Literature DB >> 22967906

Transcriptional biomarkers--high throughput screening, quantitative verification, and bioinformatical validation methods.

Irmgard Riedmaier1, Michael W Pfaffl.   

Abstract

Molecular biomarkers found their way into many research fields, especially in molecular medicine, medical diagnostics, disease prognosis, risk assessment but also in other areas like food safety. Different definitions for the term biomarker exist, but on the whole biomarkers are measureable biological molecules that are characteristic for a specific physiological status including drug intervention, normal or pathological processes. There are various examples for molecular biomarkers that are already successfully used in clinical diagnostics, especially as prognostic or diagnostic tool for diseases. Molecular biomarkers can be identified on different molecular levels, namely the genome, the epigenome, the transcriptome, the proteome, the metabolome and the lipidome. With special "omic" technologies, nowadays often high throughput technologies, these molecular biomarkers can be identified and quantitatively measured. This article describes the different molecular levels on which biomarker research is possible including some biomarker candidates that have already been identified. Hereby the transcriptomic approach will be described in detail including available high throughput methods, molecular levels, quantitative verification, and biostatistical requirements for transcriptional biomarker identification and validation.
Copyright © 2012 Elsevier Inc. All rights reserved.

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Year:  2012        PMID: 22967906     DOI: 10.1016/j.ymeth.2012.08.012

Source DB:  PubMed          Journal:  Methods        ISSN: 1046-2023            Impact factor:   3.608


  23 in total

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Authors:  J Romano-Keeler; J L Wynn; J L Maron
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Authors:  Ramkumar Menon; Janice Jones; Phillip R Gunst; Marian Kacerovsky; Stephen J Fortunato; George R Saade; Sanmaan Basraon
Journal:  Reprod Sci       Date:  2014-01-18       Impact factor: 3.060

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Review 4.  Analysis of the transcriptome in molecular epidemiology studies.

Authors:  Cliona M McHale; Luoping Zhang; Reuben Thomas; Martyn T Smith
Journal:  Environ Mol Mutagen       Date:  2013-08-01       Impact factor: 3.216

5.  Increased expression of long intergenic non-coding RNA LINC00152 in gastric cancer and its clinical significance.

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6.  Downregulation of long noncoding RNA NONHSAT037832 in papillary thyroid carcinoma and its clinical significance.

Authors:  Xiabin Lan; Wei Sun; Ping Zhang; Liang He; Wenwu Dong; Zhihong Wang; Siming Liu; Hao Zhang
Journal:  Tumour Biol       Date:  2015-11-26

7.  Transcriptomic Biomarkers for Tuberculosis: Evaluation of DOCK9. EPHA4, and NPC2 mRNA Expression in Peripheral Blood.

Authors:  Leonardo S de Araujo; Lea A I Vaas; Marcelo Ribeiro-Alves; Robert Geffers; Fernanda C Q Mello; Alexandre S de Almeida; Adriana da S R Moreira; Afrânio L Kritski; José R Lapa E Silva; Milton O Moraes; Frank Pessler; Maria H F Saad
Journal:  Front Microbiol       Date:  2016-10-25       Impact factor: 5.640

8.  Optimization of extraction of circulating RNAs from plasma--enabling small RNA sequencing.

Authors:  Melanie Spornraft; Benedikt Kirchner; Bettina Haase; Vladimir Benes; Michael W Pfaffl; Irmgard Riedmaier
Journal:  PLoS One       Date:  2014-09-17       Impact factor: 3.240

9.  The potential of circulating extracellular small RNAs (smexRNA) in veterinary diagnostics-Identifying biomarker signatures by multivariate data analysis.

Authors:  Spornraft Melanie; Kirchner Benedikt; Michael W Pfaffl; Riedmaier Irmgard
Journal:  Biomol Detect Quantif       Date:  2015-09-19

10.  Guest editor's introduction for BDQ special issue: 'Advanced Molecular Diagnostics for Biomarker Discovery'.

Authors:  Michael W Pfaffl
Journal:  Biomol Detect Quantif       Date:  2015-10-08
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