Study of proteomic changes associated with healthy and tumoral murine samples in neuroblastoma by principal component analysis and classification methods

Emilio Marengo, Elisa Robotti, Pier Giorgio Righetti, Natascia Campostrini, Jennifer Pascali, Mirko Ponzoni, Mahmoud Hamdan, Hubert Astner

Research output: Contribution to journalArticlepeer-review

Abstract

Background: The adrenal gland is the election organ forming primary neuroblastoma (NB) tumours, the most common extracranial solid tumours of infancy and childhood. Methods: Samples of adrenal gland belonging to healthy and diseased nude mouse were analysed by 2D gel-electrophoresis. The resulting 2D-PAGE maps were digitized by PDQuest and investigated by principal component analysis (PCA). Results: The analysis of the loadings of the first principal component (PC) permitted the evaluation of the spots characterising each class of samples. Moreover, the soft-independent model of class analogy (SIMCA) method confirmed the separation of the samples in the two classes and allowed the identification of the modelling and discriminating spots. Very good correlation was found between the data obtained by analysis of 2D maps via the commercial software PDQuest and the present PCA analysis. In both cases, the comparison between such maps showed up- and down-regulation of 84 polypeptide chains, out of a total of 700 spots detected by a fluorescent stain, Sypro Ruby. Spots that were differentially expressed between the two groups were analysed by matrix-assisted laser desorption time-of-flight (MALDI-TOF) mass spectrometry and 14 of these spots were identified so far.

Original languageEnglish
Pages (from-to)55-67
Number of pages13
JournalClinica Chimica Acta
Volume345
Issue number1-2
DOIs
Publication statusPublished - Jul 2004

Keywords

  • 2D gel electrophoresis
  • Multivariate analysis
  • Neuroblastoma
  • Principal component analysis
  • Proteomics

ASJC Scopus subject areas

  • Biochemistry
  • Clinical Biochemistry

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