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OMICS for Tumor Biomarker Research

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Biomarkers in Cancer

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Abstract

OMICS generally refers to a study of some gene expression products, either direct, such as RNA and proteins, or indirect, such as metabolites, and is usually based on genome information. Main sections of OMICS sciences include transcriptomics, proteomics, and metabolomics, powerful research instruments capable of high-throughput detection of biomolecules differentially expressed between tumor and non-tumor samples, including excised tissues or biopsies, blood plasma, saliva, and urine. Consequently, thousands of species of RNAs, proteins, and metabolites were suggested as candidate tumor biomarkers alone or as constituents of multiplex signatures. Despite many difficulties encountered by OMICS panels with an intended use in population screening programs, some of the multiplex panels already have found their applications in the field of theranostics. If the patient is already diagnosed with a certain cancer, RNA or protein biomarker signatures may help to select a specific therapy or to predict the probability of a relapse. A number of clinically relevant, validated, and approved signatures of RNA and protein analytes successfully emerged from OMICS pipelines. It is important to remember that an implementation of these clinical tests took the safety of reliable laboratory techniques, such as polymerase chain reaction and immunoassay.

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Abbreviations

AUC:

Area Under the Curve

DNA:

Deoxyribonucleic Acid

ENCODE:

Encyclopedia of DNA Elements

ESI:

Electrospray Ionization

FDA:

US Food and Drug Administration

HPLC:

High-Performance Liquid Chromatography

IVDMIA:

In Vitro Diagnostic Multivariate Index Assay

LC-MS/MS:

Liquid Chromatography-Tandem Mass Spectrometry

LDT:

Laboratory-Developed Tests

LOOCV:

Leave-One-Out Cross Validation

MALDI-TOF:

Matrix-Assisted Laser Desorption/Ionization Time-of-Flight Mass Spectrometry

MRM:

Multiple Reaction Monitoring

MS:

Mass Spectrometry

MS/MS:

Tandem Mass Spectrometry

m/z:

Molecular Mass/Charge Ratio

mRNA:

Matrix Ribonucleic Acid

miRNA:

Micro-Ribonucleic Acid

NMR:

Nuclear Magnetic Resonance

PCR:

Polymerase Chain Reaction

PPV:

Positive Predictive Value

qRT-PCR:

Quantitative Real-Time Polymerase Chain Reaction

RNA:

Ribonucleic Acid

RNAseq:

High-Throughput Sequencing of Ribonucleic Acid

ROC:

Receiver Operator Characteristics

SELDI:

Surface-Enhanced Laser Desorption/Ionization

SRM:

Selected Reaction Monitoring

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Acknowledgments

This work was supported by the Human Proteome Program of the Russian Academy of Medical Sciences, by the grant # 12-04-33109 of the Russian Fund for Basic Research and by the contracts # 14.512.11.0090 (27th June 2013) under the call # 2013-1.2-14-512-0042 and # 8273 (27th August 2012) under the call # 2012-1.1-12-000-2008-067 of the Ministry of Education and Science of Russia.

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Authors and Affiliations

  1. Department of Personalized Medicine, Orekhovich Institute of Biomedical Chemistry, Russian Academy of Medical Sciences, 10 Pogodinskaya, Moscow, 119121, Russia

    Sergei Moshkovskii

  2. Medico-Biological Faculty, Pirogov Russian National Research Medical University, 1 Ostrovityanova, Moscow, 117997, Russia

    Sergei Moshkovskii

  3. Department of Bioinformatics, Orekhovich Institute of Biomedical Chemistry, Russian Academy of Medical Sciences, 10 Pogodinskaya, Moscow, 119121, Russia

    Mikhail Pyatnitsky

  4. Department of Proteomics and Mass-Spectrometry, Orekhovich Institute of Biomedical Chemistry, Russian Academy of Medical Sciences, 10 Pogodinskaya, Moscow, 119121, Russia

    Petr Lokhov

  5. Center for the Study of Chronic Metabolic Diseases, School of System Biology, George Mason University, MSN 3E1, 4400 University Dr, Fairfax, VA, 22030, USA

    Ancha Baranova

  6. Research Centre for Medical Genetics, Russian Academy of Medical Sciences, 1, Moskvorechye, Moscow, 115478, Russia

    Ancha Baranova

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  1. Sergei Moshkovskii
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  2. Mikhail Pyatnitsky
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  3. Petr Lokhov
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  4. Ancha Baranova
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Correspondence to Sergei Moshkovskii .

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  1. Department of Nutrition and Dietetics Division of Diabetes & Nutritional Sciences, Faculty of Life Sciences & Medicine King’s College London, London, United Kingdom

    Victor R. Preedy

  2. Department of Biomedical Sciences Faculty of Science and Technology, University of Westminster, London, United Kingdom

    Vinood B. Patel

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Moshkovskii, S., Pyatnitsky, M., Lokhov, P., Baranova, A. (2014). OMICS for Tumor Biomarker Research. In: Preedy, V., Patel, V. (eds) Biomarkers in Cancer. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-7744-6_14-1

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