Molecular subtypes in gastric cancer.
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ABSTRACT:
PROVIDER: PRJNA329144 | ENA |
REPOSITORIES: ENA
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Project description:Molecular subtypes in gastric cancer. [III]
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Project description:We identified the molecular subtypes and conserved modules in gastric cancer by unsupervised clustering algorithm. We defined five molecular subtypes and six molecular signatrues of gastric cancer associated with the biological heterogeneity of gastric cancer and clinical outcome of patients.
2018-03-20 | GSE84433 | GEO
Project description:We identified the molecular subtypes and conserved modules in gastric cancer by unsupervised clustering algorithm. We defined five molecular subtypes and six molecular signatrues of gastric cancer associated with the biological heterogeneity of gastric cancer and clinical outcome of patients.
2018-03-20 | GSE84426 | GEO
Project description:We identified the molecular subtypes and conserved modules in gastric cancer by unsupervised clustering algorithm. We defined six molecular signatrues of gastric cancer associated with the biological heterogeneity of gastric cancer and clinical outcome of patients.
2023-03-18 | GSE147163 | GEO
Project description:Next-generation sequencing analysis of molecular subtypes of gastric cancer
| PRJNA749940 | ENA
Project description:Gastric cancer is a leading cause of death from cancer globally. Gastric cancer is classified into intestinal, diffuse and indeterminate subtypes based on histology according to the Laurén classification. The intestinal and diffuse subtypes, although different in histology, demographics and outcomes, are still treated in the same fashion. This study was designed to discover proteomic signatures of diffuse and intestinal subtypes. Mass spectrometry-based proteomics using tandem mass tags (TMT)-based multiplexed analysis was used to identify proteins in tumor tissues from patients with diffuse or intestinal gastric cancer with adjacent normal tissue control. A total of 7,804 or 5,166 proteins were identified from intestinal or diffuse subtype, respectively. This quantitative mass spectrometric analysis defined a proteomic signature of differential expression across the two subtypes, which included gremlin1 (GREM1), bcl-2-associated athanogene 2 (BAG2), olfactomedin 4 (OLFM4), thyroid hormone receptor interacting protein 6 (TRIP6) and melanoma-associated antigen 9 (MAGE-A9) proteins. Although GREM1, BAG2, OLFM4, TRIP6 and MAGE-A9 have all been previously implicated in tumor progression and metastasis, they have not been linked to intestinal or diffuse subtypes of gastric cancer. Using immunohistochemical labelling of a tissue microarray comprising of 132 cases of gastric cancer, we validated the proteomic signature obtained by mass spectrometry in the discovery cohort. Our findings should help investigate the pathogenesis of these gastric cancer subtypes and potentially lead to strategies for early diagnosis and treatment.
2022-02-17 | PXD026279 | 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:Background: Intestinal metaplasia (IM) is a gastric precancerous lesion that precedes the development of gastric cancer in up to 3.77 cases/1000 person-years. It consists in a trans-differentiation process of gastric to intestinal tissue. Two histological subtypes exist, complete (CIM) and incomplete (IIM), the latter having higher progression rates to gastric cancer . Our objective was to identify molecular processes responsible for the tumoral transition from intestinal metaplasia to GC and the initial steps of this lesion Methods: We used expression microarray to compare the transcriptome of intestinal metaplasia subtypes that progress to gastric cancer (IIM-GC and CIM-GC) after a follow-up period with respect to those that do not progress (IIM control and CIM control). Also, IM-NoGC (IM control, comprising both IIM and CIM Control) was compared with healthy gastric mucosa. Differentially expressed genes were obtained and functional analyses (GSEA and IPA softwares) were performed. Some deregulated genes were validated by qPCR. Results: Histological subtypes of intestinal metaplasia that progress or not to GC differ less among them than to healthy mucosa. Incomplete intestinal metaplasia has a higher number of over-expressed carcinogenic genes and molecular processes than the complete subtype. Most relevant molecular processes and genes in this group include antigenic processing, inflammation, activation of cell cycle and cell proliferation, oncogenes and tumor suppressors. When IM-NoGC is compared with healthy gastric mucosa new identified transcripts include TRIM, TMEM, homeobox, transporters and nucleolar RNAs SNORDs116. We confirm previously reported processes such us intestinal differentiation, metabolism of lipids and of xenobiotics and identify new ones such as non tumoral Warburg effect and melatonin degradation. Conclusions: Differentially expressed genes and molecular processes have been identified for the first time in intestinal metaplasia that progress to gastric cancer. New genes and molecular processes have also been identified in intestinal metaplasia in comparison with healthy gastric mucosa.
2017-06-12 | GSE78523 | 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