Linked Life Data

15257427

Source:http://linkedlifedata.com/resource/pubmed/id/15257427

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
7-8
pubmed:dateCreated
2004-8-31
pubmed:abstractText
In this paper, principal component analysis (PCA) is applied to a spot quantity dataset comprising 435 spots detected in 18 samples belonging to two different cell lines (Paca44 and T3M4) of control (untreated) and drug-treated pancreatic ductal carcinoma cells. The aim of the study was the identification of the differences occurring between the proteomic patterns of the two investigated cell lines and the evaluation of the effect of the drug Trichostatin A on the protein content of the cells. PCA turned out to be a successful tool for the identification of the classes of samples present in the dataset. Moreover, the loadings analysis allowed the identification of the differentially expressed spots, which characterise each group of samples. The treatment of both the cell lines with Trichostatin A therefore showed an appreciable effect on the proteomic pattern of the treated samples. Identification of some of the most relevant spots was also performed by mass spectrometry.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:chemical
pubmed:status
MEDLINE
pubmed:month
Aug
pubmed:issn
1618-2642
pubmed:author
pubmed:issnType
Print
pubmed:volume
379
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
992-1003
pubmed:dateRevised
2006-11-15
pubmed:meshHeading
pubmed:year
2004
pubmed:articleTitle
Identification of the regulatory proteins in human pancreatic cancers treated with Trichostatin A by 2D-PAGE maps and multivariate statistical analysis.
pubmed:affiliation
Department of Environmental and Life Sciences, University of Eastern Piedmont, Spalto Marengo 33, 15100 Alessandria, Italy. emilio.marengo@mfn.unipmn.it
pubmed:publicationType
Journal Article, Comparative Study, Research Support, Non-U.S. Gov't