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PROVIDER: PRJEB61170 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:Resectable gastric cancer prognosis study by multiple omics
| PRJEB61171 | ENA
Project description:Serum is a valuable body fluid to diagnose cancer as it can be accessed with minimal invasive techniques. Studying the cancer serum proteome provides valuable insights into the pathophysiology of tumor progression. Gastric adenocarcinoma is an aggressive cancer resulting in poor prognosis, mainly due to the lack of specific early diagnostic biomarkers. To this end, we used an iTRAQ-based quantitative proteomic approach to identify differentially expressed proteins in the sera of patients diagnosed with gastric cancer. Our study resulted in the identification of 643 proteins in the serum, of which 48 proteins were found to be overexpressed and 11 proteins underexpressed in gastric cancer when compared with healthy controls. We used multiple reaction monitoring assays to validate the overexpression of potential biomarkers. This catalog of serum-based biomarkers will aid in diagnosis and prognosis of gastric cancer.
2015-07-27 | PXD001265 | Pride
Project description:Gastric cancer, a leading cause of cancer related deaths, is a heterogeneous disease, with little consensus on molecular subclasses and their clinical relevance. We describe four molecular subtypes linked with distinct patterns of molecular alterations, disease progression and prognosis viz. a) Microsatellite Instable: hypermutated intestinal subtype tumors occurring in antrum, best overall prognosis, lower frequency of recurrence (22%), with liver metastasis in 23% of recurred cases b) Mesenchymal-like: diffuse tumors with worst prognosis, a tendency to occur at an earlier age and highest recurrence (63%) with peritoneal seeding in 64% of recurred cases, low frequency of molecular alterations c) TP53-inactive with TP53 loss, presence of focal amplifications and chromosomal instability d) TP53-active marked by EBV infection and PIK3CA mutations. The key molecular mechanisms and associated survival patterns are validated in multiple independent cohorts, to provide a consistent and unified framework for further preclinical and clinical research. ACRG Gastric cohort: microarray profiles from 300 gastric tumors from gastric cancer patients.
2015-04-17 | E-GEOD-62254 | biostudies-arrayexpress
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.
2024-07-23 | MODEL2407230001 | BioModels
Project description:As a key protein in the tumor microenvironment, FN1 has been proven to promote cancer in a variety of cancer species. However, in our previous study on gastric cancer, it was found that FN1 protein expression in patient tissues was not significantly correlated with prognosis, and clinical data analysis was only correlated with T stage of gastric cancer. Analysis of the relationship between FN1 mRNA expression level and prognosis by TCGA-STAD database revealed that high FN1 mRNA expression was significantly correlated with poor prognosis. Moreover, many miRNA binding sites were found on FN1 3' UTR by database prediction, so we speculated that FN1 played different functions at mRNA and protein levels, and FN1 3' UTR might function as ceRNA. At present, FN1 3' UTR overexpression has been proved to significantly promote the invasion and metastasis of gastric cancer in vivo and in vitro, and its effect is stronger than FN1 protein. Therefore, in this study, miRNA sequencing, which was used to find those miRNAs bound to FN1 3’UTR, was carried out to further explore its cancer-promoting mechanism.
2022-02-27 | GSE197423 | GEO
Project description:Molecular knowledge of normal gastric tissues and gastric cancers remains incomplete. Here, we used single-cell RNA-seq to study the cell diversity of gastric tissues and gastric cancers. The expression landscape of normal gastric cell types and several candidate stem cell markers were obtained. Surprisingly, nearly all cell types in the antrum could transdifferentiate to intestinal metaplasia (IM). We also explored intra-tumoral heterogeneity and identified four common features of gastric cancer. In addition, we classified tumor cells into three major subtypes, which are associated with their prognosis. Finally, the proportions of mesenchymal and endothelial cells in the tumor microenvironment (TME) were negatively correlated with the prognosis of gastric cancer. Therefore, our work provides comprehensive molecular characterizations of both gastric development and gastric cancer at single-cell resolution and has significant potential to inspire better treatment strategies for gastric cancer. Keywords: Expression profiling by high throughput sequencing
2022-09-27 | GSE112302 | GEO
Project description:Clinical heterogeneity of gastric cancer reflected in unequal outcome of treatment is poorly defined in molecular level, and molecular subtypes and their associated biomarkers have not been established to improve prognostification and treatment of gastric cancer. Using microarray technologies, we analyzed gene expression profiling data from patients with advanced gastric cancer and uncovered potential prognostic subtypes and identify gene expression signature associated with prognosis. Using microarray technologies, we analyzed gene expression profiling data from patients with advanced gastric cancer and uncovered potential prognostic subtypes and identify gene expression signature associated with prognosis.
2012-04-29 | E-GEOD-28541 | biostudies-arrayexpress
Project description:Gastric cancer (GC) is associated with high mortality rates and an unfavorable prognosis at advanced stages. In addition, there are no effective methods for diagnosing gastric cancer at an early stage or for predicting the outcome for the purpose of selecting patient-specific treatment options. Therefore, it is important to investigate new methods for GC diagnosis. We designed a custom microarray of gastric cancer. The customized microarray contained 1042 canceration and prognosis related genes identical to the probes on the Agilent microarray. DNA microarray profilling analysis was performed on gastric cancer tissues and premalignant tissues (20 samples per group).
2014-03-01 | E-MTAB-1881 | biostudies-arrayexpress
Project description:Bulk 3-prime RNAsequencing of MYC-overexpressing gastric cancer cell lines derived from mouse gastric neuroendocrine carcinomas
| PRJEB61969 | ENA
Project description:Gastric cancer is one of the most lethal malignancies with high mortality and gastric cancer-specific biomarker is need due to the lack of specific method for early screening, diagnosis, and prognosis of the patients with gastric cancer. Ascites is known for an important source for conducing biomarker discovery because it contains the secreted proteins from malignant cells, growth factors, and cytokines. In this study, we have conducted a comprehensive proteome study using ascites of patients with inflammatory diseases and gastric cancer. In the discovery stage, we have identified 2761 ascites-specific proteins, where 234 proteins were quantitated using the label free quantitation method, the normalized spectral abundance factor (NSAF); 152 and 82 proteins showed up and down-regulated pattern, respectively. Our ascites proteome can be used as baseline data for the discovery of novel biomarkers of the gastric cancer.
2018-04-16 | PXD002213 | Pride