Research on the factors that affecting the occurrence of gastric cancer based on NCBI gene expression Omnibus database

2020 ◽  
Author(s):  
Bowei Liao
Keyword(s):  
Gene Expression ◽  
Gastric Cancer ◽  
Gene Expression Omnibus ◽  
Gene Expression Omnibus Database ◽  
Ncbi Gene Expression Omnibus

scholarly journals COnTORT: COmprehensive Transcriptomic ORganizational Tool for Simultaneously Retrieving and Organizing Numerous Gene Expression Data Sets from the NCBI Gene Expression Omnibus Database

2020 ◽  
Vol 9 (25) ◽  
Author(s):  
Kevin S. Myers ◽  
Michael Place ◽  
Daniel R. Noguera ◽  
Timothy J. Donohue
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Gene Expression Omnibus ◽  
Data Sets ◽  
Expression Data ◽  
Gene Expression Omnibus Database ◽  
Ncbi Gene Expression Omnibus ◽  
Geo Database

ABSTRACT We introduce COnTORT (COmprehensive Transcriptomic ORganizational Tool), a publicly available program that retrieves all available gene expression data and associated metadata for an organism from the National Center for Biotechnology Information (NCBI) Gene Expression Omnibus (GEO) database. The data are compiled into text files that can be used for downstream bioinformatic applications.

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 High Expression of TTYH3 is Related to Poor Clinical Outcomes in Human Gastric Cancer

2019 ◽  
Vol 8 (11) ◽  
pp. 1762 ◽  
Author(s):  
Saha ◽  
Biswas ◽  
Gil ◽  
Cho
Keyword(s):  
Gene Expression ◽  
Gastric Cancer ◽  
Cancer Progression ◽  
Survival Data ◽  
Chloride Channels ◽  
Patient Survival ◽  
Chloride Ion ◽  
Gene Expression Omnibus ◽  
Human Gastric Cancer ◽  
Normal Tissues

Ion channels play important roles in regulating various cellular processes and malignant transformation. Expressions of some chloride channels have been suggested to be associated with patient survival in gastric cancer (GC). However, little is known about the expression and function of TTYH3, a gene encoding a chloride ion channel, in cancer progression. Here, we comprehensively analyzed the expression of TTYH3 and its clinical outcome in GC using publicly available cancer gene expression and patient survival data through various databases. We examined the differences of TTYH3 expression between cancers and their normal tissues using the Oncomine, UALCAN, and GEO (Gene Expression Omnibus) databases. TTYH3 expression was investigated from immunohistochemistry images using the Human Protein Atlas database. Copy number alterations and mutations of TTYH3 were analyzed using cBioPortal. The co-expression profile of TTYH3 in GC was revealed using Oncomine. The gene ontology and pathway analyses were done using those co-expressed genes via the Enrichr tool to explore the predicted signaling pathways in GC. TTYH3 mRNA and protein levels in GC were significantly greater than those in normal tissue. Kaplan–Meier analysis revealed the upregulation of TTYH3 expression, which was significantly correlated with worse patient survival. Collectively, our data suggest that TTYH3 might be a potential prognostic marker for GC patients.

Discovery of Key Genes in Dermatomyositis Based on the Gene Expression Omnibus Database

2018 ◽  
Vol 37 (12) ◽  
pp. 982-992 ◽  
Author(s):  
Shuoshan Xie ◽  
Hui Luo ◽  
Huali Zhang ◽  
Honglin Zhu ◽  
Xiaoxia Zuo ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gene Expression Omnibus ◽  
Gene Expression Omnibus Database ◽  
Key Genes

Identifying novel biomarkers of gastric cancer through integration analysis of single nucleotide polymorphisms and gene expression profile

2015 ◽  
Vol 30 (3) ◽  
pp. 321-326 ◽  
Author(s):  
Tao Wang ◽  
Yan Xu ◽  
Peng Hou
Keyword(s):  
Gene Expression ◽  
Gastric Cancer ◽  
Transcription Factor ◽  
Single Nucleotide Polymorphisms ◽  
Gene Expression Omnibus ◽  
Trend Test ◽  
Nucleotide Polymorphisms ◽  
Functional Variation ◽  
Single Nucleotide ◽  
Normal Controls

Purpose Single nucleotide polymorphisms (SNPs) are an important cause of functional variation in proteins leading to tumorigenesis. We aimed to identify candidate biomarkers with polymorphisms in gastric cancer (GC). Methods The SNP microarray profile GSE29996 including 50 GC samples and 50 normal controls, and gene expression data GSE56807 consisting of 5 GC samples and 5 controls were downloaded from the Gene Expression Omnibus database. After preprocessing of raw data, GC-associated SNPs were identified using the Cochran-Armitage trend test, and differentially expressed genes (DEGs) were screened out using the limma package in R. Significant DEGs with risk associated SNP loci were screened using the Fisher combination test. Gene ontology function and pathway enrichment analyses were performed for DEGs with risk associated SNP loci by GenCLip online tool. Transcriptional regulatory analysis was also conducted for transcription factor and target DEGs. Results A total of 79 DEGs with risk associated SNP loci were identified from GC samples compared with normal controls. These DEGs were mainly enriched in anatomical structure development, including embryo development. Additionally, DEGs were significantly involved in the NO1 pathway, including actin, alpha 1, skeletal muscle (ACTA1). In the regulatory network, transcription factor forkhead box L1 (FOXL1) regulated 26 DEGs with risk associated SNP loci, including Iroquois homeobox 1 (IRX1) rs11134044, sex determining region Y (SRY)-box1 (SOX1) rs9549447 and msh homeobox 1 (MSX1) rs41451149. Conclusions IRX1, SOX1 and MSX1 with risk associated SNP loci may serve as candidate biomarkers for diagnosis and prognosis of GC.

scholarly journals GEDI: an R package for integration of transcriptomic data from multiple high-throughput platforms

2021 ◽  
Author(s):  
Mathias N Stokholm ◽  
Maria B Rabaglino ◽  
Haja N Kadarmideen
Keyword(s):  
Gene Expression ◽  
Data Integration ◽  
Principal Component ◽  
R Package ◽  
Gene Expression Omnibus ◽  
Batch Effect ◽  
Sequencing Data ◽  
Transcriptomic Data ◽  
Ncbi Gene Expression Omnibus ◽  
Forward Stepwise

Transcriptomic data is often expensive and difficult to generate in large cohorts in comparison to genomic data and therefore is often important to integrate multiple transcriptomic datasets from both microarray and next generation sequencing (NGS) based transcriptomic data across similar experiments or clinical trials to improve analytical power and discovery of novel transcripts and genes. However, transcriptomic data integration presents a few challenges including re-annotation and batch effect removal. We developed the Gene Expression Data Integration (GEDI) R package to enable transcriptomic data integration by combining already existing R packages. With just four functions, the GEDI R package makes constructing a transcriptomic data integration pipeline straightforward. Together, the functions overcome the complications in transcriptomic data integration by automatically re-annotating the data and removing the batch effect. The removal of the batch effect is verified with Principal Component Analysis and the data integration is verified using a logistic regression model with forward stepwise feature selection. To demonstrate the functionalities of the GEDI package, we integrated five bovine endometrial transcriptomic datasets from the NCBI Gene Expression Omnibus. The datasets included Affymetrix, Agilent and RNA-sequencing data. Furthermore, we compared the GEDI package to already existing tools and found that GEDI is the only tool that provides a full transcriptomic data integration pipeline including verification of both batch effect removal and data integration.

scholarly journals Molecular Subtypes and CD4+ Memory T Cell-Based Signature Associated With Clinical Outcomes in Gastric Cancer

2021 ◽  
Vol 10 ◽  
Author(s):  
Zhi-Kun Ning ◽  
Ce-Gui Hu ◽  
Chao Huang ◽  
Jiang Liu ◽  
Tai-Cheng Zhou ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gastric Cancer ◽  
T Cells ◽  
T Cell ◽  
Memory T Cells ◽  
Risk Groups ◽  
Gene Expression Omnibus ◽  
Systematic Analysis ◽  
Memory T Cell ◽  
Cox Analysis

BackgroundCD4+ memory T cells are an important component of the tumor microenvironment (TME) and affect tumor occurrence and progression. Nevertheless, there has been no systematic analysis of the effect of CD4+ memory T cells in gastric cancer (GC).MethodsThree datasets obtained from microarray and the corresponding clinical data of GC patients were retrieved and downloaded from the Gene Expression Omnibus (GEO) database. We uploaded the normalize gene expression data with standard annotation to the CIBERSORT web portal for evaluating the proportion of immune cells in the GC samples. The WGCNA was performed to identify the modules the CD4+ memory T cell related module (CD4+ MTRM) which was most significantly associated with CD4+ memory T cell. Univariate Cox analysis was used to screen prognostic CD4+ memory T cell-related genes (CD4+ MTRGs) in CD4+ MTRM. LASSO analysis and multivariate Cox analysis were then performed to construct a prognostic gene signature whose effect was evaluated by Kaplan-Meier curves and receiver operating characteristic (ROC), Harrell’s concordance index (C-index), and decision curve analyses (DCA). A prognostic nomogram was finally established based on the CD4+ MTRGs.ResultWe observed that a high abundance of CD4+ memory T cells was associated with better survival in GC patients. CD4+ MTRM was used to stratify GC patients into three clusters by unsupervised clustering analysis and ten CD4+ MTRGs were identified. Overall survival, five immune checkpoint genes and 17 types of immunocytes were observed to be significantly different among the three clusters. A ten-CD4+ MTRG signature was constructed to predict GC patient prognosis. The ten-CD4+ MTRG signature could divide GC patients into high- and low-risk groups with distinct OS rates. Multivariate Cox analysis suggested that the ten-CD4+ MTRG signature was an independent risk factor in GC. A nomogram incorporating this signature and clinical variables was established, and the C-index was 0.73 (95% CI: 0.697–0.763). Calibration curves and DCA presented high credibility for the OS nomogram.ConclusionWe identified three molecule subtypes, ten CD4+ MTRGs, and generated a prognostic nomogram that reliably predicts OS in GC. These findings have implications for precise prognosis prediction and individualized targeted therapy.

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