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dc.contributor.authorKerkentzes, Konstantinos
dc.contributor.authorLagani, Vincenzo
dc.contributor.authorTsamardinos, Ioannis
dc.contributor.authorVyberg, Mogens
dc.contributor.authorRøe, Oluf Dimitri
dc.identifier.citationFrontiers in Oncology. 2014, 4 (251)nb_NO
dc.description.abstractObjective: Novel statistical methods and increasingly more accurate gene annotations can transform “old” biological data into a renewed source of knowledge with potential clinical relevance. Here, we provide an in silico proof-of-concept by extracting novel information from a high-quality mRNA expression dataset, originally published in 2001, using state-of-the-art bioinformatics approaches. Methods: The dataset consists of histologically defined cases of lung adenocarcinoma (AD), squamous (SQ) cell carcinoma, small-cell lung cancer, carcinoid, metastasis (breast and colon AD), and normal lung specimens (203 samples in total). A battery of statistical tests was used for identifying differential gene expressions, diagnostic and prognostic genes, enriched gene ontologies, and signaling pathways. Results: Our results showed that gene expressions faithfully recapitulate immunohistochemical subtype markers, as chromogranin A in carcinoids, cytokeratin 5, p63 in SQ, and TTF1 in non-squamous types. Moreover, biological information with putative clinical relevance was revealed as potentially novel diagnostic genes for each subtype with specificity 93–100% (AUC = 0.93–1.00). Cancer subtypes were characterized by (a) differential expression of treatment target genes as TYMS, HER2, and HER3 and (b) overrepresentation of treatment-related pathways like cell cycle, DNA repair, and ERBB pathways. The vascular smooth muscle contraction, leukocyte trans-endothelial migration, and actin cytoskeleton pathways were overexpressed in normal tissue. Conclusion: Reanalysis of this public dataset displayed the known biological features of lung cancer subtypes and revealed novel pathways of potentially clinical importance. The findings also support our hypothesis that even old omics data of high quality can be a source of significant biological information when appropriate bioinformatics methods are used.nb_NO
dc.publisherFrontiers Medianb_NO
dc.rightsNavngivelse 4.0 Internasjonal*
dc.titleHidden treasures in "ancient" microarrays: Gene expression portrays biology and potential resistance pathways of major lung cancer subtypes and normal tissuenb_NO
dc.typeJournal articlenb_NO
dc.typePeer reviewednb_NO
dc.source.journalFrontiers in Oncologynb_NO
dc.description.localcodeCopyright: © 2014 Kerkentzes, Lagani, Tsamardinos, Vyberg and Røe. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.nb_NO
cristin.unitnameInstitutt for klinisk og molekylær medisin

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Navngivelse 4.0 Internasjonal
Except where otherwise noted, this item's license is described as Navngivelse 4.0 Internasjonal