BACKGROUND: The field of pharmacogenomics focuses on the way a person's genome affects his or her response to a certain dose of a specified medication. The main aim is to utilize this information to guide and personalize the treatment in a way that maximizes the clinical benefits and minimizes the risks for the patients, thus fulfilling the promises of personalized medicine. Technological advances in genome sequencing, combined with the development of improved computational methods for the efficient analysis of the huge amount of generated data, have allowed the fast and inexpensive sequencing of a patient's genome, hence rendering its incorporation into clinical routine practice a realistic possibility. METHODS: This study exploited thoroughly characterized in functional level SNVs within genes involved in drug metabolism and transport, to train a classifier that would categorize novel variants according to their expected effect on protein functionality. This categorization is based on the available in silico prediction and/or conservation scores, which are selected with the use of recursive feature elimination process. Toward this end, information regarding 190 pharmacovariants was leveraged, alongside with 4 machine learning algorithms, namely AdaBoost, XGBoost, multinomial logistic regression, and random forest, of which the performance was assessed through 5-fold cross validation. RESULTS: All models achieved similar performance toward making informed conclusions, with RF model achieving the highest accuracy (85%, 95% CI: 0.79, 0.90), as well as improved overall performance (precision 85%, sensitivity 84%, specificity 94%) and being used for subsequent analyses. When applied on real world WGS data, the selected RF model identified 2 missense variants, expected to lead to decreased function proteins and 1 to increased. As expected, a greater number of variants were highlighted when the approach was used on NGS data derived from targeted resequencing of coding regions. Specifically, 71 variants (out of 156 with sufficient annotation information) were classified as to "Decreased function," 41 variants as "No" function proteins, and 1 variant in "Increased function." CONCLUSION: Overall, the proposed RF-based classification model holds promise to lead to an extremely useful variant prioritization and act as a scoring tool with interesting clinical applications in the fields of pharmacogenomics and personalized medicine.

BACKGROUND: Previous studies indicated that lncRNA taurine upregulated gene 1 (TUG1) played essential roles in human cancers. This study aimed to investigate its function in infantile hemangioma (IH). METHODS: A total of 30 pairs of clinical infantile specimens were used in this study. The expression of TUG1 in IH tissues was assessed by quantitative reverse transcriptase PCR (qRT-PCR). Two short hairpin RNA targeting TUG1 (sh-TUG1-1 and sh-TUG1-2) were transfected into hemangioma-derived endothelial cells, HemECs, to block its expression. The effects of TUG1 on HemECs were evaluated by Cell Counting Kit-8 (CCK-8), colony formation assay, wound healing assay, and Transwell assay. The underlying molecular mechanism of TUG1 was investigated by Starbase prediction and luciferase reporter assay and further determined by loss- and gain-of-function approaches. In addition, the role of TUG1 on tumorigenesis of HemECs was confirmed in an in vivo mouse model. RESULTS: TUG1 was significantly upregulated in infant hemangioma tissues compared with normal adjacent subcutaneous tissues. The loss- and gain-of-function approaches indicated that TUG1 overexpression promoted proliferation, migration, and invasion of HemECs in vitro, and TUG1 knockdown inhibited the tumorigenesis of HemECs in vivo. Specifically, TUG1 could compete with IGFBP5 for miR137 binding. Rescue experiments further confirmed the role of the TUG1/miR137/IGFBP5 axis in HemECs. CONCLUSION: TUG1 was closely associated with the progression of IH by regulating the miR-137/IGFBP5 axis, which might be a potential target for IH treatment.

BACKGROUND: The diagnostic process for uncommon disorders with similar manifestations is complicated and requires newer technology, like gene sequencing for a correct diagnosis. MAIN BODY: We described two brothers clinically diagnosed with Carpenter syndrome, which is a condition characterized by the premature fusion of certain skull bones (craniosynostosis), abnormalities of the fingers and toes, and other developmental problems, for which they underwent craniotomies. However, whole exome sequencing analysis concluded a novel pathological variation in the ATRX chromatin remodeler gene and protein remodeling demonstrated structural variations that decreased the function, giving a completely different diagnosis to these patients. CONCLUSION: Our study focuses on the importance of using newer technologies, such as whole exome sequencing analysis, in patients with ambiguous phenotypes.

BACKGROUND: Low-pass sequencing (LPS) has been extensively investigated for applicability to various genetic studies due to its advantages over genotype array data including cost-effectiveness. Predicting the risk of complex diseases such as Parkinson's disease (PD) using polygenic risk score (PRS) based on the genetic variations has shown decent prediction accuracy. Although ultra-LPS has been shown to be effective in PRS calculation, array data has been favored to the majority of PRS analysis, especially for PD. RESULTS: Using eight high-coverage WGS, we assessed imputation approaches for downsampled LPS data ranging from 0.5 × to 7.0 × . We demonstrated that uncertain genotype calls of LPS diminished imputation accuracy, and an imputation approach using genotype likelihoods was plausible for LPS. Additionally, comparing imputation accuracies between LPS and simulated array illustrated that LPS had higher accuracies particularly at rare frequencies. To evaluate ultra-low coverage data in PRS calculation for PD, we prepared low-coverage WGS and genotype array of 87 PD cases and 101 controls. Genotype imputation of array and downsampled LPS were conducted using a population-specific reference panel, and we calculated risk scores based on the PD-associated SNPs from an East Asian meta-GWAS. The PRS models discriminated cases and controls as previously reported when both LPS and genotype array were used. Also strong correlations in PRS models for PD between LPS and genotype array were discovered. CONCLUSIONS: Overall, this study highlights the potentials of LPS under 1.0 × followed by genotype imputation in PRS calculation and suggests LPS as attractive alternatives to genotype array in the area of precision medicine for PD.

Erratum in Hum Genomics. 2021 Sep 18;15(1):59.

Whether microRNAs (miRNAs) from plasma exosomes might be dysregulated in patients with depression, especially treatment-resistant depression (TRD), remains unclear, based on study of which novel biomarkers and therapeutic targets could be discovered. To this end, a small sample study was performed by isolation of plasma exosomes from patients with TRD diagnosed by Hamilton scale. In this study, 4 peripheral plasma samples from patients with TRD and 4 healthy controls were collected for extraction of plasma exosomes. Exosomal miRNAs were analyzed by miRNA sequencing, followed by image collection, expression difference analysis, target gene GO enrichment analysis, and KEGG pathway enrichment analysis. Compared with the healthy controls, 2 miRNAs in the plasma exosomes of patients with TRD showed significant differences in expression, among which has-miR-335-5p were significantly upregulated and has-miR-1292-3p were significantly downregulated. Go and KEGG analysis showed that dysregulated miRNAs affect postsynaptic density and axonogenesis as well as the signaling pathway of axon formation and cell growths. The identification of these miRNAs and their target genes may provide novel biomarkers for improving diagnosis accuracy and treatment effectiveness of TRD.

BACKGROUND: The global development and advancement of genomic medicine in the recent decade has accelerated the implementation of personalized medicine (PM) and pharmacogenomics (PGx) into clinical practice, while catalyzing the emergence of genetic testing (GT) with relevant ethical, legal, and social implications (ELSI). RESULTS: The perception of university undergraduates with regards to PM and PGx was investigated, and 80% of undergraduates valued PM as a promising healthcare model with 66% indicating awareness of personal genome testing companies. When asked about the curriculum design towards PM and PGx, compared to undergraduates in non-medically related curriculum, those studying in medically related curriculum had an adjusted 7.2 odds of perceiving that their curriculum was well-designed for learning PGx (95% CI 3.6-14.6) and a 3.7 odds of perceiving that PGx was important in their study (95% CI 2.0-6.8). Despite this, only 16% of medically related curriculum undergraduates would consider embarking on future education on PM. When asked about their perceptions on GT, 60% rated their genetic knowledge as "School Biology" level or below while 76% would consider undergoing a genetic test. As for ELSI, 75% of undergraduates perceived that they were aware of ethical issues of GT in general, particularly on "Patient Privacy" (80%) and "Data Confidentiality" (68%). Undergraduates were also asked about their perceived reaction upon receiving an unfavorable result from GT, and over half of the participants perceived that they would feel "helpless or pessimistic" (56%), "inadequate or different" (59%), and "disadvantaged at job seeking" (59%), while older undergraduates had an adjusted 2.0 odds of holding the latter opinion (95% CI 1.1-3.5), compared to younger undergraduates. CONCLUSION: Hong Kong undergraduates showed a high awareness of PM but insufficient genetic knowledge and low interest in pursuing a career towards PM. They were generally aware of ethical issues of GT and especially concerned about patient privacy and data confidentiality. There was a predominance of pessimistic views towards unfavorable testing results. This study calls for the attention to evaluate education and talent development on genomics, and update existing legal frameworks on genetic testing in Hong Kong.

BACKGROUND: Skin cutaneous melanoma (SKCM) is one of the most highly prevalent and complicated malignancies. Glycolysis and cholesterogenesis pathways both play important roles in cancer metabolic adaptations. The main aims of this study are to subtype SKCM based on glycolytic and cholesterogenic genes and to build a clinical outcome predictive algorithm based on the subtypes. METHODS: A dataset with 471 SKCM specimens was downloaded from The Cancer Genome Atlas (TCGA) database. We extracted and clustered genes from the Molecular Signatures Database v7.2 and acquired co-expressed glycolytic and cholesterogenic genes. We then subtyped the SKCM samples and validated the efficacy of subtypes with respect to simple nucleotide variations (SNVs), copy number variation (CNV), patients' survival statuses, tumor microenvironment, and proliferation scores. We also constructed a risk score model based on metabolic subclassification and verified the model using validating datasets. Finally, we explored potential drugs for high-risk SKCM patients. RESULTS: SKCM patients were divided into four subtype groups: glycolytic, cholesterogenic, mixed, and quiescent subgroups. The glycolytic subtype had the worst prognosis and MGAM SNV extent. Compared with the cholesterogenic subgroup, the glycolytic subgroup had higher rates of DDR2 and TPR CNV and higher proliferation scores and MK167 expression levels, but a lower tumor purity proportion. We constructed a forty-four-gene predictive signature and identified MST-321, SB-743921, Neuronal Differentiation Inducer III, romidepsin, vindesine, and YM-155 as high-sensitive drugs for high-risk SKCM patients. CONCLUSIONS: Subtyping SKCM patients via glycolytic and cholesterogenic genes was effective, and patients in the glycolytic-gene enriched group were found to have the worst outcome. A robust prognostic algorithm was developed to enhance clinical decisions in relation to drug administration.

BACKGROUND: Familial transthyretin (TTR) amyloidosis (ATTR) is an autosomal dominant disease with significant phenotypic heterogeneity. Its prevalence in Saudi Arabia has not previously been investigated. An existing exome variant database of Saudi individuals, sequenced to globally investigate rare diseases in the population, was mined for TTR variants and filtered for missense mutations resulting in single amino acid changes. A total of 13,906 Saudi exomes from unrelated individuals were analyzed blindly. RESULTS: Three TTR variants known to be associated with ATTR amyloidosis were identified. Additionally, three novel TTR mutations were identified. Structural analysis of the three novel variants suggests that at least two could be amyloidogenic. The most common variant associated with amyloidosis was p.Val142Ile (allele frequency 0.001). Further investigation of these variants and their translation to clinical practice may help to diagnose, monitor, and manage patients with ATTR amyloidosis. CONCLUSION: Multiple TTR variants potentially associated with systemic ATTR amyloidosis were identified in the Saudi population. Early diagnosis and intervention, facilitated by familial genetic testing of patients with ATTR amyloidosis, may benefit in the management of this disease. Early diagnosis could be enhanced through inclusion of ATTR variants in existing population-based screening programs.