title: Hidden treasures in "ancient" microarrays: gene-expression portrays biology and potential resistance pathways of major lung cancer subtypes and normal tissue.
abstract: OBJECTIVE
  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.
  
  
  CONCLUSIONS
  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.
authors.affiliation: Department of Computer Science, University of Crete , Heraklion , Greece ; Institute of Computer Science, Foundation of Research and Technology - Hellas , Heraklion , Greece.
authors.forename: Konstantinos
authors.initials: K
authors.lastname: Kerkentzes
authors.affiliation: Institute of Computer Science, Foundation of Research and Technology - Hellas , Heraklion , Greece.
authors.forename: Vincenzo
authors.initials: V
authors.lastname: Lagani
authors.affiliation: Department of Computer Science, University of Crete , Heraklion , Greece ; Institute of Computer Science, Foundation of Research and Technology - Hellas , Heraklion , Greece.
authors.forename: Ioannis
authors.initials: I
authors.lastname: Tsamardinos
authors.affiliation: Institute of Pathology, Aalborg University Hospital , Aalborg , Denmark.
authors.forename: Mogens
authors.initials: M
authors.lastname: Vyberg
authors.affiliation: Department of Cancer Research and Molecular Medicine, Norwegian University of Science and Technology , Trondheim , Norway ; Department of Oncology, Clinical Cancer Research Center, Aalborg University Hospital , Aalborg , Denmark ; Cancer Clinic, Levanger Hospital, Nord-Trøndelag Health Trust , Levanger , Norway.
authors.forename: Oluf Dimitri
authors.initials: OD
authors.lastname: Røe
keywords: bioinformatics; carcinoid; lung adenocarcinoma; mesothelioma; microarray; small cell; squamous