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Diagnostics
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Novel Omic Markers for Diseases Diagnosis
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Novel Omic Markers for Diseases Diagnosis

A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Pathology and Molecular Diagnostics".

Deadline for manuscript submissions: closed (30 April 2022) | Viewed by 14052

Special Issue Editor

Dr. María Eugenia Sáez

E-Mail Website
Guest Editor
Andalusian Bioinformatics Research Centre (CAEBi), Sevilla, Spain
Interests: genetics; OMICs data analysis and integration; Alzheimer's disease; cancer

Special Issue Information

Dear Colleagues,

Many common diseases, such as diabetes, cancer, some autoimmune disorders or Alzheimer’s disease, are caused by a combination of genetic, environmental and lifestyle factors. These so-called complex diseases do not follow the Mendelian inheritance patterns, and harboring a predisposing allele does not guarantee the appearance of the disease. In the early 2000s, the development of the array technology allowed the implementation of genome-wide association studies (GWAS) for the identification of the genetic susceptibility factors for most common diseases, with limited success. Other omics technologies have been developed more recently, including epigenomics, transcriptomics, proteomics, metabolomics and microbiomics, providing alternative views of molecular defects, inherited or not, predisposing to the disease. The potential of these technologies, and more importantly, the potential of the integration of different layers of information about cell biology, has not been yet been fully exploited.

This Special Issue welcomes original work and review articles on novel omic markers for common diseases, with a special interest in the integration of different technologies.

Dr. María Eugenia Sáez
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Diagnostics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Complex diseases
  • Genetics
  • Trancriptomics
  • Epigenomics
  • Metabolomics
  • Proteomics
  • Microbiomics
  • Integrative analysis

Published Papers (5 papers)

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Editorial

Jump to: Research, Review, Other

2 pages, 189 KiB  
Editorial
Omics in Clinical Practice: How Far Are We?
by María Eugenia Sáez
Diagnostics 2022, 12(7), 1692; https://0-doi-org.brum.beds.ac.uk/10.3390/diagnostics12071692 - 11 Jul 2022
Viewed by 947
Abstract
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 [...] Full article
(This article belongs to the Special Issue Novel Omic Markers for Diseases Diagnosis)

Research

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12 pages, 2043 KiB  
Article
Integrated Genomic, Transcriptomic and Proteomic Analysis for Identifying Markers of Alzheimer’s Disease
by Laura Madrid, Sandra C. Labrador, Antonio González-Pérez, María E. Sáez and The Alzheimer’s Disease Neuroimaging Initiative (ADNI)
Diagnostics 2021, 11(12), 2303; https://0-doi-org.brum.beds.ac.uk/10.3390/diagnostics11122303 - 08 Dec 2021
Cited by 6 | Viewed by 2725
Abstract
There is an urgent need to identify biomarkers for Alzheimer’s disease (AD), but the identification of reliable blood-based biomarkers has proven to be much more difficult than initially expected. The current availability of high-throughput multi-omics data opens new possibilities in this titanic task. [...] Read more.
There is an urgent need to identify biomarkers for Alzheimer’s disease (AD), but the identification of reliable blood-based biomarkers has proven to be much more difficult than initially expected. The current availability of high-throughput multi-omics data opens new possibilities in this titanic task. Candidate Single Nucleotide Polymorphisms (SNPs) from large, genome-wide association studies (GWAS), meta-analyses exploring AD (case-control design), and quantitative measures for cortical structure and general cognitive performance were selected. The Genotype-Tissue Expression (GTEx) database was used for identifying expression quantitative trait loci (eQTls) among candidate SNPs. Genes significantly regulated by candidate SNPs were investigated for differential expression in AD cases versus controls in the brain and plasma, both at the mRNA and protein level. This approach allowed us to identify candidate susceptibility factors and biomarkers of AD, facing experimental validation with more evidence than with genetics alone. Full article
(This article belongs to the Special Issue Novel Omic Markers for Diseases Diagnosis)
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Review

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13 pages, 512 KiB  
Review
Diagnostic Application of Volatile Organic Compounds as Potential Biomarkers for Detecting Digestive Neoplasia: A Systematic Review
by Augustin Catalin Dima, Daniel Vasile Balaban and Alina Dima
Diagnostics 2021, 11(12), 2317; https://0-doi-org.brum.beds.ac.uk/10.3390/diagnostics11122317 - 09 Dec 2021
Cited by 15 | Viewed by 2966
Abstract
Volatile organic compounds (VOCs) are part of the exhaled breath that were proposed as non-invasive breath biomarkers via different human discharge products like saliva, breath, urine, blood, or tissues. Particularly, due to the non-invasive approach, VOCs were considered as potential biomarkers for non-invasive [...] Read more.
Volatile organic compounds (VOCs) are part of the exhaled breath that were proposed as non-invasive breath biomarkers via different human discharge products like saliva, breath, urine, blood, or tissues. Particularly, due to the non-invasive approach, VOCs were considered as potential biomarkers for non-invasive early cancer detection. We herein aimed to review the data over VOCs utility in digestive neoplasia as early diagnosis or monitoring biomarkers. A systematic literature search was done using MEDLINE via PubMed, Cochrane Library, and Thomson Reuters’ Web of Science Core Collection. We identified sixteen articles that were included in the final analysis. Based on the current knowledge, we cannot identify a single VOC as a specific non-invasive biomarker for digestive neoplasia. Several combinations of up to twelve VOCs seem promising for accurately detecting some neoplasia types. A combination of different VOCs breath expression are promising tools for digestive neoplasia screening. Full article
(This article belongs to the Special Issue Novel Omic Markers for Diseases Diagnosis)
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Other

10 pages, 2656 KiB  
Perspective
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
by Charalampos Siristatidis, Konstantinos Dafopoulos, Michail Papapanou, Sofoklis Stavros, Abraham Pouliakis, Anna Eleftheriades, Tatiana Sidiropoulou and Nikolaos Vlahos
Diagnostics 2021, 11(9), 1602; https://0-doi-org.brum.beds.ac.uk/10.3390/diagnostics11091602 - 02 Sep 2021
Cited by 7 | Viewed by 2094
Abstract
Metabolomics emerged to give clinicians the necessary information on the competence, in terms of physiology and function, of gametes, embryos, and the endometrium towards a targeted infertility treatment, namely, assisted reproduction techniques (ART). Our minireview aims to investigate the current status of the [...] Read more.
Metabolomics emerged to give clinicians the necessary information on the competence, in terms of physiology and function, of gametes, embryos, and the endometrium towards a targeted infertility treatment, namely, assisted reproduction techniques (ART). Our minireview aims to investigate the current status of the use of metabolomics in assisted reproduction, the potential flaws in its use, and to propose specific solutions towards the improvement of ART outcomes through the use of the intervention. We used published reports assessing the role of metabolomic investigation of the endometrium, oocytes, and embryos in improving clinical outcomes in women undergoing ART. We initially found that there is no evidence to support that fertility outcomes can be improved through metabolomics profiling. In contrast, it may be helpful for understanding and appraising the nutritional environment of oocytes and embryos. The causes include the different infertility populations, the difference between animals and humans, technical limitations, and the great heterogeneity in the variables employed. Suggested steps include the standardization of variables of the method itself, the universal creation of a panel where all biomarkers are stored concerning specific infertile populations with different phenotypes or etiologies, specific bioinformatics contribution, significant computing power for data processing, and importantly, properly conducted trials. Full article
(This article belongs to the Special Issue Novel Omic Markers for Diseases Diagnosis)
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10 pages, 4676 KiB  
Study Protocol
Omics and Artificial Intelligence to Improve In Vitro Fertilization (IVF) Success: A Proposed Protocol
by Charalampos Siristatidis, Sofoklis Stavros, Andrew Drakeley, Stefano Bettocchi, Abraham Pouliakis, Peter Drakakis, Michail Papapanou and Nikolaos Vlahos
Diagnostics 2021, 11(5), 743; https://0-doi-org.brum.beds.ac.uk/10.3390/diagnostics11050743 - 21 Apr 2021
Cited by 11 | Viewed by 4155
Abstract
The prediction of in vitro fertilization (IVF) outcome is an imperative achievement in assisted reproduction, substantially aiding infertile couples, health systems and communities. To date, the assessment of infertile couples depends on medical/reproductive history, biochemical indications and investigations of the reproductive tract, along [...] Read more.
The prediction of in vitro fertilization (IVF) outcome is an imperative achievement in assisted reproduction, substantially aiding infertile couples, health systems and communities. To date, the assessment of infertile couples depends on medical/reproductive history, biochemical indications and investigations of the reproductive tract, along with data obtained from previous IVF cycles, if any. Our project aims to develop a novel tool, integrating omics and artificial intelligence, to propose optimal treatment options and enhance treatment success rates. For this purpose, we will proceed with the following: (1) recording subfertile couples’ lifestyle and demographic parameters and previous IVF cycle characteristics; (2) measurement and evaluation of metabolomics, transcriptomics and biomarkers, and deep machine learning assessment of the oocyte, sperm and embryo; (3) creation of artificial neural network models to increase objectivity and accuracy in comparison to traditional techniques for the improvement of the success rates of IVF cycles following an IVF failure. Therefore, “omics” data are a valuable parameter for embryo selection optimization and promoting personalized IVF treatment. “Omics” combined with predictive models will substantially promote health management individualization; contribute to the successful treatment of infertile couples, particularly those with unexplained infertility or repeated implantation failures; and reduce multiple gestation rates. Full article
(This article belongs to the Special Issue Novel Omic Markers for Diseases Diagnosis)
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