Summary
The recent development of high-throughput omics technologies has revolutionized the fields of molecular diagnosis and drug development, providing detailed information of cell biology at a degree of resolution never seen before. This huge amount of information requires specialized methods, which are, in fact, continuously growing and diversifying.
In this Special Issue, we focus on the use of multi-omics data for the diagnosis of common complex diseases. These diseases do not follow the Mendelian inheritance patterns, and harbouring a predisposing allele does not guarantee the appearance of the disease since. Other genetic factors, life habits and environmental factors have also a prominent role in the disease to complicate things further [1].
The report from Madrid et al. [2] focusses on the identification of plasma biomarkers of Alzheimer’s disease (AD), the leading cause of dementia worldwide. Although the presence of amyloid deposits and neurofibrillary tangles in the brain are hallmarks of the disease, the identification of peripheric markers of the pathology has turned out a complicated task. In fact, only amyloid and tau levels in cerebrospinal fluid (CSF) are currently used in the clinical setting. Madrid et al. identified genes whose expression was altered in the presence of polymorphic variants previously associated with AD by means of eQTL (expression Quantitative Trait Locus) analysis and compared their expression levels on blood and brain of AD cases and cognitive normal controls for identifying candidate biomarkers of AD.
Corma-Gómez et al. [3] performed a genome-wide association analysis for unrevealing genes that could be involved in the aetiopathogenesis of liver stiffness after antiviral therapy for hepatitis C infection. Subsequent system biology approaches revealed conformational changes in DNA as a key element of the process, allowing the early diagnosis of subjects at risk of developing this unwanted side effect of hepatitis therapy.
Although most commonly used biomarkers are blood-based, CSF, urine, saliva or breath are other frequent sources of biological material for diagnostics purposes. In this issue, Dima et al. [4] performed a systematic review of the use of volatile organic compounds (VOCs) in exhaled breath as non-invasive biomarkers in digestive neoplasia.
Finally, two reports from Siristatidis et al. [5,6] explore the use of omic data and artificial intelligence solutions for improving the success of in vitro fertilization (IVF) with a focus on metabolomic profiling, an application of enormous clinical and economic importance.
Funding
This research received no external funding.
Conflicts of Interest
The author declares no conflict of interest.
References
- Gibson, G. Decanalization and the origin of complex disease. Nat. Rev. Genet. 2009, 10, 134–140. [Google Scholar] [CrossRef] [PubMed]
- Madrid, L.; Labrador, S.C.; González-Pérez, A.; Sáez, M.E.; The Alzheimer’s Disease Neuroimaging Initiative (ADNI). Integrated Genomic, Transcriptomic and Proteomic Analysis for Identifying Markers of Alzheimer’s Disease. Diagnostics 2021, 11, 2303. [Google Scholar] [CrossRef] [PubMed]
- Corma-Gómez, A.; Macías, J.; Rivero, A.; Rivero-Juarez, A.; de los Santos, I.; Reus-Bañuls, S.; Morano, L.; Merino, D.; Palacios, R.; Galera, C.; et al. A Genome-Wide Association Study on Liver Stiffness Changes during Hepatitis C Virus Infection Cure. Diagnostics 2021, 11, 1501. [Google Scholar] [CrossRef] [PubMed]
- Dima, A.C.; Balaban, D.V.; Dima, A. Diagnostic Application of Volatile Organic Compounds as Potential Biomarkers for Detecting Digestive Neoplasia: A Systematic Review. Diagnostics 2021, 11, 2317. [Google Scholar] [CrossRef] [PubMed]
- Siristatidis, C.; Dafopoulos, K.; Papapanou, M.; Stavros, S.; Pouliakis, A.; Eleftheriades, A.; Tatiana Sidiropoulou and Nikolaos Vlahos. Why Has Metabolomics So Far Not Managed to Efficiently Contribute to the Improvement of Assisted Reproduction Outcomes? The Answer through a Review of the Best Available Current Evidence. Diagnostics 2021, 11, 1602. [Google Scholar] [CrossRef] [PubMed]
- Siristatidis, C.; Stavros, S.; Drakeley, A.; Bettocchi, S.; Pouliakis, A.; Drakakis, P.; Papapanou, M.; Vlahos, N. Omics and Artificial Intelligence to Improve In Vitro Fertilization (IVF) Success: A Proposed Protocol. Diagnostics 2021, 11, 743. [Google Scholar] [CrossRef] [PubMed]
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. |
© 2022 by the author. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).