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ABSTRACT: Background
MicroRNAs (miRNAs) constitute a class of small non-coding RNAs that post-transcriptionally regulate genes involved in several key biological processes and thus are involved in various diseases, including cancer. In this study we aimed to identify a miRNA expression signature that could be used to separate between normal and malignant prostate tissues.Results
Nine miRNAs were found to be differentially expressed (p <0.00001). With the exception of two samples, this expression signature could be used to separate between the normal and malignant tissues. A cross-validation procedure confirmed the generality of this expression signature. We also identified 16 miRNAs that possibly could be used as a complement to current methods for grading of prostate tumor tissues.Conclusions
We found an expression signature based on nine differentially expressed miRNAs that with high accuracy (85%) could classify the normal and malignant prostate tissues in patients from the Swedish Watchful Waiting cohort. The results show that there are significant differences in miRNA expression between normal and malignant prostate tissue, indicating that these small RNA molecules might be important in the biogenesis of prostate cancer and potentially useful for clinical diagnosis of the disease.
SUBMITTER: Carlsson J
PROVIDER: S-EPMC3123620 | biostudies-literature | 2011 May
REPOSITORIES: biostudies-literature
Carlsson Jessica J Davidsson Sabina S Helenius Gisela G Karlsson Mats M Lubovac Zelmina Z Andrén Ove O Olsson Björn B Klinga-Levan Karin K
Cancer cell international 20110527 1
<h4>Background</h4>MicroRNAs (miRNAs) constitute a class of small non-coding RNAs that post-transcriptionally regulate genes involved in several key biological processes and thus are involved in various diseases, including cancer. In this study we aimed to identify a miRNA expression signature that could be used to separate between normal and malignant prostate tissues.<h4>Results</h4>Nine miRNAs were found to be differentially expressed (p <0.00001). With the exception of two samples, this expr ...[more]