Omics Approaches to Biomarker Analysis

Omics approaches can be used to identify novel biomarkers to aid in furthering diagnostics and therapeutics. Biomarkers can indicate a cell's health, which is significant when assessing diseased cells, tissues, and patients as a whole. Molecular markers, including genes, proteins, and other molecules, can be utilized for diagnostics, prognostics, as well as for predicting therapeutic responses while also being used in research for treatment development.

Cardiovascular Biomarkers

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The use of omics approaches can aid with high-throughput technologies, including genomic microarrays as well as proteomic and metabolomic mass spectrometry, which can produce large datasets from single experiments. Omics-based approaches and computational and bioinformatic techniques can accelerate biomarker discovery and are now used ubiquitously for diagnostic and therapeutic development for various diseases, including cancer.

Examples of biomarkers identified for diseases include PPA2 and Ezrin for the diagnosis of metastatic prostate cancer and a proteolytic fragment of alpha1-anti-trypsin (BF5) as a potential diagnostic and prognostic marker for inflammatory breast cancer. BF5 was also identified as a potential therapeutic target, significant for novel treatment development.

Omics

Omics mainly includes genomics, epigenomics, proteomics, transcriptomics, and metabolomics. The technological development of omics has led to novel approaches to studying disease diagnosis and prognosis while enabling further definitions of complex diseases and disorders. These approaches can aid with biomedical research and assist with identifying molecular targets and biomarkers for diagnostic and therapeutic development.

Omics experimental design comprises a hypothesis-generating element, which enables a large dataset to be obtained for insight into novel disease processes. This contrasts the reductionist strategy of molecular medicine-based targeted methods, with omics approaches being more intensive in resources, as well as being analytically demanding. Additionally, omics approaches require statistical modeling to analyze the large datasets that comprise hundreds to thousands of variables to reduce false positives (known as type I errors) and false negatives (known as type II errors).

The analysis of large datasets through omics approaches can also aid in understanding significant fluctuations in biomarker datasets and how they relate to diagnostics or therapeutics. International recommendations based on STARD guidelines are used to examine and validate the potential of newly identified biomarkers to assess their diagnostic accuracy.

Omics biomarkers are identified from omics technologies; this is different from traditional biomarkers that typically include a single or a few molecules, while an omics biomarker consists of a panel of molecules such as mRNA, proteins, and metabolites. Researchers that use omics to identify biomarkers use the comprehension of their data source to figure out the best strategies to develop an omics biomarker.

Interestingly, the Food and Drug Administration (FDA) has developed a free tool available for analyzing omics data called ArrayTrack™, which is used routinely in reviewing pharmacogenomic data submitted to the FDA. This delivers a rich collection of information about genes, proteins, and pathways, taken from a range of public biological databases used to interpret data.

Biomarker Analysis at Various Biological levels

Molecular biomarkers at various biological levels can be significant in monitoring all types of diseases and disorders. This includes DNA, RNA, protein and metabolites, which can be assessed using different technologies to analyze a range of biomarkers.

DNA can be assessed using molecular techniques such as single nucleotide polymorphism (SNP) microarray or next-generation sequencing (NGS) for genetic biomarkers. RNA can be assessed using microarrays as well as NGS, which will produce information on gene expression for genomic biomarkers. Proteins can be assessed using proteomics technology, such as 1D/2D gel electrophoresis coupled with mass spectrometry, tandem mass spectrometry, or high-resolution nuclear magnetic resonance, which produces data on proteins for protein biomarkers. Additionally, metabolism can be assessed using nuclear magnetic resonance and mass spectrometry, which produces data on metabolites for metabolomic biomarkers.

The analysis of data from a range of biological levels through omics approaches can ensure data at all levels are analyzed to discover novel biomarkers as it would cover how different types of molecules, including proteins, DNA, and metabolites, interact with each other as well as its pathophysiology in a range of diseases.

Challenges and Future Outlook

While there has been a significant interest in discovering omics biomarkers, only a few are currently in use for risk assessment. This is due to the challenges in developing omics biomarkers as well as in translating these into practice. Challenges can include choosing samples and technologies to analyze samples and obtain data, which can vary depending on the research, as choosing blood samples due to being minimally invasive can be useful when assessing liver toxicity.

Other challenges can include requiring efficient bioinformatic tools and databases, as bioinformatics is required for all steps in biomarker discovery using omics data, and so this can require efficient bioinformatic tools; otherwise, this can delay the discovery of omics biomarkers. 

With technology such as NGS, SNP microarrays, and mass spectrometry being used for omics biomarker research, this may lead to developing more comprehensive biomarkers for a range of diseases. Additionally, combining omics technologies with bioinformatics strategies for discovering biomarkers can aid in more successfully developed omics biomarkers.

This novel strategy, combined with the high interest in omics biomarker research by scientists, can be applied to solve various problems in diagnostics and therapeutics, including orphan disorders and diseases without a cure.

Sources:

  • Brooks, J., Watson, A. and Korcsmaros, T. (2017) "OMICS approaches to identify potential biomarkers of inflammatory diseases in the focal adhesion complex," Genomics, Proteomics & Bioinformatics, 15(2), pp. 101–109. Available at: https://doi.org/10.1016/j.gpb.2016.12.003.
  • Fang, H. et al. (2013) "OMICS biomarkers in risk assessment," Computational Toxicology, pp. 195–213. Available at: https://doi.org/10.1016/b978-0-12-396461-8.00013-0.
  • Hu, Z.-Z. et al. (2011) "OMICS-based molecular target and biomarker identification," Methods in Molecular Biology, pp. 547–571. Available at: https://doi.org/10.1007/978-1-61779-027-0_26.
  • Li, C. et al. (2021) "Data Analysis Methods for defining biomarkers from OMICS data," Analytical and Bioanalytical Chemistry, 414(1), pp. 235–250. Available at: https://doi.org/10.1007/s00216-021-03813-7.

Last Updated: Jan 30, 2023

Marzia Khan

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Marzia Khan

Marzia Khan is a lover of scientific research and innovation. She immerses herself in literature and novel therapeutics which she does through her position on the Royal Free Ethical Review Board. Marzia has a MSc in Nanotechnology and Regenerative Medicine as well as a BSc in Biomedical Sciences. She is currently working in the NHS and is engaging in a scientific innovation program.

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