Linked Life Data

16085230

Source:http://linkedlifedata.com/resource/pubmed/id/16085230

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
4
pubmed:dateCreated
2005-8-8
pubmed:abstractText
Breast cancer has never had any good serum tumor markers. Therefore, we developed and evaluated a proteomics approach to searching for new biomarkers and building diagnostic models. SELDI-TOF-MS ProteinChip was used to detect the serum protein patterns of 49 breast cancer patients, 51 patients with benign breast diseases, and 33 healthy women. The diagnostic models were developed and validated using bioinformatics tools such as artificial neural networks and discriminant analysis. In total, four models were built and their sensitivities and specificities were satisfactory. The abilities of these models to diagnose stage I breast cancer were not worse than for stages II-IV (P>0.05). Four candidate biomarkers of breast cancer were found. The high sensitivity and specificity achieved by this method show great potential for the early detection of breast cancer and facilitation of discovering new and improved biomarkers.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Aug
pubmed:issn
0960-9776
pubmed:author
pubmed:issnType
Print
pubmed:volume
14
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
250-5
pubmed:dateRevised
2006-11-15
pubmed:meshHeading
pubmed:year
2005
pubmed:articleTitle
SELDI-TOF-MS: the proteomics and bioinformatics approaches in the diagnosis of breast cancer.
pubmed:affiliation
Cancer Institute, The Second Affiliated Hospital of Zhejiang University, School of Medicine, 88 Jiefang Road, Hangzhou, Zhejiang 310009, China.
pubmed:publicationType
Journal Article, Research Support, Non-U.S. Gov't, Evaluation Studies