Project description:This is a prospective, multi-centered study to assess whether urine metabolomics can play a role in the screening of colorectal cancer (CRC). Urine samples will be collected from 1000 patients going through an established CRC screening program, and from a further 500 patients who already have a diagnosis of CRC. Using nuclear magnetic resonance (NMR) spectroscopy, the 1H NMR spectrum of urine samples will be analyzed for specific metabolites, and establish the metabolomic signature of colorectal cancer. The results from metabolomic urinalysis of this screening cohort will be compared with results from colonoscopy, histological descriptions, fecal occult blood testing (FOBT), and fecal immune testing (FIT) to assess the accuracy of urine metabolomics in identifying patients with polyps and malignancies. The urine metabolomic results from the colorectal cancer group will be correlated with operative, histological and clinical staging to define the role of urine metabolomics in assessing colorectal cancer type, location and stage. Additionally approximately 300 urine samples from breast cancer patients and 300 from prostate cancer patients will be collected to validate that the colorectal cancer signature is unique.
Project description:RNA sequencing allows for transcriptome-wide comparative studies of RNA levels, including gene expression, localization in subcellular compartments, or content of ribonucleoprotein complexes. It is often challenging, however, to separate real biological variation from technical artifacts arising from variable sample preparation, uneven contamination and difficulties normalizing sequencing counts between samples. These challenges are magnified in complex biochemical preparations, such as isolating polysomes to study translation. To address these challenges, we developed TILAC, an approach to compare RNA content between samples with internal controls and normalization. TILAC uses two different metabolic labels (4-thiouridine, s4U, and 6-thioguanisine, s6G) to differentially label RNA from each condition, allowing the samples to be pooled prior to downstream processing. TILAC uses nucleotide-recoding chemistry and sequencing to determine which RNAs are enriched in each sample. TILAC accurately identifies known changes in the transcriptome during RNA polymerase II inhibition and heat shock response. Using TILAC, we discovered a set of transcripts that are enriched in actively-translating ribosome complexes during stress, including MCM2 and DDX5, and verified their translational upregulation. These results demonstrate the power of TILAC to uncover differences between samples revealing new biology.
Project description:The pooled sample method is used in epigenomic research and expression analysis and is a cost-effective screening approach for small amounts of DNA. Evaluation of the pooled sample method in epigenomic studies is performed using the Illumina Infinium Methylation 450K BeadChip array; however, subsequent reports on the updated 850K array are lacking. A previous study demonstrated that the methylation levels obtained from individual samples were accurately replicated using pooled samples but did not address epigenome-wide association study (EWAS) statistics. The DNA quantification method, which is important for the homogeneous mixing of DNA in the pooled sample method, has since become fluorescence-based, and additional factors need to be considered including the resolution of batch effects of microarray chips and the heterogeneity of the cellular proportions from which the DNA samples are derived. In this study, four pooled samples were created from 44 individual samples, and EWAS statistics for differentially methylated positions (DMPs) and regions (DMRs) were conducted for individual samples and compared with the statistics obtained from the pooled samples.
Project description:With an estimated prevalence of about 30% in western countries non-alcoholic fatty liver disease (NAFLD) is a major public health issue [PMID: 18956290]. NAFLD is associated with the metabolic syndrome of insulin resistance, obesity, glucose intolerance. Although many studies are pointing to an induction of insulin resistance by NAFLD causality between both phenotypes is not fully clarified. Furthermore, mechanisms leading to strongly differing progression of NAFLD have to be elucidated which range from mild steatosis up to severe steatohepatitis. Steatohepatitis might even result in liver cirrhosis and hepatocellular carcinoma. Additional complexity is introduced into the understanding of the disease by recent studies providing evidence for a direct development of carcinoma from steatosis without the formerly assumed intermediary phase of cirrhosis. Here, we investigate liver samples from patients with varying severities of steatosis in an integrative approach employing transcriptomics, serum biomarker profling, metabolomics data and systems biology models. Total RNA obtained from hepatocytes derived from nine obese patients with distinct grades of steatosis. This dataset is part of the TransQST collection.
Project description:The paper "Metabolomic Machine Learning Predictor for Diagnosis and Prognosis of Gastric Cancer" addresses the need for non-invasive diagnostic tools for gastric cancer (GC). Traditional methods like endoscopy are invasive and expensive. The authors conducted a targeted metabolomics analysis of 702 plasma samples to develop machine learning models for GC diagnosis and prognosis. The diagnostic model, using 10 metabolites, achieved a sensitivity of 0.905, outperforming conventional protein marker-based methods. The prognostic model effectively stratified patients into risk groups, surpassing traditional clinical models.
I have successfully reproduced the diagnosis model from the paper. This machine learning-based system differentiates GC patients from non-GC controls using metabolomics data from plasma samples analyzed by liquid chromatography-mass spectrometry (LC-MS). The model focuses on 10 metabolites, including succinate, uridine, lactate, and serotonin. Employing LASSO regression and a random forest classifier, the model achieved an AUROC of 0.967, with a sensitivity of 0.854 and specificity of 0.926. This model significantly outperforms traditional diagnostic methods and underscores the potential of integrating machine learning with metabolomics for early GC detection and treatment.
Project description:With an estimated prevalence of about 30% in western countries non-alcoholic fatty liver disease (NAFLD) is a major public health issue [PMID: 18956290]. NAFLD is associated with the metabolic syndrome of insulin resistance, obesity, glucose intolerance. Although many studies are pointing to an induction of insulin resistance by NAFLD causality between both phenotypes is not fully clarified. Furthermore, mechanisms leading to strongly differing progression of NAFLD have to be elucidated which range from mild steatosis up to severe steatohepatitis. Steatohepatitis might even result in liver cirrhosis and hepatocellular carcinoma. Additional complexity is introduced into the understanding of the disease by recent studies providing evidence for a direct development of carcinoma from steatosis without the formerly assumed intermediary phase of cirrhosis. Here, we investigate liver samples from patients with varying severities of steatosis in an integrative approach employing transcriptomics, serum biomarker profling, metabolomics data and systems biology models.
Project description:The current test strategy for carcinogenicity consists initially of in vivo and in vitro genotoxicity tests. Non-genotoxic carcinogens do not directly induce DNA damage and, as such, go undetected under this test strategy. In a previous study we setup a comparison approach to categorize chemicals having similar modes of action, according to similarity in gene expression. In the current study we will investigate whether this comparison approach can be improved by omptimizing the concentration selection procedure and by testing a concentration range per chemical.