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Open Access Research article

Array-based gene expression, CGH and tissue data defines a 12q24 gain in neuroblastic tumors with prognostic implication

Maija Wolf12*, Miikka Korja34, Ritva Karhu5, Henrik Edgren12, Sami Kilpinen1, Kalle Ojala1, Spyro Mousses6, Anne Kallioniemi5 and Hannu Haapasalo7

Author Affiliations

1 Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Tukholmankatu 8, FIN-00290, Helsinki, Finland

2 Medical Biotechnology, VTT Technical Research Centre of Finland and University of Turku, Itäinen Pitkäkatu 4C, FIN-20520 Turku, Finland

3 Department of Medical Biochemistry and Molecular Biology, University of Turku, FIN-20520 Turku, Finland

4 Department of Neurosurgery, Helsinki University Central Hospital, FIN-00029 HUS, Finland

5 Laboratory of Cancer Genetics, Tampere University Hospital and Institute of Medical Technology, University of Tampere, FIN-33520 Tampere, Finland

6 Pharmaceutical Genomics Division, The Translational Genomics Research Institute, Scottsdale, Arizona 85259, USA

7 Department of Pathology, Tampere University Hospital, FIN-33521 Tampere, Finland

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BMC Cancer 2010, 10:181  doi:10.1186/1471-2407-10-181

Published: 5 May 2010



Neuroblastoma has successfully served as a model system for the identification of neuroectoderm-derived oncogenes. However, in spite of various efforts, only a few clinically useful prognostic markers have been found. Here, we present a framework, which integrates DNA, RNA and tissue data to identify and prioritize genetic events that represent clinically relevant new therapeutic targets and prognostic biomarkers for neuroblastoma.


A single-gene resolution aCGH profiling was integrated with microarray-based gene expression profiling data to distinguish genetic copy number alterations that were strongly associated with transcriptional changes in two neuroblastoma cell lines. FISH analysis using a hotspot tumor tissue microarray of 37 paraffin-embedded neuroblastoma samples and in silico data mining for gene expression information obtained from previously published studies including up to 445 healthy nervous system samples and 123 neuroblastoma samples were used to evaluate the clinical significance and transcriptional consequences of the detected alterations and to identify subsequently activated gene(s).


In addition to the anticipated high-level amplification and subsequent overexpression of MYCN, MEIS1, CDK4 and MDM2 oncogenes, the aCGH analysis revealed numerous other genetic alterations, including microamplifications at 2p and 12q24.11. Most interestingly, we identified and investigated the clinical relevance of a previously poorly characterized amplicon at 12q24.31. FISH analysis showed low-level gain of 12q24.31 in 14 of 33 (42%) neuroblastomas. Patients with the low-level gain had an intermediate prognosis in comparison to patients with MYCN amplification (poor prognosis) and to those with no MYCN amplification or 12q24.31 gain (good prognosis) (P = 0.001). Using the in silico data mining approach, we identified elevated expression of five genes located at the 12q24.31 amplicon in neuroblastoma (DIABLO, ZCCHC8, RSRC2, KNTC1 and MPHOSPH9). Among these, DIABLO showed the strongest activation suggesting a putative role in neuroblastoma progression.


The presented systematic and rapid framework, which integrates aCGH, gene expression and tissue data to obtain novel targets and biomarkers for cancer, identified a low-level gain of the 12q24.31 as a potential new biomarker for neuroblastoma progression. Furthermore, results of in silico data mining suggest a new neuroblastoma target gene, DIABLO, within this region, whose functional and therapeutic role remains to be elucidated in follow-up studies.