Long non-coding RNA metastasis associated in lung adenocarcinoma transcript 1 derived miniRNA as a novel plasma-based biomarker for diagnosing prostate cancer.

Abstract: Examining plasma RNA is an emerging non-invasive diagnosis technique. However, whether tumour-derived long non-coding RNAs (lncRNAs) in plasma can be used as a novel approach to detect human prostate cancer (PCa) has not yet been established. The study was divided into three parts: (1) the...

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Publicado en:European Journal of Cancer Vol. 49; no. 13; pp. 2949 - 2960
Autores principales: Ren, Shancheng, Wang, Fubo, Shen, Jian, Sun, Yi, Xu, Weidong, Lu, Ji, Wei, Min, Xu, Chuanliang, Wu, Chengyao, Zhang, Zhensheng, Gao, Xu, Liu, Zhiyong, Hou, Jianguo, Huang, Jiaoti, Sun, Yinghao
Formato: research Journal Article
Publicado: Pergamon Press - An Imprint of Elsevier Science Sep2013
Acceso en línea:Ver este registro en EBSCOhost
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      dt: Sep2013
      vid: 49
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      pub: Pergamon Press - An Imprint of Elsevier Science
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        104203159
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        10.1016/j.ejca.2013.04.026
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        104203159
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        atl: Long non-coding RNA metastasis associated in lung adenocarcinoma transcript 1 derived miniRNA as a novel plasma-based biomarker for diagnosing prostate cancer.
      aug:
        au:
          Ren, Shancheng
          Wang, Fubo
          Shen, Jian
          Sun, Yi
          Xu, Weidong
          Lu, Ji
          Wei, Min
          Xu, Chuanliang
          Wu, Chengyao
          Zhang, Zhensheng
          Gao, Xu
          Liu, Zhiyong
          Hou, Jianguo
          Huang, Jiaoti
          Sun, Yinghao
        affil: Department of Urology, Shanghai Changhai Hospital, Second Military Medical University, Shanghai, China
      sug:
        subj:
          Adenocarcinoma
          Lung Neoplasms Complications
          Neoplasm Metastasis Analysis
          Biological Markers Analysis
          Prostatic Neoplasms Diagnosis
          RNA Analysis
          Human
          Male
          Descriptive Statistics
          ROC Curve
          Gene Expression
          P-Value
          Sensitivity and Specificity
          Male
      ab: Abstract: Examining plasma RNA is an emerging non-invasive diagnosis technique. However, whether tumour-derived long non-coding RNAs (lncRNAs) in plasma can be used as a novel approach to detect human prostate cancer (PCa) has not yet been established. The study was divided into three parts: (1) the characteristics of PCa-related lncRNA fragments were systematically studied in the plasma or serum of 25 patients; (2) the source of the circulating lncRNA fragments was explored in vitro and in vivo; and (3) the diagnostic performance of metastasis associated in lung adenocarcinoma transcript 1 (MALAT-1) derived (MD) miniRNA was validated in an independent cohort of 192 patients. The expression levels of lncRNAs were measured by quantitative real time polymerase chain reaction (qRT-PCR). The MD-miniRNA copies were calculated using a standard curve in an area under the ROC curve (AUC)-receiver operating characteristic (ROC) analysis. Genome-wide profiling revealed that MALAT-1 and prostate cancer gene 3 (PCA3) are overexpressed in PCa tissues. Plasma lncRNAs probably exist in the form of fragments in a stable form. MD-miniRNA enters cell culture medium at measurable levels, and MD-miniRNA derived from human PCa xenografts actually enters the circulation in vivo and can be measured to distinguish xenografted mice from controls. In addition, plasma MD-miniRNA levels are significantly elevated in PCa patients compared to non-PCa patients (p <0.001). At a cut-off of 867.8 MD-miniRNA copies per microlitre of plasma, the sensitivity is 58.6%, 58.6% and 43.5% and the specificity is 84.8%, 84.8% and 81.6% for discriminating PCa from non-PCa, positive biopsy from negative biopsy and positive biopsy from negative biopsy, respectively. We conclude that MD-miniRNA can be used as a novel plasma-based biomarker for PCa detection and can improve diagnostic accuracy by predicting prostate biopsy outcomes. Further large-scale studies are needed to confirm our findings.
      pubtype: Academic Journal
      doctype:
        research
        Journal Article
      ougenre: Article
    language: English
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