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Translating vitamin D transcriptomics to clinical evidence: Analysis of data in asthma and chronic obstructive pulmonary disease, followed by clinical data meta-analysis.
Malliaraki, N, Lakiotaki, K, Vamvoukaki, R, Notas, G, Tsamardinos, I, Kampa, M, Castanas, E
The Journal of steroid biochemistry and molecular biology. 2020;:105505
Abstract
Vitamin D (VitD) continues to trigger intense scientific controversy, regarding both its bi ological targets and its supplementation doses and regimens. In an effort to resolve this dispute, we mapped VitD transcriptome-wide events in humans, in order to unveil shared patterns or mechanisms with diverse pathologies/tissue profiles and reveal causal effects between VitD actions and specific human diseases, using a recently developed bioinformatics methodology. Using the similarities in analyzed transcriptome data (c-SKL method), we validated our methodology with osteoporosis as an example and further analyzed two other strong hits, specifically chronic obstructive pulmonary disease (COPD) and asthma. The latter revealed no impact of VitD on known molecular pathways. In accordance to this finding, review and meta-analysis of published data, based on an objective measure (Forced Expiratory Volume at one second, FEV1%) did not further reveal any significant effect of VitD on the objective amelioration of either condition. This study may, therefore, be regarded as the first one to explore, in an objective, unbiased and unsupervised manner, the impact of VitD levels and/or interventions in a number of human pathologies.
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Metabolomics Signatures in Type 2 Diabetes: A Systematic Review and Integrative Analysis.
Sun, Y, Gao, HY, Fan, ZY, He, Y, Yan, YX
The Journal of clinical endocrinology and metabolism. 2020;(4)
Abstract
OBJECTIVE Metabolic signatures have emerged as valuable signaling molecules in the biochemical process of type 2 diabetes (T2D). To summarize and identify metabolic biomarkers in T2D, we performed a systematic review and meta-analysis of the associations between metabolites and T2D using high-throughput metabolomics techniques. METHODS We searched relevant studies from MEDLINE (PubMed), Embase, Web of Science, and Cochrane Library as well as Chinese databases (Wanfang, Vip, and CNKI) inception through 31 December 2018. Meta-analysis was conducted using STATA 14.0 under random effect. Besides, bioinformatic analysis was performed to explore molecule mechanism by MetaboAnalyst and R 3.5.2. RESULTS Finally, 46 articles were included in this review on metabolites involved amino acids, acylcarnitines, lipids, carbohydrates, organic acids, and others. Results of meta-analysis in prospective studies indicated that isoleucine, leucine, valine, tyrosine, phenylalanine, glutamate, alanine, valerylcarnitine (C5), palmitoylcarnitine (C16), palmitic acid, and linoleic acid were associated with higher T2D risk. Conversely, serine, glutamine, and lysophosphatidylcholine C18:2 decreased risk of T2D. Arginine and glycine increased risk of T2D in the Western countries subgroup, and betaine was negatively correlated with T2D in nested case-control subgroup. In addition, slight improvements in T2D prediction beyond traditional risk factors were observed when adding these metabolites in predictive analysis. Pathway analysis identified 17 metabolic pathways may alter in the process of T2D and metabolite-related genes were also enriched in functions and pathways associated with T2D. CONCLUSIONS Several metabolites and metabolic pathways associated with T2D have been identified, which provide valuable biomarkers and novel targets for prevention and drug therapy.
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Molecular mechanism and role of microRNA-93 in human cancers: A study based on bioinformatics analysis, meta-analysis, and quantitative polymerase chain reaction validation.
Gao, Y, Deng, K, Liu, X, Dai, M, Chen, X, Chen, J, Chen, J, Huang, Y, Dai, S, Chen, J
Journal of cellular biochemistry. 2019;(4):6370-6383
Abstract
INTRODUCTION Currently, studies have shown that microRNA-93 (miR-93) can be an oncogene or a tumor suppressor in different kinds of cancers. The role of miR-93 in human cancers is inconsistent and the underlying mechanism on the aberrant expression of miR-93 is complicated. METHODS We first conducted gene enrichment analysis to give insight into the prospective mechanism of miR-93. Second, we performed a meta-analysis to evaluate the clinical value of miR-93. Finally, a validation test based on quantitative polymerase chain reaction (qPCR) was performed to further investigate the role of miR-93 in pan-cancer. RESULTS Gene Ontology (GO) enrichment analysis results showed that the target genes of miR-93 were closely related to transcription, and MAPK1, RBBP7 and Smad7 became the hub genes. In the diagnostic meta-analysis, the overall sensitivity, specificity, and area under the curve were 0.76 (0.64-0.85), 0.82 (0.64-0.92), and 0.85 (0.82-0.88), respectively, which suggested that miR-93 had excellent performance on the diagnosis for human cancers. In the prognostic meta-analysis, dysregulated miR-93 was found to be associated with poor OS in cancer patients. In the qPCR validation test, the serum levels of miR-93 were upregulated in breast cancer, breast hyperplasia, lung cancer, chronic obstructive pulmonary disease, nasopharyngeal cancer, hepatocellular cancer, gastric ulcer, endometrial cancer, esophageal cancer, laryngeal cancer, and prostate cancer compared with healthy controls. CONCLUSIONS miR-93 could act as an effective diagnostic and prognostic factor for cancer patients. Its clinical value for cancer early diagnosis and survival prediction is promising.
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1000 Genomes-based meta-analysis identifies 10 novel loci for kidney function.
Gorski, M, van der Most, PJ, Teumer, A, Chu, AY, Li, M, Mijatovic, V, Nolte, IM, Cocca, M, Taliun, D, Gomez, F, et al
Scientific reports. 2017;:45040
Abstract
HapMap imputed genome-wide association studies (GWAS) have revealed >50 loci at which common variants with minor allele frequency >5% are associated with kidney function. GWAS using more complete reference sets for imputation, such as those from The 1000 Genomes project, promise to identify novel loci that have been missed by previous efforts. To investigate the value of such a more complete variant catalog, we conducted a GWAS meta-analysis of kidney function based on the estimated glomerular filtration rate (eGFR) in 110,517 European ancestry participants using 1000 Genomes imputed data. We identified 10 novel loci with p-value < 5 × 10-8 previously missed by HapMap-based GWAS. Six of these loci (HOXD8, ARL15, PIK3R1, EYA4, ASTN2, and EPB41L3) are tagged by common SNPs unique to the 1000 Genomes reference panel. Using pathway analysis, we identified 39 significant (FDR < 0.05) genes and 127 significantly (FDR < 0.05) enriched gene sets, which were missed by our previous analyses. Among those, the 10 identified novel genes are part of pathways of kidney development, carbohydrate metabolism, cardiac septum development and glucose metabolism. These results highlight the utility of re-imputing from denser reference panels, until whole-genome sequencing becomes feasible in large samples.
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Genevestigator transcriptome meta-analysis and biomarker search using rice and barley gene expression databases.
Zimmermann, P, Laule, O, Schmitz, J, Hruz, T, Bleuler, S, Gruissem, W
Molecular plant. 2008;(5):851-7
Abstract
The wide-spread use of microarray technologies to study plant transcriptomes has led to important discoveries and to an accumulation of profiling data covering a wide range of different tissues, developmental stages, perturbations, and genotypes. Querying a large number of microarray experiments can provide insights that cannot be gained by analyzing single experiments. However, such a meta-analysis poses significant challenges with respect to data comparability and normalization, systematic sample annotation, and analysis tools. Genevestigator addresses these issues using a large curated expression database and a set of specifically developed analysis tools that are accessible over the internet. This combination has already proven to be useful in the area of plant research based on a large set of Arabidopsis data (Grennan, 2006). Here, we present the release of the Genevestigator rice and barley gene expression databases that contain quality-controlled and well annotated microarray experiments using ontologies. The databases currently comprise experiments from pathology, plant nutrition, abiotic stress, hormone treatment, genotype, and spatial or temporal analysis, but are expected to cover a broad variety of research areas as more experimental data become available. The transcriptome meta-analysis of the model species rice and barley is expected to deliver results that can be used for functional genomics and biotechnological applications in cereals.