Expression patterns and bioinformatic analysis of miR-1260a and miR-1274a in Prostate Cancer Tunisian patients.
Rahma SaidYoelsis Garcia-MayeaNesrine TrabelsiNouha Setti BoubakerCristina MirAhlem BlelNidhal AtiRosanna PaciucciJavier Hernández-LosaSoumaya RammehAmine DerouicheMohamed ChebilMatilde E LleonartSlah OuerhaniPublished in: Molecular biology reports (2018)
Currently, microRNAs (miRs) represent great biomarkers in cancer due to their stability and their potential role in diagnosis, prognosis and therapy. This study aims to evaluate the expression levels of miRs-1260 and -1274a in prostate cancer (PC) samples and to identify their eventual targets by using bioinformatic analysis. In this project, we evaluated the expression status of miRs-1260 and -1274a in 86 PC patients and 19 controls by using real-time quantitative PCR and 2-ΔΔCt method. Moreover, we retrieved validated and predicted targets of miRs from several datasets by using the "multiMir" R/Bioconductor package. We have found that miRs-1260 and -1274a were over-expressed in PC patients compared to controls (p < 1 × 10-5). Moreover ROC curve for miRs-1260 and 1274a showed a good performance to distinguish between controls group and PC samples with an area under the ROC curve of 0.897 and 0.784 respectively. However, no significant association could be shown between these two miRs and clinical parameters such as PSA levels, Gleason score, tumor stage, D'Amico classification, lymph node metastasis statues, tumor recurrence, metastasis status and progression after a minimum of 5 years follow-up. Finally, a bioinformatic analysis revealed the association between these two miRs and several targets implicated in prostate cancer initiation pathways.
Keyphrases
- prostate cancer
- end stage renal disease
- radical prostatectomy
- newly diagnosed
- lymph node metastasis
- ejection fraction
- chronic kidney disease
- poor prognosis
- cell proliferation
- prognostic factors
- stem cells
- squamous cell carcinoma
- papillary thyroid
- computed tomography
- magnetic resonance imaging
- deep learning
- magnetic resonance
- machine learning
- single cell
- young adults
- long noncoding rna
- binding protein
- patient reported
- rna seq
- data analysis