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:Gastric cancer is the second most common cause of cancer death worldwide, but incidence and mortality rates show large variations across different countries. Variation in risk factors between different populations, including environmental and host factors influencing gastric cancer risk, have been reported but little is known about the biological differences between gastric cancers from different geographic locations. We set out to study genomic instability patterns of gastric cancers obtained from patients from United Kingdom (UK) and South Africa (SA). DNA was isolated from 67 gastric adenocarcinomas, 33 UK patients, 9 Caucasian SA patients and 25 native SA patients. Microsatellite instability and chromosomal instability were analyzed by PCR and microarray comparative genomic hybridization, respectively. Data was analyzed by supervised univariate and multivariate analyses as well as unsupervised hierarchical cluster analysis. Tumors from Caucasian and native SA patients showed significantly more microsatellite instable tumors (p<0.05). For the microsatellite stable tumors, geographical origin of the patients correlated with cluster membership, derived from unsupervised hierarchical cluster analysis (p=0.001). Several chromosomal alterations showed significantly different frequencies in tumors from UK patients and native SA patients, but not between UK patients and Caucasian SA patients and between native and Caucasian SA patients. In conclusion, gastric cancers from South African and UK patients show differences in genetic instability patterns, indicating possible different biological mechanisms underlying the disease.
Project description:Gastric cancer is the most common cancer in Asia and most developing countries. To identify the molecular underpinnings of gastric cancer in the Asian population, we applied an RNA-sequencing approach to gastric tumor and noncancerous specimens to quantitatively characterize the entire transcriptome of gastric cancer (including mRNAs and microRNAs). A multi-layer analysis was then developed to identify multiple types of transcriptional aberrations associated with different stages of gastric cancer, including differentially expressed mRNAs, recurrent somatic mutations and key differentially expressed microRNAs. Through this approach, we identified the central metabolic regulator AMPK-M-NM-1 as a potential functional target in Asian gastric cancer. Further, we experimentally demonstrated the translational relevance of this gene as a potential therapeutic target for early-stage gastric cancer in Asian patients. Together, our findings not only provide a valuable information resource for identifying and elucidating the molecular mechanisms of Asian gastric cancer, but also represent a general integrative framework to develop more effective therapeutic targets. Using Life Technologies SOLiDM-bM-^DM-" sequencing platform, we performed transcriptome-wide profiling of gastric cancer samples from 30 anonymous, unrelated Asians of both sexes. Included were six noncancerous gastric tissue samples and 24 gastric tumor samples that represented stages I through IV of tumor development. From the WT-seq protocol we generated a WT-seq dataset of 2.1 billion 50-nt short reads from the 30 samples; Applying the second small RNA-seq protocol to 19 gastric tumor samples (5 of the original 24 yielded insufficient sample amounts) and 6 noncancerous gastric tissue samples resulted in a small RNA-seq dataset.
Project description:Gastric cancer is the second most common cause of cancer death worldwide, but incidence and mortality rates show large variations across different countries. Variation in risk factors between different populations, including environmental and host factors influencing gastric cancer risk, have been reported but little is known about the biological differences between gastric cancers from different geographic locations. We set out to study genomic instability patterns of gastric cancers obtained from patients from United Kingdom (UK) and South Africa (SA). DNA was isolated from 67 gastric adenocarcinomas, 33 UK patients, 9 Caucasian SA patients and 25 native SA patients. Microsatellite instability and chromosomal instability were analyzed by PCR and microarray comparative genomic hybridization, respectively. Data was analyzed by supervised univariate and multivariate analyses as well as unsupervised hierarchical cluster analysis. Tumors from Caucasian and native SA patients showed significantly more microsatellite instable tumors (p<0.05). For the microsatellite stable tumors, geographical origin of the patients correlated with cluster membership, derived from unsupervised hierarchical cluster analysis (p=0.001). Several chromosomal alterations showed significantly different frequencies in tumors from UK patients and native SA patients, but not between UK patients and Caucasian SA patients and between native and Caucasian SA patients. In conclusion, gastric cancers from South African and UK patients show differences in genetic instability patterns, indicating possible different biological mechanisms underlying the disease. 67 gastric adenocarcinomas, 33 UK patients, 9 Caucasian SA patients and 25 native SA patients.
Project description:Synthetic peptides are commonly used in biomedical science for many applications in basic and translational research. Here, we assembled a large dataset of synthetic peptides whose identity was validated using mass spectrometry. We analyzed the mass spectra and used them for method validation as well as the creation of ground truth datasets and cognate databases. Contact: Michele Mishto, Head of the research group Molecular Immunology at King’s College London and the Francis Crick Institute, London (UK). Email: michele.mishto@kcl.ac.uk,
Project description:This SuperSeries is composed of the following subset Series:; GSE15455: GEMINI (Gastric Encyclopedia of Molecular Interactions and Nodes for Intervention) Phases A-C; GSE15456: Primary Gastric Cancer Expression Profiles (UK Patient Cohort); GSE15459: Gastric Cancer Project '08 (Singapore Patient Cohort); GSE15537: GEMINI (Gastric Encyclopedia of Molecular Interactions and Nodes for Intervention) Phases A-C, normal skin fibroblasts Experiment Overall Design: Refer to individual Series
Project description:We aimed to investigate the C. elegans response to Haptoglossa zoospora infection. To this end we performed RNA sequencing using the illumina Hiseq 2500 platform on infected animals at the L4 stage of development after 6hours and 12 hours of pathogen exposure and uninfected animals at the same time points . The sequence reads, generated by Imperial College London sequencing facility (Hammersmith campus) that passed quality controls were aligned to the most recent C. elegans (WBcel235) transcriptome using the pseudoalignment tool Kallisto. The generated abundance files were analysed using an R pipeline for Sleuth. Differentially expressed genes with a p value <0.01 and FDR value <0.1 were isolated from the dataset and compared to genes induced in infections by other C. elegans pathogens including the oomycete Myzocytiopsis humicola. The result of this comparison revealed conserved features underlying oomycete detection by C. elegans.
Project description:In this dataset, we include the expression data obtained from gastric cancer tissues and gastric normal tissues to determine the differentially expressed genes in gastric cancer tissues