Homo sapiens
Ontology highlight
ABSTRACT:
OTHER RELATED OMICS DATASETS IN: GSE66222
PROVIDER: PRJNA263592 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:Molecular analysis of gastric cancer identifies discrete subtypes associated with distinct clinical characteristics and survival outcomes: the ACRG (Asian Cancer Research Group) study
| PRJNA276142 | ENA
Project description:Molecular analysis of gastric cancer identifies discrete subtypes associated with distinct clinical characteristics and survival outcomes: the ACRG (Asian Cancer Research Group) study [normal tissue]
| PRJNA276147 | ENA
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: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-α 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.
2012-04-03 | GSE36968 | GEO
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 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.
2012-04-03 | E-GEOD-36968 | biostudies-arrayexpress
Project description:Previous studies successfully revealed molecular characteristics of bladder cancers, dealing with non-muscle invasive bladder cancer and muscle invasive bladder cancer, separately. At the molecular level, however, there is a great need to aggregate these subtypes, which may share biological characteristics. This study aimed to identify distinct molecular subtypes of BC and the clinical and/or biological characteristics of each subtype. We used seven gene expression data sets for bladder cancer, which included data from 118 primary bladder cancer samples and 27 recurrent bladder tumor tissues from the Yonsei University Severance Hospital. Hierarchical clustering revealed four molecular subtypes of BC with different clinical outcomes: class 1 with low-grade NMIBC and the best prognosis; class 2 characterized by active FGFR3 and inhibited immune response pathways; class 3 with high-grade NMIBC and the worst progression-free survival; and class 4 mainly comprised of MIBC along with EMT activation. By applying the classifier based on these characteristics, we stratified all BC samples into newly identified molecular subtypes. When comparing previously reported subtypes, our subtypes well agreed with their molecular characteristics regardless of breast cancer-based biology, and showed a strong prognostic relevance in class 3. Integrative analysis of mutation and gene expression suggested that class 3 may have the potential benefit from anti-PD-L1 immunotherapy. Our classifier, constructed by NMIBC and MIBC integration, successfully stratified BC patients into distinct subtypes with different clinical outcomes and a possible treatment option.
2018-10-03 | GSE120736 | GEO
Project description:Genomic profiling can provide prognostic and predictive information to guide clinical care. Biomarkers that reliably predict patient response to chemotherapy and immune checkpoint inhibition in gastric cancer are lacking. In this retrospective analysis, we use our machine learning algorithm NTriPath [Park, Sunho et al. “An integrative somatic mutation analysis to identify pathways linked with survival outcomes across 19 cancer types.” Bioinformatics (2016): 1643-51. doi:10.1093/bioinformatics/btv692] to identify a gastric-cancer specific 32-gene signature. Using unsupervised clustering on expression levels of these 32 genes in tumors from 567 patients, we identify four molecular subtypes that are prognostic for survival. We then built a support vector machine with linear kernel to generate a risk score that is prognostic for five-year overall survival and validate the risk score using three independent datasets. We also find that the molecular subtypes predict response to adjuvant 5-fluorouracil and platinum therapy after gastrectomy and to immune checkpoint inhibitors in patients with metastatic or recurrent disease. In sum, we show that the 32-gene signature is a promising prognostic and predictive biomarker to guide the clinical care of gastric cancer patients and should be validated in a prospective manner.
2022-01-01 | GSE183136 | GEO
Project description:Genome-wide mRNA expression profiles of 70 primary gastric tumors from the Australian patient cohort. Like many cancers, gastric adenocarcinomas (gastric cancers) show considerable heterogeneity between patients. Thus, there is intense interest in using gene expression profiles to discover subtypes of gastric cancers with particular biological properties or therapeutic vulnerabilities. Identification of such subtypes could generate insights into the mechanisms of cancer progression or lay the foundation for personalized treatments. Here we report a robust gene-xpression-based clustering of a large collection of gastric adenocarcinomas from Singaporean patients [GSE34942 and GSE15459]. We developed and validated a classifier for the three subtypes in Australian patient cohort. Profiling of 70 primary gastric tumors on Affymetrix GeneChip Human Genome U133 Plus 2.0 Array. All tumors were collected with approvals from Peter MacCallum Cancer Center, Australia; the Research Ethics Review Committee; and signed patient informed consent.
2012-07-04 | E-GEOD-35809 | biostudies-arrayexpress