Messina: A Novel Analysis Tool to Identify Biologically Relevant Molecules in Disease

Morphologically similar cancers display heterogeneous patterns of molecular aberrations and follow substantially different clinical courses. This diversity has become the basis for the definition of molecular phenotypes, with significant implications for therapy. Microarray or proteomic expression p...

Celý popis

Uloženo v:

Podrobná bibliografie
Vydáno v:	PloS one Ročník 4; číslo 4; s. e5337
Hlavní autoři:	Pinese, Mark, Scarlett, Christopher J., Kench, James G., Colvin, Emily K., Segara, Davendra, Henshall, Susan M., Sutherland, Robert L., Biankin, Andrew V.
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	United States Public Library of Science 28.04.2009 Public Library of Science (PLoS)
Témata:	Aberration Algorithms Artificial intelligence Bayesian analysis Biochemistry/Bioinformatics Bioinformatics Cancer Cell Biology/Gene Expression Chemotactic Factors - metabolism Computational Biology/Transcriptional Regulation Data processing Databases, Genetic DNA microarrays Experiments Gene Expression Gene Expression Profiling - statistics & numerical data Genes Genetic Markers Genetic Techniques - statistics & numerical data Genetics and Genomics/Bioinformatics Genetics and Genomics/Cancer Genetics Genetics and Genomics/Gene Expression Genomics Humans Identification methods Immunohistochemistry Markers Mathematics/Algorithms Medical diagnosis Medical research Molecular biology Molecular Biology/Bioinformatics Neoplasms - genetics Neoplasms - metabolism Oligonucleotide Array Sequence Analysis - statistics & numerical data Pancreatic cancer Pancreatic Neoplasms - genetics Prostate cancer Protein Array Analysis - statistics & numerical data S100 Proteins - metabolism Sensitivity and Specificity Software Transcription factors Australia New South Wales Australia
ISSN:	1932-6203, 1932-6203
On-line přístup:	Získat plný text
Tagy:	Přidat tag Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!

Popis
Shrnutí:	Morphologically similar cancers display heterogeneous patterns of molecular aberrations and follow substantially different clinical courses. This diversity has become the basis for the definition of molecular phenotypes, with significant implications for therapy. Microarray or proteomic expression profiling is conventionally employed to identify disease-associated genes, however, traditional approaches for the analysis of profiling experiments may miss molecular aberrations which define biologically relevant subtypes. Here we present Messina, a method that can identify those genes that only sometimes show aberrant expression in cancer. We demonstrate with simulated data that Messina is highly sensitive and specific when used to identify genes which are aberrantly expressed in only a proportion of cancers, and compare Messina to contemporary analysis techniques. We illustrate Messina by using it to detect the aberrant expression of a gene that may play an important role in pancreatic cancer. Messina allows the detection of genes with profiles typical of markers of molecular subtype, and complements existing methods to assist the identification of such markers. Messina is applicable to any global expression profiling data, and to allow its easy application has been packaged into a freely-available stand-alone software package.
Bibliografie:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 ObjectType-Article-2 ObjectType-Feature-1 content type line 23 ObjectType-Undefined-3 Conceived and designed the experiments: MP. Performed the experiments: MP JGK EKC DS AVB. Analyzed the data: MP. Contributed reagents/materials/analysis tools: MP RS AVB. Wrote the paper: MP. Wrote the software: MP. Provided critical advice on experimental design: CJS. Provided critical advice on manuscript structure: CJS RS. Performed extensive critical reading of the manuscript: SH.
ISSN:	1932-6203 1932-6203
DOI:	10.1371/journal.pone.0005337