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:In this study, we performed LC-QTOF-MS-based metabolomics and RNA-seq based transcriptome analysis using seven tissues of M. japonicus.
Project description:In this study, we performed LC-QTOF-MS-based metabolomics and RNA-seq based transcriptome analysis using four tissues of A. japonicum.
Project description:In this study, we performed LC-QTOF-MS-based metabolomics and RNA-seq based transcriptome analysis using seven tissues of Magnolia obovata
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:Females typically outlive males, a disparity mitigated by castration, yet the molecular underpinnings remain elusive. Our study leverages untargeted metabolomics and RNA sequencing to uncover the pivotal compounds and genes influencing healthy aging post-castration, examining serum, kidney, and liver biospecimens from 12-week and 18-month old castrated male mice and their unaltered counterparts. Behavioral tests and LC-MS/MS metabolomics reveal that castrated males exhibit altered steroid hormones, superior cognitive performance, and higher levels of anti-oxidative compounds like taurine, despite identical diets. Integrated metabolome-transcriptome analysis confirms reduced lipid peroxidation and oxidative stress in female and castrated male mice, suggesting a protective mechanism against aging. Histological examinations post-cisplatin treatment highlight the model’s applicability in studying drug toxicity and reveal varying susceptibility in organ-specific toxicities, underlining the crucial role of sex hormones in physiological defenses. In essence, our castration model unveils a feminized metabolic and transcriptomic intermediary, serving as a robust tool for studying gender-specific aspects of healthy aging and exploring sex hormone-induced differences in diverse biomedical domains.
Project description:Sustainable avian production demands genetic strategies beyond vaccines to enhance disease resilience, particularly in indigenous breeds like Lueyang Silky Fowl (LSF), which show superior resistance compared to commercial White Leghorn chickens (WLC) but lack molecular characterization. We hypothesized LSF breast muscle harbors integrated immune–metabolic adaptations absent in WLC, reflecting evolutionary divergences and physiological ecology. We performed RNA-seq, LC-MS/MS proteomics and untargeted metabolomics on sex- and age-matched LSF and WLC breast muscle (n=3/group). Differentially expressed genes (DEGs), proteins (DAPs), and metabolites (DAMs) were identified and integrated via pathway enrichment and network analyses. LSF muscle showed 2,577 DEGs (949 up, 1,628 down), 262 DAPs (48 up, 214 down), and 197 DAMs (52 up, 145 down). Concordant enrichments spanned mitochondrial oxidative phosphorylation; amino-acid metabolism (arginine/proline; alanine/aspartate/glutamate); MAPK and calcium signaling; and antioxidant pathways (notably glutathione). Integrated networks revealed 66 shared KEGG pathways, including convergent hubs in calcium/MAPK signaling, apelin, and focal adhesion, nominating candidate markers like NOS1, GOT1, PRKCA, MAP2K6, and ATP6V1E1. These bulk-tissue signatures likely integrate myocyte-intrinsic, stromal, and resident immune-cell programs; thus, offering testable signatures for avian immunometabolic resilience. We propose validation through single-cell/spatial transcriptomics, immunohistochemistry, targeted LC-MS and metabolomics, and pathogen challenge assays. This study advances ornithological understanding by revealing breed-specific physiological adaptations in poultry, positioning muscle as proxy for systemic resilience, and providing biomarkers for genomic selection to conserve and enhance genetic diversity in resilient avian lines under intensive farming pressure.