Negative ◽  
Cancer Survival ◽  
Process Analysis ◽  
Interaction Network ◽  
Bioinformatic Analysis ◽  
Gene Expression Omnibus

Abstract Background. Triple-negative breast cancer (TNBC) is a major subtype of breast cancer. Due to the lack of effective therapeutic targets, the prognosis is poor. In order to find an effective target, despite many efforts, the molecular mechanisms of TNBC are still not well understood which remain to be a profound clinical challenge.Methods. To identify the candidate genes in the carcinogenesis and progression of TNBC, microarray datasets GSE36693 and GSE65216 were downloaded from Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) were identified, and functional and pathway enrichment analyses were performed using the Gene Ontology(GO) and Kyoto Encyclopedia of Genes and Genomes(KEGG) databases via DAVID. We constructed the protein-protein interaction network (PPI) and the performed the module analysis using STRING and Cytoscape. Then we reanalyzed the selected DEGs genes and the survival analysis was performed using cBioportal.Results. A total of 140 DEGs were identified, consisting of 69 upregulated genes and 71 downregulated genes. Three hub genes were up-regulated among the selected genes from PPI and biological process analysis uncovered the fact that these genes were mainly enriched in p53 pathway and the pathways in cancer. Survival analysis showed that only CCNE1 may be involved in the carcinogenesis, invasion or recurrence of TNBC. Conclusion. CCNE1 could confer a poorer prognostic in TNBC identified by bioinformatic analysis and play key roles in the progression of TNBC which may contribute potential targets for the diagnosis, treatment and prognosis assessment of TNBC.

scholarly journals Overexpression of CCNE1 confers a poorer prognosis in triple-negative breast cancer identified by bioinformatic analysis

2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Qianqian Yuan ◽  
Lewei Zheng ◽  
Yiqin Liao ◽  
Gaosong Wu
Keyword(s):  
Breast Cancer ◽  
Survival Analysis ◽  
Triple Negative Breast Cancer ◽  
Molecular Mechanisms ◽  
Triple Negative ◽  
Cancer Survival ◽  
Process Analysis ◽  
Interaction Network ◽  
Bioinformatic Analysis ◽  
Gene Expression Omnibus

Abstract Background Triple-negative breast cancer (TNBC) is a major subtype of breast cancer. Due to the lack of effective therapeutic targets, the prognosis is poor. In order to find an effective target, despite many efforts, the molecular mechanisms of TNBC are still not well understood which remain to be a profound clinical challenge. Methods To identify the candidate genes in the carcinogenesis and progression of TNBC, microarray datasets GSE36693 and GSE65216 were downloaded from the Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) were identified, and functional and pathway enrichment analyses were performed using the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases via DAVID. We constructed the protein-protein interaction network (PPI) and performed the module analysis using STRING and Cytoscape. Then, we reanalyzed the selected DEG genes, and the survival analysis was performed using cBioportal. Results A total of 140 DEGs were identified, consisting of 69 upregulated genes and 71 downregulated genes. Three hub genes were upregulated among the selected genes from PPI, and biological process analysis uncovered the fact that these genes were mainly enriched in p53 pathway and the pathways in cancer. Survival analysis showed that only CCNE1 may be involved in the carcinogenesis, invasion, or recurrence of TNBC. The expression levels of CCNE1 were significantly higher in TNBC cells than non-TNBC cells that were detected by qRT-PCR (P < 0.05). Conclusion CCNE1 could confer a poorer prognosis in TNBC identified by bioinformatic analysis and plays key roles in the progression of TNBC which may contribute potential targets for the diagnosis, treatment, and prognosis assessment of TNBC.

scholarly journals Overexpression of CCNE1 confers a poorer prognostic in triple negative breast cancer identified by bioinformatic analysis

2021 ◽  
Author(s):  
Qianqian Yuan ◽  
Lewei Zheng ◽  
Yiqin Liao ◽  
Gaosong Wu
Keyword(s):  
Breast Cancer ◽  
Survival Analysis ◽  
Triple Negative Breast Cancer ◽  
Molecular Mechanisms ◽  
Triple Negative ◽  
Cancer Survival ◽  
Process Analysis ◽  
Interaction Network ◽  
Bioinformatic Analysis ◽  
Gene Expression Omnibus

Abstract Background. Triple-negative breast cancer (TNBC) is a major subtype of breast cancer. Due to the lack of effective therapeutic targets, the prognosis is poor. In order to find an effective target, despite many efforts, the molecular mechanisms of TNBC are still not well understood which remain to be a profound clinical challenge.Methods. To identify the candidate genes in the carcinogenesis and progression of TNBC, microarray datasets GSE36693 and GSE65216 were downloaded from Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) were identified, and functional and pathway enrichment analyses were performed using the Gene Ontology(GO) and Kyoto Encyclopedia of Genes and Genomes(KEGG) databases via DAVID. We constructed the protein-protein interaction network (PPI) and the performed the module analysis using STRING and Cytoscape. Then we reanalyzed the selected DEGs genes and the survival analysis was performed using cBioportal.Results. A total of 140 DEGs were identified, consisting of 69 upregulated genes and 71 downregulated genes. Three hub genes were up-regulated among the selected genes from PPI and biological process analysis uncovered the fact that these genes were mainly enriched in p53 pathway and the pathways in cancer. Survival analysis showed that only CCNE1 may be involved in the carcinogenesis, invasion or recurrence of TNBC. Conclusion. CCNE1 could confer a poorer prognostic in TNBC identified by bioinformatic analysis and play key roles in the progression of TNBC which may contribute potential targets for the diagnosis, treatment and prognosis assessment of TNBC.

scholarly journals Identification of Potential Key Genes and Molecular Mechanisms of Medulloblastoma Based on Integrated Bioinformatics Approach

2022 ◽  
Vol 2022 ◽  
pp. 1-17
Author(s):  
Md. Rakibul Islam ◽  
Lway Faisal Abdulrazak ◽  
Mohammad Khursheed Alam ◽  
Bikash Kumar Paul ◽  
Kawsar Ahmed ◽  
...  
Keyword(s):  
Gene Expression ◽  
Molecular Mechanisms ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Interaction Network ◽  
Enrichment Analysis ◽  
Clinical Information ◽  
Gene Expression Omnibus ◽  
Hub Genes ◽  
New Treatments

Background. Medulloblastoma (MB) is the most occurring brain cancer that mostly happens in childhood age. This cancer starts in the cerebellum part of the brain. This study is designed to screen novel and significant biomarkers, which may perform as potential prognostic biomarkers and therapeutic targets in MB. Methods. A total of 103 MB-related samples from three gene expression profiles of GSE22139, GSE37418, and GSE86574 were downloaded from the Gene Expression Omnibus (GEO). Applying the limma package, all three datasets were analyzed, and 1065 mutual DEGs were identified including 408 overexpressed and 657 underexpressed with the minimum cut-off criteria of ∣ log   fold   change ∣ > 1 and P < 0.05 . The Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and WikiPathways enrichment analyses were executed to discover the internal functions of the mutual DEGs. The outcomes of enrichment analysis showed that the common DEGs were significantly connected with MB progression and development. The Search Tool for Retrieval of Interacting Genes (STRING) database was used to construct the interaction network, and the network was displayed using the Cytoscape tool and applying connectivity and stress value methods of cytoHubba plugin 35 hub genes were identified from the whole network. Results. Four key clusters were identified using the PEWCC 1.0 method. Additionally, the survival analysis of hub genes was brought out based on clinical information of 612 MB patients. This bioinformatics analysis may help to define the pathogenesis and originate new treatments for MB.

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 Bioinformatic Analysis for Influential Core Gene Identification and Prognostic Significance in Advanced Serous Ovarian Carcinoma

Medicina ◽  
2021 ◽  
Vol 57 (9) ◽  
pp. 933
Author(s):  
Changho Song ◽  
Kyoung-Bo Kim ◽  
Jae-Ho Lee ◽  
Shin Kim
Keyword(s):  
Gene Expression ◽  
Ovarian Cancer ◽  
Molecular Mechanisms ◽  
Prognostic Significance ◽  
Interaction Network ◽  
Bioinformatic Analysis ◽  
Tumor Stage ◽  
Core Gene ◽  
Gene Expression Omnibus ◽  
Serous Ovarian Cancer

Background and objectives: Ovarian cancer is one of the leading causes of death among women worldwide. Most newly diagnosed ovarian cancer patients are diagnosed in advanced stages of the disease. Despite various treatments, most patients with advanced stage ovarian cancer, including serous ovarian cancer (the most common subtype of ovarian cancer), experience recurrence, which is associated with extremely poor prognoses. In the present study, we aimed to identify core genes involved in ovarian cancer and their associated molecular mechanisms, as well as to investigate related clinicopathological implications in ovarian cancer. Materials and methods: Three gene expression cohorts (GSE14407, GSE36668, and GSE38666) were obtained from the Gene Expression Omnibus databases to explore potential therapeutic biomarkers for ovarian cancer. Nine up-regulated and six down-regulated genes were screened. Three publicly available gene expression datasets (GSE14407, GSE36668, and GSE38666) were analyzed. Results: A total of 14 differently expressed genes (DEGs) were identified, among which nine genes were upregulated (BIRC5, CDCA3, CENPF, KIF4A, NCAPG, RRM2, UBE2C, VEGFA, and NR2F6) and were found to be significantly enriched in cell cycle regulation by gene ontology analysis. Further protein–protein interaction network analysis revealed seven hub genes among these DEGs. Moreover, Kaplan–Meier survival analysis showed that a higher expression of CDCA3 and UBE2C was associated with poor overall patient survival regardless of tumor stage and a higher tumor histologic grade. Conclusion: Altogether, our study suggests that CDCA3 and UBE2C may be valuable biomarkers for predicting the outcome of patients with advanced serous ovarian cancer.

scholarly journals Bioinformatics analysis of microarray data to identify the candidate biomarkers of lung adenocarcinoma

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e7313 ◽  
Author(s):  
Tingting Guo ◽  
Hongtao Ma ◽  
Yubai Zhou
Keyword(s):  
Survival Analysis ◽  
Lung Adenocarcinoma ◽  
Molecular Mechanisms ◽  
Prion Diseases ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Gene Expression Omnibus ◽  
Functional Enrichment ◽  
Ppi Network ◽  
Hub Genes

Background Lung adenocarcinoma (LUAD) is the major subtype of lung cancer and the most lethal malignant disease worldwide. However, the molecular mechanisms underlying LUAD are not fully understood. Methods Four datasets (GSE118370, GSE85841, GSE43458 and GSE32863) were obtained from the gene expression omnibus (GEO). Identification of differentially expressed genes (DEGs) and functional enrichment analysis were performed using the limma and clusterProfiler packages, respectively. A protein–protein interaction (PPI) network was constructed via Search Tool for the Retrieval of Interacting Genes (STRING) database, and the module analysis was performed by Cytoscape. Then, overall survival analysis was performed using the Kaplan–Meier curve, and prognostic candidate biomarkers were further analyzed using the Oncomine database. Results Totally, 349 DEGs were identified, including 275 downregulated and 74 upregulated genes which were significantly enriched in the biological process of extracellular structure organization, leukocyte migration and response to peptide. The mainly enriched pathways were complement and coagulation cascades, malaria and prion diseases. By extracting key modules from the PPI network, 11 hub genes were screened out. Survival analysis showed that except VSIG4, other hub genes may be involved in the development of LUAD, in which MYH10, METTL7A, FCER1G and TMOD1 have not been reported previously to correlated with LUAD. Briefly, novel hub genes identified in this study will help to deepen our understanding of the molecular mechanisms of LUAD carcinogenesis and progression, and to discover candidate targets for early detection and treatment of LUAD.

scholarly journals Identification of Key Genes with Clinical Outcome in Benign Meningioma Using Bioinformatic Analysis

2020 ◽  
Author(s):  
Ming Cao ◽  
Chen Shen ◽  
Jie Zhu ◽  
YuHai Wang
Keyword(s):  
Molecular Mechanisms ◽  
Therapeutic Targets ◽  
Microarray Dataset ◽  
Bioinformatic Analysis ◽  
Gene Expression Omnibus ◽  
Hub Genes ◽  
Benign Meningioma ◽  
Protein Protein Interaction ◽  
Kegg Pathways ◽  
Key Genes

Abstract Background: Meningioma is the second most common type of brain neoplasms.However,the underlying molecular mechanisms are still not clear,and the main treatment is mainly surgery plus radiotherapy. Material and method: To explore the key genes in benign meningioma,we downloaded microarray dataset GSE43290 from Gene Expression Omnibus(GEO) database.The differential genes (DEGs) between benign meningioma and normal meninges were identified by GEO2R.The gene ontology (GO) and Kyoto Encyclopedia of Gene and Genomes (KEGG) pathway were performed by the Database for Annotation,Visualization and Integrated Discovery (DAVID).The protein-protein interaction (PPI) network and module analysis were performed and visualized by the Search Tool for the Retrieval of Interacting Gene database (STRING) and Cytoscape.The hub genes were evaluated by the Cytohubba and further explored by MCODE plugin of Cytoscape and Enrichr.The relationship between hub genes and clinical factors were further explored by GSE16581 through R software. Result: A total of 358 DEGs were identified,including 15 upregulated genes and 343 downregulated genes.The main enriched functions were extracellular matrix organization、inflammatory response、cell adhesion、extracellular space and integrin binding.The main KEGG pathways were Malaria and focal adhesion.Among these DEGs,5 overlapping genes(CXCL8、AGT、CXCL2、CXCL12、CXCR4) were selected as hub genes.CXCL2 and CXCL8 were correlated with age and tumor recurrence,which could be clinical therapeutic targets. Conclusion: This study indicates the key genes in benign meningioma which may help us understand the molecular mechanisms and provide the candidate therapeutic targets.

scholarly journals Identification of key genes in relapsed multiple myeloma by weighted gene co-expression network analysis

2021 ◽  
Author(s):  
Lele Cao¹ ◽  
Ye Meng¹ ◽  
Rui Huang ◽  
Xiaoxue Wang ◽  
Hui Qin
Keyword(s):  
Multiple Myeloma ◽  
Survival Analysis ◽  
Differentially Expressed Genes ◽  
Interaction Network ◽  
R Package ◽  
Gene Expression Omnibus ◽  
Differentially Expressed ◽  
Hub Genes ◽  
Hematologic Disorder ◽  
Key Genes

Abstract Background Multiple myeloma is a hematologic disorder of abnormal plasma cell proliferation. Although there are some agents with different mechanisms in the clinic, the treatment of multiple myeloma is still challenging for the reason that its recurrence and progression are common. Therefore, it is critical to determine novel biomarkers to improve the prognosis of patients. Methods Firstly, raw data of GSE82307 was collected from the Gene Expression Omnibus database. Secondly, the top 50% of most variant genes were employed to construct a gene co-expression network in the R.WGCNA algorithm, and module significance and module membership were utilized to identify hub modules and hub genes respectively. The gene ontology enrichment and Kyoto encyclopedia of genes and genomes pathway analysis were carried out to assess biological characteristics. Then a protein-protein interaction network was conducted based on the STRING website and Cytoscape software. Next, differentially expressed genes were analyzed using the limma R package. Finally, survival analysis was performed by Kaplan–Meier plotter to evaluate prognosis. Results 10826 genes were used to construct a co-expression network. In this network, the blue module was identified as a hub module in which 68 genes were identified as hub genes. Furthermore, 46 differentially expressed genes were screened in samples of GSE82307. Integrating hub genes and differentially expressed genes, we determined 14 key genes. Finally, survival analysis revealed that ten genes CDCA5, CEP55, HJURP, CDC20, FOXM1, RRM2, TTK, CENPE, SKA1, NUF2 were related to the relapse and prognosis of multiple myeloma. Conclusion Our study suggested that CDCA5, CEP55, HJURP, CDC20, FOXM1, RRM2, TTK, CENPE, SKA1, NUF2 may be potential biomarkers for predicting the relapse and prognosis of multiple myeloma.

Screening and identification of key biomarkers in alimentary tract cancers: A bioinformatic analysis

2020 ◽  
Vol 29 (2) ◽  
pp. 221-233
Author(s):  
Zeling Cai ◽  
Yi Wei ◽  
Shuai Chen ◽  
Yu Gong ◽  
Yue Fu ◽  
...  
Keyword(s):  
Molecular Mechanisms ◽  
Alimentary Tract ◽  
Bioinformatic Analysis ◽  
Gene Expression Omnibus ◽  
Hub Genes ◽  
Protein Protein Interaction ◽  
Cellular Processes ◽  
Screening And Identification ◽  
Gene Modules ◽  
Microarray Datasets

BACKGROUND: Alimentary tract cancers (ATCs) are the most malignant cancers in the world. Numerous studies have revealed the tumorigenesis, diagnosis and treatment of ATCs, but many mechanisms remain to be explored. METHODS: To identify the key genes of ATCs, microarray datasets of oesophageal cancer, gastric cancer and colorectal cancer were obtained from the Gene Expression Omnibus (GEO) database. In total, 207 differentially expressed genes (DEGs) were screened. KEGG and GO function enrichment analyses were conducted, and a protein-protein interaction (PPI) network was generated and gene modules analysis was performed using STRING and Cytoscape. RESULTS: Five hub genes were screened, and the associated biological processes indicated that these genes were mainly enriched in cellular processes, protein binding and metabolic processes. Clinical survival analysis showed that COL10A1 and KIF14 may be significantly associated with the tumorigenesis or pathology grade of ATCs. In addition, relative human ATC cell lines along with blood samples and tumour tissues of ATC patients were obtained. The data proved that high expression of COL10A1 and KIF14 was associated with tumorigenesis and could be detected in blood. CONCLUSION: In conclusion, the identification of hub genes in the present study helped us to elucidate the molecular mechanisms of tumorigenesis and identify potential diagnostic indicators and targeted treatment for ATCs.

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