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      Circulating microRNAs in plasma of patients with gastric cancers

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          Abstract

          Background:

          We examined plasma microRNA (miRNA) concentrations from patients with gastric cancers (GCs) to assess their clinical application for diagnosing and monitoring diseases.

          Methods:

          We initially investigated the appropriateness of plasma miRNA assay, and then compared plasma miRNA results with the expressions in cancer tissues from eight GC patients, and also compared plasma miRNAs between pre- and post-operative paired samples from 10 GC patients. Then, plasma miRNAs ( miR-17-5p, miR-21, miR-106a, miR-106b and let-7a) were analysed in 69 GC patients and 30 healthy volunteers in total.

          Results:

          The initial analysis showed that miRNAs were stable and detectable in all plasma samples, and the plasma miRNA levels reflected the tumour miRNAs in most cases. The levels of these miRNAs were significantly reduced in post-operative samples. In large-scale analysis, the plasma concentrations of miRNAs ( miR-17-5p, miR-21, miR-106a, miR-106b) were significantly higher in GC patients than controls ( P=0.05, 0.006, 0.008 and <0.001 respectively), whereas let-7a was lower in GC patients ( P=0.002). The values of the area under the receiver-operating characteristic curve were 0.721 for the miR-106b assay and 0.879 for the miR-106a/ let-7a ratio assay.

          Conclusion:

          Detection of circulating miRNAs might provide new complementary tumour markers for GC.

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          Most cited references 31

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          Analysis of relative gene expression data using real-time quantitative PCR and the 2(-Delta Delta C(T)) Method.

           K Livak,  T Schmittgen (2001)
          The two most commonly used methods to analyze data from real-time, quantitative PCR experiments are absolute quantification and relative quantification. Absolute quantification determines the input copy number, usually by relating the PCR signal to a standard curve. Relative quantification relates the PCR signal of the target transcript in a treatment group to that of another sample such as an untreated control. The 2(-Delta Delta C(T)) method is a convenient way to analyze the relative changes in gene expression from real-time quantitative PCR experiments. The purpose of this report is to present the derivation, assumptions, and applications of the 2(-Delta Delta C(T)) method. In addition, we present the derivation and applications of two variations of the 2(-Delta Delta C(T)) method that may be useful in the analysis of real-time, quantitative PCR data. Copyright 2001 Elsevier Science (USA).
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            A new mathematical model for relative quantification in real-time RT-PCR.

             M. Pfaffl (2001)
            Use of the real-time polymerase chain reaction (PCR) to amplify cDNA products reverse transcribed from mRNA is on the way to becoming a routine tool in molecular biology to study low abundance gene expression. Real-time PCR is easy to perform, provides the necessary accuracy and produces reliable as well as rapid quantification results. But accurate quantification of nucleic acids requires a reproducible methodology and an adequate mathematical model for data analysis. This study enters into the particular topics of the relative quantification in real-time RT-PCR of a target gene transcript in comparison to a reference gene transcript. Therefore, a new mathematical model is presented. The relative expression ratio is calculated only from the real-time PCR efficiencies and the crossing point deviation of an unknown sample versus a control. This model needs no calibration curve. Control levels were included in the model to standardise each reaction run with respect to RNA integrity, sample loading and inter-PCR variations. High accuracy and reproducibility (<2.5% variation) were reached in LightCycler PCR using the established mathematical model.
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              Global cancer statistics, 2002.

               D Parkin,  F Bray,  J Ferlay (2005)
              Estimates of the worldwide incidence, mortality and prevalence of 26 cancers in the year 2002 are now available in the GLOBOCAN series of the International Agency for Research on Cancer. The results are presented here in summary form, including the geographic variation between 20 large "areas" of the world. Overall, there were 10.9 million new cases, 6.7 million deaths, and 24.6 million persons alive with cancer (within three years of diagnosis). The most commonly diagnosed cancers are lung (1.35 million), breast (1.15 million), and colorectal (1 million); the most common causes of cancer death are lung cancer (1.18 million deaths), stomach cancer (700,000 deaths), and liver cancer (598,000 deaths). The most prevalent cancer in the world is breast cancer (4.4 million survivors up to 5 years following diagnosis). There are striking variations in the risk of different cancers by geographic area. Most of the international variation is due to exposure to known or suspected risk factors related to lifestyle or environment, and provides a clear challenge to prevention.
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                Author and article information

                Affiliations
                [1 ]simpleDivision of Digestive Surgery, Department of Surgery, Kyoto Prefectural University of Medicine 465 Kajii-cho, Kawaramachihirokoji, Kamigyo-ku, Kyoto 602–8566, Japan
                Author notes
                [* ]Author for correspondence: ichikawa@ 123456koto.kpu-m.ac.jp
                Journal
                Br J Cancer
                British Journal of Cancer
                Nature Publishing Group
                0007-0920
                1532-1827
                16 March 2010
                30 March 2010
                30 March 2010
                : 102
                : 7
                : 1174-1179
                2853097
                6605608
                10.1038/sj.bjc.6605608
                20234369
                Copyright 2010, Cancer Research UK
                Categories
                Molecular Diagnostics

                Oncology & Radiotherapy

                biomarker, microrna, gastric cancer, plasma

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