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Human diseases account for complex traits that usually exhibit markedly diverse clinical manifestations coming from a series of pathogenic processes that shape heterogeneous phenotypes. Considering that correlation does not imply causation as well as population differences and/or inter-individual variability, disease-specific signatures are becoming critical for biomarker discovery. Untargeted metabolomics is deemed to be a powerful approach to delineate molecular pathways of prime interest. Metabotypes capture the interplay of genomics and environmental influences per se. Untargeted metabolomics share the charm of being not only hypothesis-driven but also hypothesis-generating. Notwithstanding, the applicability of untargeted metabolomics toward clinically relevant outcomes depend on wet- and dry-lab procedures in the context of elegant study designs with clear rationale. As ideal may be far from feasible, herein we provide recommendations to combat sample mishandling that adversely affect data outcomes and if so, deal with imbalanced datasets toward data integrity.

Recurrent cardiovascular events remain an enigma that accounts for30% of deaths worldwide. While heredity and human genetics variation play a key role, host-environment interactions offer a sound conceptual framework to dissect the molecular basis of recurrent cardiovascular events from genes and proteins to metabolites, thus accounting for environmental contributions as well. We report here a multiomics systems science approach so as to map interindividual variability in susceptibility to recurrent cardiovascular events. First, we performed data and text mining through a mixed-methods content analysis to select genomic variants, 10 single nucleotide polymorphisms, and microRNAs (miR-10a, miR-21, and miR-20a), minimizing bias in candidate marker selection. Next, we validated our

Malignant gliomas constitute a complex disease phenotype that demands optimum decision-making as they are highly heterogeneous. Such inter-individual variability also renders optimum patient stratification extremely difficult. microRNA (hsa-miR-20a, hsa-miR-21, hsa-miR-21) expression levels were determined by RT-qPCR, upon FFPE tissue sample collection of glioblastoma multiforme patients (n = 37). In silico validation was then performed through discriminant analysis. Immunohistochemistry images from biopsy material were utilized by a hybrid deep learning system to further cross validate the distinctive capability of patient risk groups. Our standard-of-care treated patient cohort demonstrates no age- or sex- dependence. The expression values of the 3-miRNA signature between the low- (OS > 12 months) and high-risk (OS < 12 months) groups yield a p-value of 12 months) vs. high-risk (OS < 12 months) glioblastoma multiforme patients. Our 3-microRNA signature (hsa-miR-20a, hsa-miR-21, hsa-miR-10a) may further empower glioblastoma multiforme prognostic evaluation in clinical practice and enrich drug repurposing pipelines.

Genomic medicine aims to improve health using the individual genomic data of people to inform care. While clinical utility of genomic medicine in many monogenic, Mendelian disorders is amply demonstrated, clinical utility is less evident in polygenic traits, e.g., coronary artery disease or breast cancer. Polygenic risk scores (PRS) are subsets of individual genotypes designed to capture heritability of common traits, and hence to allow the stratification of risk of the trait in a population. We systematically reviewed the PubMed database for unequivocal evidence of clinical utility of polygenic risk scores, using stringent inclusion and exclusion criteria. While we identified studies demonstrating clinical validity in conditions where medical intervention based on a PRS is likely to benefit patient outcome, we did not identify a single study demonstrating unequivocally such a benefit, i.e. clinical utility. We conclude that while the routine use of PRSs hold great promise, translational research is still needed before they should enter mainstream clinical practice.

Aim: Hemoglobinopathies exhibit a remarkable phenotypic diversity that restricts any safe association between molecular pathology and clinical outcomes. Patients & methods: Herein, we explored the role of genes involved in the nitric oxide biosynthesis and signaling pathway, implicated in the increase of fetal hemoglobin levels and response to hydroxyurea treatment, in 119 Hellenic patients with β-type hemoglobinopathies. Results: We show that two ASS1 genomic variants (namely, rs10901080 and rs10793902) can serve as pharmacogenomic biomarkers to predict hydroxyurea treatment efficacy in sickle cell disease/β-thalassemia compound heterozygous patients. Conclusion: These markers may exert their effect by inducing nitric oxide biosynthesis, either via altering splicing and/or miRNA binding, as predicted by in silico analysis, and ultimately, increase γ-globin levels, via guanylyl cyclase targeting.