Project description:Cancer Gastric (CG) is a multifactorial disease with an important genetics background, per example copy number variants (CNV). Microarray data analysis were performed to identify CNV that could be contributing to those Mexican patient's clinical phenotypes
Project description:miRNAs expression of tumor sample of mexican patients with breast cancer. Samples obtained from the Hospital San Jose Tec de Monterrey.
Project description:Gene expression of tumor sample of mexican patients with breast cancer. Samples obtained from the Hospital San Jose Tec de Monterrey.
Project description:Gene expression of tumor sample of mexican patients with breast cancer. Samples obtained from the Hospital San Jose Tec de Monterrey. The experiments were with one color per patient, gene expression profile is from a tumor sample of mexican patients with breast cancer.
Project description:miRNAs expression of tumor sample of mexican patients with breast cancer. Samples obtained from the Hospital San Jose Tec de Monterrey. The experiments were with one color per patient, miRNAs expression profile is from a tumor sample of mexican patients with breast cancer.
Project description:Our aim was to decipher the underlying molecular mechanism of synchronous ovarian metastasis of gastric cancer. We hereby conducted transcriptome sequencing of triple-matched samples including normal gastric mucosa, primary gastric cancer and ovarian metastatic tumors from 3 individual patients with the application of Illumina sequencing platform with 150-bp paired-end. Follow-up analyses not only identified differentially expressed genes between different sample sets (a threshold of fold change >2 and adjusted P value <0.05) but also uncovered significantly enriched signaling pathways of individual type. To sum up, our comparative transcriptomic analyses of triple-matched fresh samples stored in liquid nitrogen profiled the molecular expression and revealed functionally enriched pathways underlying the ovarian metastasis of gastric cancer.
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:Genome wide DNA methylation profiling of normal and gastric cardia cancer samples. The Illumina Infinium 850k Human DNA methylation Beadchip was used to obtain DNA methylation profiles across approximately 850,000 CpGs in normal and gastric cardia cancer samples. Samples included 8 normal -gastrica cardia cancer paired tissues.