scholarly journals Variability in statin-induced changes in gene expression profiles of pancreatic cancer

2017 ◽  
Vol 7 (1) ◽  
Author(s):  
Helena Gbelcová ◽  
Silvie Rimpelová ◽  
Tomáš Ruml ◽  
Marie Fenclová ◽  
Vítek Kosek ◽  
...  
Keyword(s):  
Gene Expression ◽  
Pancreatic Cancer ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Induced Changes

scholarly journals Age and Vitamin E-Induced Changes in Gene Expression Profiles of T Cells

2006 ◽  
Vol 177 (9) ◽  
pp. 6052-6061 ◽  
Author(s):  
Sung Nim Han ◽  
Oskar Adolfsson ◽  
Cheol-Koo Lee ◽  
Tomas A. Prolla ◽  
Jose Ordovas ◽  
...  
Keyword(s):  
Gene Expression ◽  
T Cells ◽  
Vitamin E ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Induced Changes

scholarly journals Gene expression profiles of pancreatic cancer and stromal desmoplasia

Oncogene ◽  
2001 ◽  
Vol 20 (50) ◽  
pp. 7437-7446 ◽  
Author(s):  
Tatjana Crnogorac-Jurcevic ◽  
Evangelis Efthimiou ◽  
Paola Capelli ◽  
Ekaterina Blaveri ◽  
Antonella Baron ◽  
...  
Keyword(s):  
Gene Expression ◽  
Pancreatic Cancer ◽  
Expression Profiles ◽  
Gene Expression Profiles

scholarly journals Identify key genes and pathways in pancreatic ductal adenocarcinoma via bioinformatics analysis

2020 ◽  
Author(s):  
Huatian Luo ◽  
Da-qiu Chen ◽  
Jing-jing Pan ◽  
Zhang-wei Wu ◽  
Can Yang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Pancreatic Cancer ◽  
Pancreatic Ductal Adenocarcinoma ◽  
Bioinformatics Analysis ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Gene Expression Omnibus ◽  
Ductal Adenocarcinoma ◽  
Hub Genes ◽  
Expression Levels

Abstract Background: Pancreatic cancer has many pathologic types, among which pancreatic ductal adenocarcinoma (PDAC) is the most common one. Bioinformatics has become a very common tool for the selection of potentially pathogenic genes. Methods: Three data sets containing the gene expression profiles of PDAC were downloaded from the gene expression omnibus (GEO) database. The limma package of R language was utilized to explore the differentially expressed genes (DEGs). To analyze functions and signaling pathways, the Database Visualization and Integrated Discovery (DAVID) was used. To visualize the protein-protein interaction (PPI) of the DEGs ,Cytoscape was performed under the utilization of Search Tool for the Retrieval of Interacting Genes (STRING). With the usage of the plug-in cytoHubba in cytoscape software, the hub genes were found out. To verify the expression levels of hub genes, Gene Expression Profiling Interactive Analysis (GEPIA) was performed. Last but not least, UALCAN analysis online tool was implemented to analyze the overall survival. Results: The 376 DEGs were highly enriched in biological processes including signal transduction, apoptotic process and several pathways, mainly associated with Protein digestion and absorption and Pancreatic secretion pathway. The expression levels of nucleolar and spindle associated protein 1 (NUSAP1) and SHC binding and spindle associated 1 (SHCBP1) were discovered highly expressed in pancreatic ductal adenocarcinoma tissues. NUSAP1 and SHCBP1 had a high correlation with prognosis. Conclusions: The findings of this bioinformatics analysis indicate that NUSAP1 and SHCBP1 may be key factors in the prognosis and treatment of pancreatic cancer.

454 pyrosequencing-based analysis of gene expression profiles in the amphipod Melita plumulosa: Transcriptome assembly and toxicant induced changes

2014 ◽  
Vol 153 ◽  
pp. 73-88 ◽  
Author(s):  
Sharon E. Hook ◽  
Natalie A. Twine ◽  
Stuart L. Simpson ◽  
David A. Spadaro ◽  
Philippe Moncuquet ◽  
...  
Keyword(s):  
Gene Expression ◽  
454 Pyrosequencing ◽  
Transcriptome Assembly ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Induced Changes

scholarly journals Gene expression profiling of the effects of organic dust in lung epithelial and THP-1 cells reveals inductive effects on inflammatory and immune response genes

2016 ◽  
Vol 48 (4) ◽  
pp. 281-289 ◽  
Author(s):  
Vijay Boggaram ◽  
David S. Loose ◽  
Koteswara R. Gottipati ◽  
Kartiga Natarajan ◽  
Courtney T. Mitchell
Keyword(s):  
Gene Expression ◽  
Inflammatory Responses ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Organic Dust ◽  
Monocytic Cells ◽  
Lung Epithelial ◽  
Poultry Dust ◽  
Induced Changes ◽  
Dust Extract

The intensification and concentration of animal production operations expose workers to high levels of organic dusts in the work environment. Exposure to organic dusts is a risk factor for the development of acute and chronic respiratory symptoms and diseases. Lung epithelium plays important roles in the control of immune and inflammatory responses to environmental agents to maintain lung health. To better understand the effects of organic dust on lung inflammatory responses, we characterized the gene expression profiles of A549 alveolar and Beas2B bronchial epithelial and THP-1 monocytic cells influenced by exposure to poultry dust extract by DNA microarray analysis using Illumina Human HT-12 v4 Expression BeadChip. We found that A549 alveolar and Beas2B bronchial epithelial and THP-1 cells responded with unique changes in the gene expression profiles with regulation of genes encoding inflammatory cytokines, chemokines, and other inflammatory proteins being common to all the three cells. Significantly induced genes included IL-8, IL-6, IL-1β, ICAM-1, CCL2, CCL5, TLR4, and PTGS2. Validation by real-time qRT-PCR, ELISA, Western immunoblotting, and immunohistochemical staining of lung sections from mice exposed to dust extract validated DNA microarray results. Pathway analysis indicated that dust extract induced changes in gene expression influenced functions related to cellular growth and proliferation, cell death and survival, and cellular development. These data show that a broad range of inflammatory mediators produced in response to poultry dust exposure can modulate lung immune and inflammatory responses. This is the first report on organic dust induced changes in expression profiles in lung epithelial and THP-1 monocytic cells.

scholarly journals Single-Cell Transcript Profiling of Barley Attacked by the Powdery Mildew Fungus

2007 ◽  
Vol 20 (3) ◽  
pp. 235-246 ◽  
Author(s):  
Torben Gjetting ◽  
Peter H. Hagedorn ◽  
Patrick Schweizer ◽  
Hans Thordal-Christensen ◽  
Timothy L. W. Carver ◽  
...  
Keyword(s):  
Gene Expression ◽  
Powdery Mildew ◽  
Expression Profiles ◽  
Cdna Array ◽  
Gene Expression Profiles ◽  
Powdery Mildew Fungus ◽  
Nonspecific Resistance ◽  
Susceptible Host ◽  
Leaf Epidermal Cell ◽  
Induced Changes

In many plant-pathogen interactions, there are several possible outcomes for simultaneous attacks on the same leaf. For instance, an attack by the powdery mildew fungus on one barley leaf epidermal cell may succeed in infection and formation of a functional haustorium, whereas a neighboring cell attacked at the same time may resist fungal penetration. To date, the mixed cellular responses seen even in susceptible host leaves have made it difficult to relate induced changes in gene expression to resistance or susceptibility in bulk leaf samples. By microextraction of cell-specific mRNA and subsequent cDNA array analysis, we have successfully obtained separate gene expression profiles for specific mildew-resistant and -infected barley cells. Thus, for the first time, it is possible to identify genes that are specifically regulated in infected cells and, presumably, involved in fungal establishment. Further, although much is understood about the genetic basis of effective papilla resistance associated with mutant mlo barley, we provide here the first evidence for gene regulation associated with effective papilla-based nonspecific resistance expressed in nominally “susceptible” wild-type barley.

Prednisolone-induced changes in gene-expression profiles in healthy volunteers

2011 ◽  
Vol 12 (7) ◽  
pp. 985-998 ◽  
Author(s):  
Erik JM Toonen ◽  
Wilco WM Fleuren ◽  
Ulla Nässander ◽  
Marie-José C van Lierop ◽  
Susanne Bauerschmidt ◽  
...  
Keyword(s):  
Gene Expression ◽  
Healthy Volunteers ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Induced Changes
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