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:Untargeted LC-MS/MS data for plasma, serum, fecal, and saliva samples from patients with osteoarthritis before and after diet intervention.
Project description:To evoke further attention to the potential hazard of increasingly accumulative blue light exposure, we construct a series of in vivo Drosophila models employed for multi-omics analyses. This project includes the identification results of untargeted metabolome quantification by LC-MS/MS and the Input and m6A IP data of MeRIP-seq of w1118 male adult whole flies.
Project description:Purpose: We generated extensive transcriptional and proteomic profiles from a Her2-driven mouse model of breast cancer that closely recapitulates human breast cancer. This report makes these data publicly available in raw and processed forms, as a resource to the community. Importantly, we previously made biospecimens from this same mouse model freely available through a sample repository, so researchers can obtain samples to test biological hypotheses without the need of breeding animals and collecting biospecimens. Experimental design: Twelve datasets are available, encompassing 841 LC-MS/MS experiments (plasma and tissues) and 255 microarray analyses of multiple tissues (thymus, spleen, liver, blood cells, and breast). Cases and controls were rigorously paired to avoid bias. Results: In total, 18,880 unique peptides were identified (PeptideProphet peptide error rate ≤1%), with 3884 and 1659 non-redundant protein groups identified in plasma and tissue datasets, respectively. Sixty-one of these protein groups overlapped between cancer plasma and cancer tissue. Conclusions and clinical relevance: These data are of use for advancing our understanding of cancer biology, for software and quality control tool development, investigations of analytical variation in MS/MS data, and selection of proteotypic peptides for MRM-MS. The availability of these datasets will contribute positively to clinical proteomics.
Project description:Purpose: We generated extensive transcriptional and proteomic profiles from a Her2-driven mouse model of breast cancer that closely recapitulates human breast cancer. This report makes these data publicly available in raw and processed forms, as a resource to the community. Importantly, we previously made biospecimens from this same mouse model freely available through a sample repository, so researchers can obtain samples to test biological hypotheses without the need of breeding animals and collecting biospecimens. Experimental design: Twelve datasets are available, encompassing 841 LC-MS/MS experiments (plasma and tissues) and 255 microarray analyses of multiple tissues (thymus, spleen, liver, blood cells, and breast). Cases and controls were rigorously paired to avoid bias. Results: In total, 18,880 unique peptides were identified (PeptideProphet peptide error rate ≤1%), with 3884 and 1659 non-redundant protein groups identified in plasma and tissue datasets, respectively. Sixty-one of these protein groups overlapped between cancer plasma and cancer tissue. Conclusions and clinical relevance: These data are of use for advancing our understanding of cancer biology, for software and quality control tool development, investigations of analytical variation in MS/MS data, and selection of proteotypic peptides for MRM-MS. The availability of these datasets will contribute positively to clinical proteomics.
Project description:Background: There is limited data on the mechanisms of aspirin desensitization in patients with nonsteroidal anti-inflammatory drug (NSAID)-induced urticaria/angioedema (NIUA). Objectives: To characterize the transcriptomic and metabolomic profiles of NIUA patients undergoing aspirin desensitization. Methods: Peripheral blood mononuclear cells (PBMCs) and plasma were separated from the blood of NIUA patients undergoing aspirin desensitization for coronary artery disease and NSAID-tolerant controls. RNA was isolated from PBMCs and subjected to mRNA- and lncRNA-seq. Plasma samples were analyzed using LC-MS/MS for metabolite shifts using a semi-targeted metabolomics panel. Results: Eleven patients with NIUA and 10 healthy controls were recruited. The mRNA gene profiles of pre- versus post-desensitization and healthy control versus post-desensitization did not differ significantly. However, we identified 739 mRNAs and 888 lncRNAs as differentially expressed from pre-aspirin desensitization patients and controls. A 12-mRNA gene signature was trained using a machine learning algorithm to distinguish between controls, post-dose and pre-dose samples. Ingenuity Pathway Analysis identified 5 canonical pathways that were significantly enriched in pre-aspirin desensitization samples. Interleukin (IL)-22 was the most upregulated pathway. To investigate the potential regulatory roles of the differentially expressed lncRNA on the mRNAs, 9 lncRNAs and 12 mRNAs showed significantly correlated expression patterns in the IL-22 pathway. To validate the transcriptomics data, IL-22 was measured in the plasma samples of the subjects using ELISA. IL-22 was significantly higher in pre-aspirin desensitization patients compared to controls. In parallel, metabolomic analysis revealed stark differences in plasma profiles of pre-aspirin desensitization patients and healthy controls. In particular, 2-hydroxybenzoic acid (salicylic acid) was significantly lower in pre-aspirin desensitization patients compared to healthy controls. Conclusion: This is the first study to combine both transcriptomic and metabolomic approaches in patients with NIUA, which contributes to a deeper understanding about the pathogenesis of NIUA and may potentially pave the way towards a molecular diagnosis of NSAID hypersensitivity.