scholarly journals Gastric Cancer Associated Genes Identified by an Integrative Analysis of Gene Expression Data

2017 ◽  
Vol 2017 ◽  
pp. 1-7 ◽  
Author(s):  
Bing Jiang ◽  
Shuwen Li ◽  
Zhi Jiang ◽  
Ping Shao
Keyword(s):  
Gene Expression ◽  
Gastric Cancer ◽  
Gene Expression Data ◽  
Molecular Mechanisms ◽  
Integrative Analysis ◽  
Literature Mining ◽  
High Morbidity ◽  
Expression Data ◽  
Cancer Heterogeneity ◽  
Chemokine Ligand

Gastric cancer is one of the most severe complex diseases with high morbidity and mortality in the world. The molecular mechanisms and risk factors for this disease are still not clear since the cancer heterogeneity caused by different genetic and environmental factors. With more and more expression data accumulated nowadays, we can perform integrative analysis for these data to understand the complexity of gastric cancer and to identify consensus players for the heterogeneous cancer. In the present work, we screened the published gene expression data and analyzed them with integrative tool, combined with pathway and gene ontology enrichment investigation. We identified several consensus differentially expressed genes and these genes were further confirmed with literature mining; at last, two genes, that is, immunoglobulin J chain and C-X-C motif chemokine ligand 17, were screened as novel gastric cancer associated genes. Experimental validation is proposed to further confirm this finding.

Insights into Responses to Extreme Environmental Conditions in Humans from Studies on Saccharomyces cerevisiae

2015 ◽  
Vol 65 (6) ◽  
pp. 444
Author(s):  
Ramesh C. Meena ◽  
Amitabha Chakrabarti
Keyword(s):  
Gene Expression ◽  
Saccharomyces Cerevisiae ◽  
Stress Response ◽  
Gene Expression Data ◽  
Molecular Mechanisms ◽  
Multiple Stressors ◽  
Expression Data ◽  
Environmental Stress Response ◽  
Yeast Saccharomyces Cerevisiae ◽  
Pathways Analysis

<p>The versatility of the yeast experimental model has aided in innumerable ways in the understanding of fundamental cellular functions and has also contributed towards the elucidation of molecular mechanisms underlying several pathological conditions in humans. Genome-wide expression, functional, localization and interaction studies on the yeast Saccharomyces cerevisiae exposed to various stressors have made profound contributions towards the understanding of stress response pathways. Analysis of gene expression data from S. cerevisiae cells indicate that the expression of a common set of genes is altered upon exposure to all the stress conditions examined. This common response to multiple stressors is known as the Environmental stress response. Knowledge gained from studies on the yeast model has now become helpful in understanding stress response pathways and associated disease conditions in humans. Cross-species microarray experiments and analysis of data with ever improving computational methods has led to a better comparison of gene expression data between diverse organisms that include yeast and humans.</p>

Identification of osteosarcoma driver genes by integrative analysis of copy number and gene expression data

2012 ◽  
Vol 51 (7) ◽  
pp. 696-706 ◽  
Author(s):  
Marieke L. Kuijjer ◽  
Halfdan Rydbeck ◽  
Stine H. Kresse ◽  
Emilie P. Buddingh ◽  
Ana B. Lid ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Copy Number ◽  
Integrative Analysis ◽  
Expression Data ◽  
Driver Genes

Prediction of disease-free survival in gastric cancer using gene expression data

2003 ◽  
Vol 124 (4) ◽  
pp. A554-A555
Author(s):  
Alex Boussioutas ◽  
Ryan Van Laar ◽  
Paul Desmond ◽  
David Bowtell
Keyword(s):  
Gene Expression ◽  
Gastric Cancer ◽  
Gene Expression Data ◽  
Disease Free Survival ◽  
Expression Data ◽  
Free Survival ◽  
Disease Free

scholarly journals Chip-seq and gene expression data for the identification of functional sub-pathways: a proof of concept in lung cancer

2020 ◽  
Author(s):  
Xanthoula Atsalaki ◽  
Lefteris Koumakis ◽  
George Potamias ◽  
Manolis Tsiknakis
Keyword(s):  
Gene Expression ◽  
Lung Cancer ◽  
Systems Biology ◽  
Gene Expression Data ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Molecular Mechanisms ◽  
Integrative Approach ◽  
Expression Data ◽  
Gene Regulatory

AbstractHigh-throughput technologies, such as chromatin immunoprecipitation (ChIP) with massively parallel sequencing (ChIP-seq) have enabled cost and time efficient generation of immense amount of genome data. The advent of advanced sequencing techniques allowed biologists and bioinformaticians to investigate biological aspects of cell function and understand or reveal unexplored disease etiologies. Systems biology attempts to formulate the molecular mechanisms in mathematical models and one of the most important areas is the gene regulatory networks (GRNs), a collection of DNA segments that somehow interact with each other. GRNs incorporate valuable information about molecular targets that can be corellated to specific phenotype.In our study we highlight the need to develop new explorative tools and approaches for the integration of different types of -omics data such as ChIP-seq and GRNs using pathway analysis methodologies. We present an integrative approach for ChIP-seq and gene expression data on GRNs. Using public microarray expression samples for lung cancer and healthy subjects along with the KEGG human gene regulatory networks, we identified ways to disrupt functional sub-pathways on lung cancer with the aid of CTCF ChIP-seq data, as a proof of concept.We expect that such a systems biology pipeline could assist researchers to identify corellations and causality of transcription factors over functional or disrupted biological sub-pathways.

scholarly journals Discovery and Validation of Novel Methylation Markers in Helicobacter pylori-Associated Gastric Cancer

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Huan Wang ◽  
Nian-Shuang Li ◽  
Cong He ◽  
Chuan Xie ◽  
Yin Zhu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gastric Cancer ◽  
Dna Methylation ◽  
Helicobacter Pylori ◽  
Gene Expression Data ◽  
Functional Enrichment ◽  
Expression Data ◽  
H Pylori

Previous studies have shown that abnormal methylation is an early key event in the pathogenesis of most human cancers, contributing to the development of tumors. However, little attention has been given to the potential of DNA methylation patterns as markers for Helicobacter pylori- (H. pylori-) associated gastric cancer (GC). In this study, an integrated analysis of DNA methylation and gene expression was conducted to identify some potential key epigenetic markers in H. pylori-associated GC. DNA methylation data of 28 H. pylori-positive and 168 H. pylori-negative GC samples were compared and analyzed. We also analyzed the gene expression data of 18 H. pylori-positive and 145 H. pylori-negative GC cases. Finally, the results were verified by in vitro and in vivo experiments. A total of 5609 differentially methylated regions associated with 2454 differentially methylated genes were identified. A total of 228 differentially expressed genes were identified from the gene expression data of H. pylori-positive and H. pylori-negative GC cases. The screened genes were analyzed for functional enrichment. Subsequently, we obtained 28 genes regulated by methylation through a Venn diagram, and we identified five genes (GSTO2, HUS1, INTS1, TMEM184A, and TMEM190) downregulated by hypermethylation. HUS1, GSTO2, and TMEM190 were expressed at lower levels in GC than in adjacent samples ( P < 0.05 ). Moreover, H. pylori infection decreased HUS1, GSTO2, and TMEM190 expression in vitro and in vivo. Our study identified HUS1, GSTO2, and TMEM190 as novel methylation markers for H. pylori-associated GC.

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