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      Circulating miR-375 as a novel prognostic marker for metastatic medullary thyroid cancer patients

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          Abstract

          This study aimed to identify circulating miRNAs as novel non-invasive biomarkers for prognosis and vandetanib response in advanced medullary thyroid cancer (MTC) patients. We prospectively recruited two independent cohorts of locally advanced/metastatic MTC patients including a subgroup of vandetanib-treated subjects: a discovery cohort ( n = 20), including matched plasma/tissue samples ( n = 17/20), and a validation cohort, yielding only plasma samples ( n = 17). Plasma samples from healthy subjects ( n = 36) and MTC patients in remission ( n = 9) were used as controls. MTC ( n = 17 from 8 patients included in discovery cohort) and non-neoplastic thyroid specimens ( n = 3) were assessed by microarray profiling to identify candidate circulating miRNAs. qRT-PCR and in situ hybridization were carried out to validate the expression and localization of a selected miRNA within tissues, and qRT-PCR was also performed to measure miRNA levels in plasma samples. By microarray analysis, we identified 51 miRNAs differentially expressed in MTC. The most overexpressed miR, miR-375, was highly expressed by C cells compared to other thyroid cells, and more expressed in MTC than in reactive C-cell hyperplasia. MTC patients had significantly higher miR-375 plasma levels than healthy controls ( P < 0.0001) and subjects in remission ( P = 0.0004) as demonstrated by qRT-PCR analysis. miR-375 plasma levels were not predictive of vandetanib response, but, notably, high levels were associated with significantly reduced overall survival (HR 10.61, P < 0.0001) and were a strong prognostic factor of poor prognosis (HR 6.24, P = 0.00025) in MTC patients. Overall, our results unveil plasma miR-375 as a promising prognostic marker for advanced MTC patients, to be validated in larger cohorts.

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          Most cited references31

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          Robust hyperparameter estimation protects against hypervariable genes and improves power to detect differential expression

          One of the most common analysis tasks in genomic research is to identify genes that are differentially expressed (DE) between experimental conditions. Empirical Bayes (EB) statistical tests using moderated genewise variances have been very effective for this purpose, especially when the number of biological replicate samples is small. The EB procedures can however be heavily influenced by a small number of genes with very large or very small variances. This article improves the differential expression tests by robustifying the hyperparameter estimation procedure. The robust procedure has the effect of decreasing the informativeness of the prior distribution for outlier genes while increasing its informativeness for other genes. This effect has the double benefit of reducing the chance that hypervariable genes will be spuriously identified as DE while increasing statistical power for the main body of genes. The robust EB algorithm is fast and numerically stable. The procedure allows exact small-sample null distributions for the test statistics and reduces exactly to the original EB procedure when no outlier genes are present. Simulations show that the robustified tests have similar performance to the original tests in the absence of outlier genes but have greater power and robustness when outliers are present. The article includes case studies for which the robust method correctly identifies and downweights genes associated with hidden covariates and detects more genes likely to be scientifically relevant to the experimental conditions. The new procedure is implemented in the limma software package freely available from the Bioconductor repository.
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            Youden Index and optimal cut-point estimated from observations affected by a lower limit of detection.

            The receiver operating characteristic (ROC) curve is used to evaluate a biomarker's ability for classifying disease status. The Youden Index (J), the maximum potential effectiveness of a biomarker, is a common summary measure of the ROC curve. In biomarker development, levels may be unquantifiable below a limit of detection (LOD) and missing from the overall dataset. Disregarding these observations may negatively bias the ROC curve and thus J. Several correction methods have been suggested for mean estimation and testing; however, little has been written about the ROC curve or its summary measures. We adapt non-parametric (empirical) and semi-parametric (ROC-GLM [generalized linear model]) methods and propose parametric methods (maximum likelihood (ML)) to estimate J and the optimal cut-point (c *) for a biomarker affected by a LOD. We develop unbiased estimators of J and c * via ML for normally and gamma distributed biomarkers. Alpha level confidence intervals are proposed using delta and bootstrap methods for the ML, semi-parametric, and non-parametric approaches respectively. Simulation studies are conducted over a range of distributional scenarios and sample sizes evaluating estimators' bias, root-mean square error, and coverage probability; the average bias was less than one percent for ML and GLM methods across scenarios and decreases with increased sample size. An example using polychlorinated biphenyl levels to classify women with and without endometriosis illustrates the potential benefits of these methods. We address the limitations and usefulness of each method in order to give researchers guidance in constructing appropriate estimates of biomarkers' true discriminating capabilities. Copyright 2008 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim
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              Serum miR-21 as a diagnostic and prognostic biomarker in colorectal cancer.

              The oncogenic microRNAs (miRNAs) miR-21 and miR-31 negatively regulate tumor-suppressor genes. Their potential as serum biomarkers has not been determined in human colorectal cancer (CRC). To determine whether miR-21 and miR-31 are secretory miRNAs, we screened expression in medium from 2 CRC cell lines, which was followed by serum analysis from 12 CRC patients and 12 control subjects. We validated expression of candidate miRNAs in serum samples from an independent cohort of 186 CRC patients, 60 postoperative patients, 43 advanced adenoma patients, and 53 control subjects. We analyzed miR-21 expression in 166 matched primary CRC tissues to determine whether serum miRNAs reflect expression in CRC. Patient survival analyses were performed by Kaplan-Meier analyses and Cox regression models. All statistical tests were two-sided. Although miR-21 was secreted from CRC cell lines and upregulated in serum of CRC patients, no statistically significant differences were observed in serum miR-31 expression between CRC patients and control subjects. In the validation cohort, miR-21 levels were statistically significantly elevated in preoperative serum from patients with adenomas (P < .001) and CRCs (P < .001). Importantly, miR-21 expression dropped in postoperative serum from patients who underwent curative surgery (P < .001). Serum miR-21 levels robustly distinguished adenoma (area under the curve [AUC] = 0.813; 95% confidence interval [CI] = 0.691 to 0.910) and CRC (AUC = 0.919; 95% CI = 0.867 to 0.958) patients from control subjects. High miR-21 expression in serum and tissue was statistically significantly associated with tumor size, distant metastasis, and poor survival. Moreover, serum miR-21 was an independent prognostic marker for CRC (hazard ratio = 4.12; 95% CI = 1.10 to 15.4; P = .03). Serum miR-21 is a promising biomarker for the early detection and prognosis of CRC.
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                Author and article information

                Journal
                Endocrine-Related Cancer
                Bioscientifica
                1351-0088
                1479-6821
                March 2018
                March 2018
                March 2018
                March 2018
                : 25
                : 3
                : 217-231
                Article
                10.1530/ERC-17-0389
                29298817
                588a75a6-b3a7-44c5-b338-5ef71bb008a1
                © 2018

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