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:We combined analysis of serum metabolomics and whole blood transcriptomics in patients with RRMS and SPMS to build an integrated network describing these different phases of MS and to help improve our understanding of the potential mechanisms driving disease progression. Transcriptomic analysis of whole blood was performed to assess differential gene expression between five patients with RRMS and eight patients with SPMS.
Project description:Existence of potent in vitro regeneration system is a prerequisite for efficient genetic transformation and functional genomics of crop plants. Little is known why only some cultivars in crop plants are tissue culture friendly? In this study, contrasting barley cultivars Golden Promise (GP) and DWRB91 (D91) were utilized for digging the molecular basis of regeneration efficiency. Multiomics studies involving transcriptomics, proteomics, metabolomics, and biochemical analysis were performed using GP and D91 callus to unravel the regulatory mechanisms. Transcriptomics analysis revealed 1487 differentially expressed genes (DEGs), in which 795 DEGs were upregulated and 692 DEGs were downregulated in the GP-D91 transcriptome. Genes encoding proteins localized in chloroplast and involved in ROS generation were upregulated in the embryogenic calli of GP. Moreover, proteome analysis by LC-MS revealed 3062 protein groups and 16989 peptide groups, out of these 1586 protein groups were differentially expressed proteins (DEPs). Eventually, GC-MS based metabolomics analysis also revealed the higher activity of plastids and alterations in key metabolic processes such as sugar metabolism, fatty acid biosynthesis, and secondary metabolism. Higher accumulation of sugars, amino acids and metabolites corresponding to lignin biosynthesis were observed in GP as compared to D91.
Project description:Lateral organ development is important for cucumber yield, while the molecular mechanism controlling leaf and floral organ development in cucumber remain elusive. In this report, a novel EMS-mutaginized mutant, round leaf (rl) was distinguished with remarkable round leaf shape, abnormal floral organ and inhibited tendril outgrowth in early development phase. Moreover, the ovule organogenesis disrupted completely in parthenocarpy fruit of rl. MutMap+ analysis revealed that RL encodes a protein kinase PINOID (CsPID, Csa1G537400). A non-synoymous SNP in the second exon of CsPID resulted in an amino-acid substitution from Arg in the wild type to Lys in the rl mutant. CsPID was down-regulated in rl mutant and preferentially expressed in young leaf, and flower buds. IAA quantification showed that rl plants exhibited a lower IAA content than wild type in ovary and blade edge. IAA immunolocalization results confirmed the IAA content alteration in rl plants. Transcriptome profile analysis further suggested IAA biosynthesis, polar transport and signal transduction genes participated in the leaf and floral development process by CsPID. Biochemical analyses showed that CsPID may regulate leaf shape by interacting with CsREV. In conclusion, this study revealed that the extensive genetic architecture of lateral organ organogenesis and development via CsPID regulating auxin polar transport action in cucumber.