BACKGROUND: Recent efforts in the field of nutritional science have allowed the discovery of disease-beating molecules within foods based on the commonality of bioactive food molecules to FDA-approved drugs. The pioneering work in this field used an unsupervised network propagation algorithm to learn the systemic-wide effect on the human interactome of 1962 FDA-approved drugs and a supervised algorithm to predict anticancer therapeutics using the learned representations. Then, a set of bioactive molecules within foods was fed into the model, which predicted molecules with cancer-beating potential.The employed methodology consisted of disjoint unsupervised feature generation and classification tasks, which can result in sub-optimal learned drug representations with respect to the classification task. Additionally, due to the disjoint nature of the tasks, the employed approach proved cumbersome to optimize, requiring testing of thousands of hyperparameter combinations and significant computational resources.To overcome the technical limitations highlighted above, we represent each drug as a graph (human interactome) with its targets as binary node features on the graph and formulate the problem as a graph classification task. To solve this task, inspired by the success of graph neural networks in graph classification problems, we use an end-to-end graph neural network model operating directly on the graphs, which learns drug representations to optimize model performance in the prediction of anticancer therapeutics. RESULTS: The proposed model outperforms the baseline approach in the anticancer therapeutic prediction task, achieving an F1 score of 67.99%±2.52% and an AUPR of 73.91%±3.49%. It is also shown that the model is able to capture knowledge of biological pathways to predict anticancer molecules based on the molecules' effects on cancer-related pathways. CONCLUSIONS: We introduce an end-to-end graph convolutional model to predict cancer-beating molecules within food. The introduced model outperforms the existing baseline approach, and shows interpretability, paving the way to the future of a personalized nutritional science approach allowing the development of nutrition strategies for cancer prevention and/or therapeutics.

BACKGROUND: Mitochondrial genome copy number (MT-CN) varies among humans and across tissues and is highly heritable, but its causes and consequences are not well understood. When measured by bulk DNA sequencing in blood, MT-CN may reflect a combination of the number of mitochondria per cell and cell-type composition. Here, we studied MT-CN variation in blood-derived DNA from 19184 Finnish individuals using a combination of genome (N = 4163) and exome sequencing (N = 19034) data as well as imputed genotypes (N = 17718). RESULTS: We identified two loci significantly associated with MT-CN variation: a common variant at the MYB-HBS1L locus (P = 1.6 × 10-8), which has previously been associated with numerous hematological parameters; and a burden of rare variants in the TMBIM1 gene (P = 3.0 × 10-8), which has been reported to protect against non-alcoholic fatty liver disease. We also found that MT-CN is strongly associated with insulin levels (P = 2.0 × 10-21) and other metabolic syndrome (metS)-related traits. Using a Mendelian randomization framework, we show evidence that MT-CN measured in blood is causally related to insulin levels. We then applied an MT-CN polygenic risk score (PRS) derived from Finnish data to the UK Biobank, where the association between the PRS and metS traits was replicated. Adjusting for cell counts largely eliminated these signals, suggesting that MT-CN affects metS via cell-type composition. CONCLUSION: These results suggest that measurements of MT-CN in blood-derived DNA partially reflect differences in cell-type composition and that these differences are causally linked to insulin and related traits.

For decades, various strategies have been proposed to solve the enigma of hemoglobinopathies, especially severe cases. However, most of them seem to be lagging in terms of effectiveness and safety. So far, the most prevalent and promising treatment options for patients with β-types hemoglobinopathies, among others, predominantly include drug treatment and gene therapy. Despite the significant improvements of such interventions to the patient's quality of life, a variable response has been demonstrated among different groups of patients and populations. This is essentially due to the complexity of the disease and other genetic factors. In recent years, a more in-depth understanding of the molecular basis of the β-type hemoglobinopathies has led to significant upgrades to the current technologies, as well as the addition of new ones attempting to elucidate these barriers. Therefore, the purpose of this article is to shed light on pharmacogenomics, gene addition, and genome editing technologies, and consequently, their potential use as direct and indirect genome-based interventions, in different strategies, referring to drug and gene therapy. Furthermore, all the latest progress, updates, and scientific achievements for patients with β-type hemoglobinopathies will be described in detail.

BACKGROUND: Trisomy 18 syndrome (Edwards syndrome, ES) is a type of aneuploidy caused by the presence of an extra chromosome 18. Aneuploidy is the leading cause of early pregnancy loss, intellectual disability, and multiple congenital anomalies. The research of trisomy 18 is progressing slowly, and the molecular characteristics of the disease mechanism and phenotype are still largely unclear. RESULTS: In this study, we used the commercial Chromium platform (10× Genomics) to perform sc-ATAC-seq to measure chromatin accessibility in 11,611 single umbilical cord blood cells derived from one trisomy 18 syndrome patient and one healthy donor. We obtained 13 distinct major clusters of cells and identified them as 6 human umbilical cord blood mononuclear cell types using analysis tool. Compared with the NC group, the ES group had a lower ratio of T cells to NK cells, the ratio of monocytes/DC cell population did not change significantly, and the ratio of B cell nuclear progenitor and megakaryocyte erythroid cells was higher. The differential genes of ME-0 are enriched in Human T cell leukemia virus 1 infection pathway, and the differential peak genes of ME-1 are enriched in apopotosis pathway. We found that CCNB2 and MCM3 may be vital to the development of trisomy 18. CCNB2 and MCM3, which have been reported to be essential components of the cell cycle and chromatin. CONCLUSIONS: We have identified 6 cell populations in cord blood. Disorder in megakaryocyte erythroid cells implicates trisomy 18 in perturbing fetal hematopoiesis. We identified a pathway in which the master differential regulatory pathway in the ME-0 cell population involves human T cell leukemia virus 1 infection, a pathway that is dysregulated in patients with trisomy 18 and which may increase the risk of leukemia in patients with trisomy 18. CCNB2 and MCM3 in progenitor may be vital to the development of trisomy 18. CCNB2 and MCM3, which have been reported to be essential components of the cell cycle and chromatin, may be related to chromosomal abnormalities in trisomy 18.

BACKGROUND: Acute myeloid leukemia (AML) is recognized as a hematological neoplasm with heterogenetic cytology and short-term outcome. HCP5 has been proven to be related with the pathogenesis of AML. However, the underlying mechanism of HCP5 in AML remains unclear. METHODS: Clinical profiles of AML patients were downloaded from TCGA and GTEx databases. LncBase and TargetScan online tools were utilized to predict potential targets, and dual-luciferase reporter assay was performed to verify the association between miR-1291 and HCP5 or PIK3R5. Cell Counting Kit 8 and flow cytometry tests were implemented to evaluate the effects of HCP5/miR-1291/PIK3R5 axis in AML cells. Quantitative RT-PCR and Western blot were conducted to detect the expression levels of genes. RESULTS: HCP5 and PIK3R5 were significantly increased in AML tissue samples compared with healthy controls. HCP5 facilitated AML cells viability and inhibited apoptosis. There was a positive relationship between HCP5 and PIK3R5, but miR-1291 negatively regulated PIK3R5. Overexpression of PIK3R5 enhanced the promoting effect of HCP5 in the development of AML, while weakened the suppression of miR-1291 to AML progression. CONCLUSION: Our findings manifested that HCP5 was remarkably upregulated in AML and upregulated HCP5 promoted the malignant behaviors of AML cells by mediating miR-1291/PIK3R5 axis, which would provide a new insight for the treatment of AML.

BACKGROUND: Liver cancer is one of the most common cancers and causes of cancer death worldwide. The objective was to elucidate novel hub genes which were benefit for diagnosis, prognosis, and targeted therapy in liver cancer via integrated analysis. METHODS: GSE84402, GSE101685, and GSE112791 were filtered from the Gene Expression Omnibus (GEO). Differentially expressed genes (DEGs) were identified by using the GEO2R. The GO and KEGG pathway of DEGs were analyzed in the DAVID. PPI and TF network of the DEGs were constructed by using the STRING, TRANSFAC, and Harmonizome. The relationship between hub genes and prognoses in liver cancer was analyzed in UALCAN based on The Cancer Genome Atlas (TCGA). The diagnostic value of hub genes was evaluated by ROC. The relationship between hub genes and tumor-infiltrate lymphocytes was analyzed in TIMER. The protein levels of hub genes were verified in HPA. The interaction between the hub genes and the drug were identified in DGIdb. RESULTS: In total, 108 upregulated and 60 downregulated DEGs were enriched in 148 GO terms and 20 KEGG pathways. The mRNA levels and protein levels of CDK1, HMMR, PTTG1, and TTK were higher in liver cancer tissues compared to normal tissues, which showed excellent diagnostic and prognostic value. CDK1, HMMR, PTTG1, and TTK were positively correlated with tumor-infiltrate lymphocytes, which might involve tumor immune response. The CDK1, HMMR, and TTK had close interaction with anticancer agents. CONCLUSIONS: The CDK1, HMMR, PTTG1, and TTK were hub genes in liver cancer; hence, they might be potential biomarkers for diagnosis, prognosis, and targeted therapy of liver cancer.

BACKGROUND: Genetic disposition is considered critical for identifying subjects at high risk for disease development. Investigating disease-causing and high and low expressed genes can support finding the root causes of uncertainties in patient care. However, independent and timely high-throughput next-generation sequencing data analysis is still a challenge for non-computational biologists and geneticists. RESULTS: In this manuscript, we present a findable, accessible, interactive, and reusable (FAIR) bioinformatics platform, i.e., GVViZ (visualizing genes with disease-causing variants). GVViZ is a user-friendly, cross-platform, and database application for RNA-seq-driven variable and complex gene-disease data annotation and expression analysis with a dynamic heat map visualization. GVViZ has the potential to find patterns across millions of features and extract actionable information, which can support the early detection of complex disorders and the development of new therapies for personalized patient care. The execution of GVViZ is based on a set of simple instructions that users without a computational background can follow to design and perform customized data analysis. It can assimilate patients' transcriptomics data with the public, proprietary, and our in-house developed gene-disease databases to query, easily explore, and access information on gene annotation and classified disease phenotypes with greater visibility and customization. To test its performance and understand the clinical and scientific impact of GVViZ, we present GVViZ analysis for different chronic diseases and conditions, including Alzheimer's disease, arthritis, asthma, diabetes mellitus, heart failure, hypertension, obesity, osteoporosis, and multiple cancer disorders. The results are visualized using GVViZ and can be exported as image (PNF/TIFF) and text (CSV) files that include gene names, Ensembl (ENSG) IDs, quantified abundances, expressed transcript lengths, and annotated oncology and non-oncology diseases. CONCLUSIONS: We emphasize that automated and interactive visualization should be an indispensable component of modern RNA-seq analysis, which is currently not the case. However, experts in clinics and researchers in life sciences can use GVViZ to visualize and interpret the transcriptomics data, making it a powerful tool to study the dynamics of gene expression and regulation. Furthermore, with successful deployment in clinical settings, GVViZ has the potential to enable high-throughput correlations between patient diagnoses based on clinical and transcriptomics data.

BACKGROUND AND PURPOSE: Shoulder morbidity following breast cancer treatment is multifactorial. Despite several treatment- and patient-related factors being implicated, unexplained inter-individual variability exists in the development of such morbidity. Given the paucity of relavant genetic studies, we investigate the role of polymorphisms in candidate proteoglycan genes. PATIENTS AND METHODS: We conducted a cross-sectional study on 254 South African breast cancer survivors, to evaluate associations between shoulder pain/disability and ten single nucleotide polymorphisms (SNPs) within four proteoglycan genes: ACAN (rs1126823 G>A, rs1516797 G>T, rs2882676 A>C); BGN (rs1042103 G>A, rs743641 A>T, rs743642 G>T); DCN rs516115 C>T; and VCAN (rs11726 A>G, rs2287926 G>A, rs309559). Participants were grouped into no-low and moderate-high shoulder pain/disability based on total pain/disability scores: < 30 and ≥ 30, respectively using the Shoulder Pain and Disability Index (SPADI). RESULTS: The GG genotype of VCAN rs11726 was independently associated with an increased risk of being in the moderate-to-high shoulder pain (P = 0.005, OR = 2.326, 95% CI = 1.259-4.348) or disability (P = 0.011, OR = 2.439, 95% CI = 1.235-4.762) categories, after adjusting for participants' age. In addition, the T-T-G inferred allele combination of BGN (rs74364-rs743642)-VCAN rs11726 was associated with an increased risk of being in the moderate-to-high shoulder disability category (0 = 0.002, OR = 2.347, 95% CI = 1.215-4.534). CONCLUSION: Our study is first to report that VCAN rs11726, independently or interacting with BGN polymorphisms, is associated with shoulder pain or disability in breast cancer survivors. Whereas our findings suggest an involvement of proteoglycans in the etiology of shoulder pain/disability, further studies are recommended.