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      A plasma miRNA signature for lung cancer early detection

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          Abstract

          The early detection of lung cancer continues to be a major clinical challenge. Using whole-transcriptome next-generation sequencing to analyze lung tumor and the matched noncancerous tissues, we previously identified 54 lung cancer-associated microRNAs (miRNAs). The objective of this study was to investigate whether the miRNAs could be used as plasma biomarkers for lung cancer. We determined expressions of the lung tumor-miRNAs in plasma of a development cohort of 180 subjects by using reverse transcription PCR to develop biomarkers. The development cohort included 92 lung cancer patients and 88 cancer-free smokers. We validated the biomarkers in a validation cohort of 64 individuals comprising 34 lung cancer patients and 30 cancer-free smokers. Of the 54 miRNAs, 30 displayed a significant different expression level in plasma of the lung cancer patients vs. cancer-free controls (all P < 0.05). A plasma miRNA signature (miRs-126, 145, 210, and 205-5p) with the best prediction was developed, producing 91.5% sensitivity and 96.2% specificity for lung cancer detection. Diagnostic performance of the plasma miRNA signature had no association with stage and histological type of lung tumor, and patients’ age, sex, and ethnicity (all p > 0.05). The plasma miRNA signature was reproducibly confirmed in the validation cohort. The plasma miRNA signature may provide a blood-based assay for diagnosing lung cancer at the early stage, and thereby reduce the associated mortality and cost.

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          Most cited references45

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          MicroRNA profiling: approaches and considerations.

          MicroRNAs (miRNAs) are small RNAs that post-transcriptionally regulate the expression of thousands of genes in a broad range of organisms in both normal physiological contexts and in disease contexts. miRNA expression profiling is gaining popularity because miRNAs, as key regulators in gene expression networks, can influence many biological processes and also show promise as biomarkers for disease. Technological advances have spawned a multitude of platforms for miRNA profiling, and an understanding of the strengths and pitfalls of different approaches can aid in their effective use. Here, we review the major considerations for carrying out and interpreting results of miRNA-profiling studies.
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            The National Lung Screening Trial: overview and study design.

            The National Lung Screening Trial (NLST) is a randomized multicenter study comparing low-dose helical computed tomography (CT) with chest radiography in the screening of older current and former heavy smokers for early detection of lung cancer, which is the leading cause of cancer-related death in the United States. Five-year survival rates approach 70% with surgical resection of stage IA disease; however, more than 75% of individuals have incurable locally advanced or metastatic disease, the latter having a 5-year survival of less than 5%. It is plausible that treatment should be more effective and the likelihood of death decreased if asymptomatic lung cancer is detected through screening early enough in its preclinical phase. For these reasons, there is intense interest and intuitive appeal in lung cancer screening with low-dose CT. The use of survival as the determinant of screening effectiveness is, however, confounded by the well-described biases of lead time, length, and overdiagnosis. Despite previous attempts, no test has been shown to reduce lung cancer mortality, an endpoint that circumvents screening biases and provides a definitive measure of benefit when assessed in a randomized controlled trial that enables comparison of mortality rates between screened individuals and a control group that does not undergo the screening intervention of interest. The NLST is such a trial. The rationale for and design of the NLST are presented. © RSNA, 2010
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              Optimal cut-point and its corresponding Youden Index to discriminate individuals using pooled blood samples.

              Costs can hamper the evaluation of the effectiveness of new biomarkers. Analysis of smaller numbers of pooled specimens has been shown to be a useful cost-cutting technique. The Youden index (J), a function of sensitivity (q) and specificity (p), is a commonly used measure of overall diagnostic effectiveness. More importantly, J is the maximum vertical distance or difference between the ROC curve and the diagonal or chance line; it occurs at the cut-point that optimizes the biomarker's differentiating ability when equal weight is given to sensitivity and specificity. Using the additive property of the gamma and normal distributions, we present a method to estimate the Youden index and the optimal cut-point, and extend its applications to pooled samples. We study the effect of pooling when only a fixed number of individuals are available for testing, and pooling is carried out to save on the number of assays. We measure loss of information by the change in root mean squared error of the estimates of the optimal cut-point and the Youden index, and we study the extent of this loss via a simulation study. In conclusion, pooling can result in a substantial cost reduction while preserving the effectiveness of estimators, especially when the pool size is not very large.
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                Author and article information

                Journal
                Oncotarget
                Oncotarget
                Oncotarget
                ImpactJ
                Oncotarget
                Impact Journals LLC
                1949-2553
                19 December 2017
                5 December 2017
                : 8
                : 67
                : 111902-111911
                Affiliations
                1 Department of Pathology, University of Maryland School of Medicine, Baltimore, MD 21201, USA
                2 Departments of Biological Sciences, University of Maryland, Baltimore County, Baltimore, MD 21250, USA
                3 Departments of Epidemiology & Public Health, University of Maryland School of Medicine, Baltimore, MD 21201, USA
                4 Department of Biostatistics, Bioinformatics and Biomathematics, Georgetown University Medical Center, Washington, DC 20057, USA
                5 Department of Mathematics & Statistics, University of Maryland, Baltimore County, Baltimore, MD 21250, USA
                Author notes
                Correspondence to: Feng Jiang, fjiang@ 123456som.umaryland.edu
                [*]

                The authors have contributed equally to this work

                Article
                22950
                10.18632/oncotarget.22950
                5762367
                29340099
                4d25aada-96da-423c-80ba-51839e37de3c
                Copyright: © 2017 Leng et al.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License 3.0 (CC BY 3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 8 November 2017
                : 19 November 2017
                Categories
                Research Paper

                Oncology & Radiotherapy
                diagnosis,lung cancer,plasma,microrna,biomarkers
                Oncology & Radiotherapy
                diagnosis, lung cancer, plasma, microrna, biomarkers

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