Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal and aggressive cancers with a less than 6% five-year survival rate. Circulating microRNAs (miRNAs) are emerging as a useful tool for non-invasive diagnosis and prognosis estimation in the various cancer types, including PDAC. Our study aimed to evaluate whether miRNAs in the pre-operative blood plasma specimen have the potential to predict the prognosis of PDAC patients. In total, 112 PDAC patients planned for surgical resection were enrolled in our prospective study. To identify prognostic miRNAs, we used small RNA sequencing in 24 plasma samples of PDAC patients with poor prognosis (overall survival (OS) < 16 months) and 24 plasma samples of PDAC patients with a good prognosis (OS > 20 months). qPCR validation of selected miRNA candidates was performed in the independent cohort of PDAC patients (n = 64). In the discovery phase of the study, we identified 44 miRNAs with significantly different levels in the plasma samples of the group of good and poor prognosis patients. Among these miRNAs, 23 showed lower levels, and 21 showed higher levels in plasma specimens from PDAC patients with poor prognosis. Eleven miRNAs were selected for the validation, but only miR-99a-5p and miR-365a-3p were confirmed to have significantly lower levels and miR-200c-3p higher levels in plasma samples of poor prognosis cases. Using the combination of these 3-miRNA levels, we were able to identify the patients with poor prognosis with sensitivity 85% and specificity 80% (Area Under the Curve = 0.890). Overall, 3-miRNA prognostic score associated with OS was identified in the pre-operative blood plasma samples of PDAC patients undergoing surgical resection. Following further independent validations, the detection of these miRNA may enable identification of PDAC patients who have no survival benefit from the surgical treatment, which is associated with the high morbidity rates.
BACKGROUND Meningioma growth rates are highly variable, even within benign subgroups, with some remaining stable, whereas others grow rapidly. OBJECTIVE To identify molecular-genetic markers for more accurate prediction of meningioma recurrence and better-targeted therapy. METHODS Microarrays identified microRNA (miRNA) expression in primary and recurrent meningiomas of all World Health Organization (WHO) grades. Those found to be deregulated were further validated by quantitative real-time polymerase chain reaction in a cohort of 172 patients. Statistical analysis of the resulting dataset revealed predictors of meningioma recurrence. RESULTS Adjusted and nonadjusted models of time to relapse identified the most significant prognosticators to be miR-15a-5p, miR-146a-5p, and miR-331-3p. The final validation phase proved the crucial significance of miR-146a-5p and miR-331-3p, and clinical factors such as type of resection (total or partial) and WHO grade in some selected models. Following stepwise selection in a multivariate model on an expanded cohort, the most predictive model was identified to be that which included lower miR-331-3p expression (hazard ratio [HR] 1.44; P < .001) and partial tumor resection (HR 3.90; P < .001). Moreover, in the subgroup of total resections, both miRNAs remained prognosticators in univariate models adjusted to the clinical factors. CONCLUSION The proposed models might enable more accurate prediction of time to meningioma recurrence and thus determine optimal postoperative management. Moreover, combining this model with current knowledge of molecular processes underpinning recurrence could permit the identification of distinct meningioma subtypes and enable better-targeted therapies.
BACKGROUND: Meningioma is the most common primary central nervous system neoplasm, accounting for about a third of all brain tumors. Because their growth rates and prognosis cannot be accurately estimated, biomarkers that enable prediction of their biological behavior would be clinically beneficial. OBJECTIVE: To identify coding and noncoding RNAs crucial in meningioma prognostication and pathogenesis. METHODS: Total RNA was purified from formalin-fixed and paraffin-embedded tumor samples of 64 patients with meningioma with distinct clinical characteristics (16 recurrent, 30 nonrecurrent with follow-up of >5 years, and 18 with follow-up of <5 years without recurrence). Transcriptomic sequencing was performed using the HiSeq 2500 platform (Illumina), and biological and functional differences between meningiomas of different types were evaluated by analyzing differentially expression of messenger RNA (mRNA) and long noncoding RNA (IncRNA). The prognostic value of 11 differentially expressed RNAs was then validated in an independent cohort of 90 patients using reverse transcription quantitative (real-time) polymerase chain reaction. RESULTS: In total, 69 mRNAs and 108 lncRNAs exhibited significant differential expression between recurrent and nonrecurrent meningiomas. Differential expression was also observed with respect to sex (12 mRNAs and 59 lncRNAs), World Health Organization grade (58 mRNAs and 98 lncRNAs), and tumor histogenesis (79 mRNAs and 76 lncRNAs). Lnc-GOLGA6A-1, ISLR2, and AMH showed high prognostic power for predicting meningioma recurrence, while lnc-GOLGA6A-1 was the most significant factor for recurrence risk estimation (1/hazard ratio = 1.31; P = .002). CONCLUSION: Transcriptomic sequencing revealed specific gene expression signatures of various clinical subtypes of meningioma. Expression of the lnc-GOLGA61-1 transcript was found to be the most reliable predictor of meningioma recurrence.
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