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Human disease biomarker panels through systems biology

Abstract

As more uses for biomarkers are sought after for an increasing number of disease targets, single-target biomarkers are slowly giving way for biomarker panels. These panels incorporate various sources of biomolecular and clinical data to guarantee a higher robustness and power of separation for a clinical test. Multifactorial diseases such as psychiatric disorders show great potential for clinical use, assisting medical professionals during the analysis of risk and predisposition, disease diagnosis and prognosis, and treatment applicability and efficacy. More specific tests are also being developed to assist in ruling out, distinguishing between, and confirming suspicions of multifactorial diseases, as well as to predict which therapy option may be the best option for a given patient’s biochemical profile. As more complex datasets are entering the field, involving multi-omic approaches, systems biology has stepped in to facilitate the discovery and validation steps during biomarker panel generation. Filtering biomolecules and clinical data, pre-validating and cross-validating potential biomarkers, generating final biomarker panels, and testing the robustness and applicability of those panels are all beginning to rely on machine learning and systems biology and research in this area will only benefit from advances in these approaches.

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Fig. 1

source software at Diagrams.net. Different classes of biomarkers are separated based on the method(s) available for detection. Nucleic acid sequencing can be used for DNA, epigenetics by DNA modification, mRNA, post-transcriptional modifications, along with tRNA, snRNA, miRNA, siRNA, and aRNA. Immunoassays can detect epigenetics by histone/chromatin modification, polypeptides, proteins, and post-translational modifications. Mass spectrometry can detect all the biomarkers in immunoassays with the addition of metabolite and lipids. UV detection can also be used for metabolites. Gas chromatography and nuclear magnetic resonance can be used for lipids

Fig. 2

source software at Diagrams.net. A flow chart detailing the process from a need for a biomarker-based test, through genomic, epigenomic transcriptomic, proteomic, metabolomic, lipidomic, PTMomic, and clinical data in a discovery-based, unbiased approach into biomolecular and clinical data. Using univariate and multivariate statistics, this and the previously mentioned data can both be published in publicly available repositories and peer-reviewed articles. Statistically filtered data can then pass into potential biomarkers that loop through more statistics and AI-based filtering using machine and deep learning before passing through a model adjustment, cross-validation, and reproducibility tests to reach biomarker status. This then passes on to a biomarker-based test, all of which occurred in a validation-based, biased approach. During implementation, the assay may be redeveloped and is eventually tested for clinical implementability and reaches clinical test status. In the acceptance and use phase, approval by regulatory agencies must be obtained to reach a final, qualified biomarker test for clinical use

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Funding

This research is funded by the Coordination for the Improvement of Higher Education Personnel (CAPES; grant number 88887.495565/2020–00) and The São Paulo Research Foundation (FAPESP; grant numbers 2016/07948–8, 2017/25588–1, 2018/03422–7, 2019/25957–2, and 2020/04746–0).

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Smith, B.J., Silva-Costa, L.C. & Martins-de-Souza, D. Human disease biomarker panels through systems biology. Biophys Rev (2021). https://doi.org/10.1007/s12551-021-00849-y

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Keywords

  • Proteomics
  • Biomarkers
  • Biomarker panels
  • Post-translational modifications
  • Bioinformatics