Novel gene expression model for outcome prediction in paediatric medulloblastoma
| Autor: | Krzysztof Zakrzewski, Magdalena Zakrzewska, Sylwia M. Gresner, Beata Zalewska-Szewczyk, Pawel P. Liberski |
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| Rok vydání: | 2013 |
| Předmět: |
Male
Adolescent Transcription Genetic Lewis X Antigen Nerve Tissue Proteins Disease Receptors Nerve Growth Factor Biology Bioinformatics Cellular and Molecular Neuroscience SOX1 Antigen Antigens CD medicine Basic Helix-Loop-Helix Transcription Factors Humans AC133 Antigen Child Survival rate Survival analysis Glycoproteins Medulloblastoma Framingham Risk Score Otx Transcription Factors Proportional hazards model SOXB1 Transcription Factors Infant General Medicine Neoplasms Germ Cell and Embryonal medicine.disease Fucosyltransferases Prognosis Child Preschool Neoplastic Stem Cells Female Peptides |
| Zdroj: | Journal of molecular neuroscience : MN. 51(2) |
| ISSN: | 1559-1166 |
| Popis: | Medulloblastoma is the most frequent type of embryonal tumour in the paediatric population. The disease progression in patients with this tumour may be connected with the presence of stem/tumour-initiating cells, but the precise source and characteristics of such cells is still a subject of debate. Thus, we tried to analyse biomarkers for which a connection with the presence of stem/tumour-initiating cells was suggested. We evaluated the transcriptional level of the ATOH1, FUT4, NGFR, OTX1, OTX2, PROM1 and SOX1 genes in 48 samples of medulloblastoma and analysed their usefulness in the prediction of disease outcome. The analyses showed a strong correlation of PROM1, ATOH1 and OTX1 gene expression levels with the outcome (p ≤ 0.2). On the basis of the multivariate Cox regression analysis, we propose a three-gene model predicting risk of the disease, calculated as follows: RS(risk score) =( 0:81 x PROM1) + (0:18 x OTX1) + (0:02 x ATOH1). Survival analysis revealed a better outcome among standard-risk patients, with a 5-year survival rate of 65 %, compared to the 40 % rate observed among high-risk patients. The most promising advantage of such molecular analysis consists in the identification of molecular markers influencing clinical behaviour, which may in turn be useful in therapy optimization. |
| Databáze: | OpenAIRE |
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