4.5 Article

Small nucleolar RNAs as new biomarkers in chronic lymphocytic leukemia

期刊

BMC MEDICAL GENOMICS
卷 6, 期 -, 页码 -

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BMC
DOI: 10.1186/1755-8794-6-27

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资金

  1. Associazione Italiana Ricerca sul Cancro (AIRC) [9980, 2010-15, IG10136, IG10492]
  2. Ministero Italiano dell' Istruzione, Universita e Ricerca (MIUR) [2009PKMYA2]
  3. Fondazione 'Amelia Scorza' onlus, Cosenza

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Background: Small nucleolar RNAs (snoRNAs) and small Cajal body-specific RNAs are non-coding RNAs involved in the maturation of other RNA molecules. Alterations of sno/scaRNA expression may play a role in cancerogenesis. This study elucidates the patterns of sno/scaRNA expression in 211 chronic lymphocytic leukemia (CLL) patients (Binet stage A) also in comparison with those of different normal B-cell subsets. Methods: The patterns of sno/scaRNA expression in highly purified CD19(+) B-cells of 211 CLL patients and in 18 normal B-cell samples - 6 from peripheral blood, and 12 from tonsils (4 germinal center, 2 marginal zone, 3 switched memory and 3 naive B-cells) - were analyzed on the Affymetrix GeneChip (R) Human Gene 1.0 ST array. Results: CLLs display a sno/scaRNAs expression profile similar to normal memory, naive and marginal-zone B-cells, with the exception of a few down-regulated transcripts (SNORA31, -6, -62, and -71C). Our analyses also suggest some heterogeneity in the pattern of sno/scaRNAs expression which is apparently unrelated to the major biological (ZAP-70 and CD38), molecular (IGHV mutation) and cytogenetic markers. Moreover, we found that SNORA70F was significantly down-regulated in poor prognostic subgroups and this phenomenon was associated with the down-regulation of its host gene COBLL1. Finally, we generated an independent model based on SNORA74A and SNORD116-18 expression, which appears to distinguish two different prognostic CLL groups. Conclusions: These data extend the view of sno/scaRNAs deregulation in cancer and may contribute to discover novel biomarkers associated with the disease and potentially useful to predict the clinical outcome of early stage CLL patients.

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