Identification of plasma lipid biomarkers for prostate cancer by lipidomics and bioinformatics

Background: Lipids have critical functions in cellular energy storage, structure and signaling. Many individual lipid molecules have been associated with the evolution of prostate cancer; however, none of them has been approved to be used as a biomarker. The aim of this study is to identify lipid molecules from hundreds plasma apparent lipid species as biomarkers for diagnosis of prostate cancer. Methodology/Principal Findings: Using lipidomics, lipid profiling of 390 individual apparent lipid species was performed on 141 plasma samples from 105 patients with prostate cancer and 36 male controls. High throughput data generated from lipidomics were analyzed using bioinformatic and statistical methods. From 390 apparent lipid species, 35 species were demonstrated to have potential in differentiation of prostate cancer. Within the 35 species, 12 were identified as individual plasma lipid biomarkers for diagnosis of prostate cancer with a sensitivity above 80%, specificity above 50% and accuracy above 80%. Using top 15 of 35 potential biomarkers together increased predictive power dramatically in diagnosis of prostate cancer with a sensitivity of 93.6%, specificity of 90.1% and accuracy of 97.3%. Principal component analysis (PCA) and hierarchical clustering analysis (HCA) demonstrated that patient and control populations were visually separated by identified lipid biomarkers. RandomForest and 10-fold cross validation analyses demonstrated that the identified lipid biomarkers were able to predict unknown populations accurately, and this was not influenced by patient's age and race. Three out of 13 lipid classes, phosphatidylethanolamine (PE), ether-linked phosphatidylethanolamine (ePE) and ether-linked phosphatidylcholine (ePC) could be considered as biomarkers in diagnosis of prostate cancer. Conclusions/Significance: Using lipidomics and bioinformatic and statistical methods, we have identified a few out of hundreds plasma apparent lipid molecular species as biomarkers for diagnosis of prostate cancer with a high sensitivity, specificity and accuracy

Characterization of Changes in Serum Anti-Glycan Antibodies in Crohn's Disease – a Longitudinal Analysis

INTRODUCTION: Anti-glycan antibodies are a promising tool for differential diagnosis and disease stratification of patients with Crohn's disease (CD). We longitudinally assessed level and status changes of anti-glycan antibodies over time in individual CD patients as well as determinants of this phenomenon. METHODS: 859 serum samples derived from a cohort of 253 inflammatory bowel disease (IBD) patients (207 CD, 46 ulcerative colitis (UC)) were tested for the presence of anti-laminarin (Anti-L), anti-chitin (Anti-C), anti-chitobioside (ACCA), anti-laminaribioside (ALCA), anti-mannobioside (AMCA) and anti-Saccharomyces cerevisiae (gASCA) antibodies by ELISA. All patients had at least two and up to eleven serum samples taken during the disease course. RESULTS: Median follow-up time for CD was 17.4 months (Interquartile range (IQR) 8.0, 31.6 months) and for UC 10.9 months (IQR 4.9, 21.0 months). In a subgroup of CD subjects marked changes in the overall immune response (quartile sum score) and levels of individual markers were observed over time. The marker status (positive versus negative) remained widely stable. Neither clinical phenotype nor NOD2 genotype was associated with the observed fluctuations. In a longitudinal analysis neither changes in disease activity nor CD behavior led to alterations in the levels of the glycan markers. The ability of the panel to discriminate CD from UC or its association with CD phenotypes remained stable during follow-up. In the serum of UC patients neither significant level nor status changes were observed. CONCLUSIONS: While the levels of anti-glycan antibodies fluctuate in a subgroup of CD patients the antibody status is widely stable over time

Common and rare variant association analyses in amyotrophic lateral sclerosis identify 15 risk loci with distinct genetic architectures and neuron-specific biology

Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease with a lifetime risk of one in 350 people and an unmet need for disease-modifying therapies. We conducted a cross-ancestry genome-wide association study (GWAS) including 29,612 patients with ALS and 122,656 controls, which identified 15 risk loci. When combined with 8,953 individuals with whole-genome sequencing (6,538 patients, 2,415 controls) and a large cortex-derived expression quantitative trait locus (eQTL) dataset (MetaBrain), analyses revealed locus-specific genetic architectures in which we prioritized genes either through rare variants, short tandem repeats or regulatory effects. ALS-associated risk loci were shared with multiple traits within the neurodegenerative spectrum but with distinct enrichment patterns across brain regions and cell types. Of the environmental and lifestyle risk factors obtained from the literature, Mendelian randomization analyses indicated a causal role for high cholesterol levels. The combination of all ALS-associated signals reveals a role for perturbations in vesicle-mediated transport and autophagy and provides evidence for cell-autonomous disease initiation in glutamatergic neurons. A cross-ancestry genome-wide association meta-analysis of amyotrophic lateral sclerosis (ALS) including 29,612 patients with ALS and 122,656 controls identifies 15 risk loci with distinct genetic architectures and neuron-specific biology