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:MAP Stool samples for Microbiome Core - CMI. Human stool samples analyzed via untargeted metabolomics (LC/MS) using Thermo Q-Exactive and Polar C18 column. Positive polarity acquisition of LC-MS/MS.
Project description:Sharpton whole stool samples for Microbiome Core - CMI. Human stool samples analyzed via untargeted metabolomics (LC/MS) using Thermo Q-Exactive and Polar C18 column. Positive polarity acquisition of LC-MS/MS.
Project description:Previously, we published a dataset of human blood plasma and serum samples of 10 healthy males and 10 healthy females, fractionated on a set of sorbents (cation exchange Toyopearl CM-650M, CM Bio-Gel A, SP Sephadex C-25 and anion exchange QAE Sephadex A-25) and analyzed by LC-MS/MS individually and pooled in equal amounts (Supplementary Table S1, Sheet 1) [33]. The mass spectrometry peptidomics data was deposited to the ProteomeXchange Consortium via the PRIDE partner repository (dataset identifiers PXD008141 and 10.6019/PXD008141). Direct download link: http://www.ebi.ac.uk/pride/archive/projects/PXD008141. We analyzed this dataset again within this work. The detailed information about the dataset of blood plasma/serum samples of 20 healthy donors fractionated on a set of sorbents is available in the original paper [33], including the clinical parameters of the donors, sample collection, plasma/serum fractionation, peptide extraction and LC-MS/MS analysis. 33. Arapidi, G. et al. Peptidomics dataset: Blood plasma and serum samples of healthy donors fractionated on a set of chromatography sorbents. Data Brief 18, 1204–1211 (2018).