Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

An Invasive Gene Expression Signature in Pancreas Cancer.

ABSTRACT: Gene expression profiling has demonstrated clinical utility as a predictive tool in clinical oncology. We have identified genes associated with invasion of pancreatic cancer, and with potential for identifying early recurrence. We used Affymetrix Human U133 Plus 2.0 microarrays to identifiy specific predictive profiles in pancreatic cancer, and the evolution of gene expression. We identified distinct classes of up-regulated genes during this process.

ORGANISM(S): Homo sapiens

PROVIDER: GSE22973 | GEO | 2011/01/01

SECONDARY ACCESSION(S): PRJNA127749

REPOSITORIES: GEO

ACCESS DATA

Json Xml

Similar Datasets

An Invasive Gene Expression Signature in Pancreas Cancer

Project description:Gene expression profiling has demonstrated clinical utility as a predictive tool in clinical oncology. We have identified genes associated with invasion of pancreatic cancer, and with potential for identifying early recurrence. We used Affymetrix Human U133 Plus 2.0 microarrays to identify specific predictive profiles in pancreatic cancer, and the evolution of gene expression. We identified distinct classes of up-regulated genes during this process. Primary and metastatic pancreatic cancer cell lines (BxPC-3 and AsPC-1), were stimulated with with phorbol-12-myristate 13-acetate (PMA), a known inducer of invasion. Affymetrix gene expression microarray analysis was performed, comparing PMA stimulated BxPC-3 and AsPC-1 gene expression to unstimulated controls, and also PMA stimulated BxPC-3 verses stimulated AsPC-1 cell lines. Differential gene expression was identified using ArrayAssist bioinformatics software. Gene expression changes were confirmed using quantitative reverse transcription polymerase chain reaction (qRT-PCR) (Assays-on-demand, Taqman, ABI systems). Pathway Assist and GOstat were used to identify pathway and gene ontology changes.

2011-01-01 | E-GEOD-22973 | biostudies-arrayexpress

Identification of a Novel Inflamed Tumor Microenvironment Signature as a Predictive Biomarker of Bacillus Calmette-Guérin Immunotherapy in Non–Muscle-Invasive Bladder Cancer

Project description:Improved risk stratification and predictive biomarkers of treatment response are needed for non–muscle-invasive bladder cancer (NMIBC). Here we assessed the clinical utility of targeted RNA and DNA molecular profiling in NMIBC. We performed RNA-based profiling by NanoString nCounter on non–muscle-invasive bladder cancer (NMIBC) clinical specimens and found that a novel expression signature of an inflamed tumor microenvironment (TME), but not molecular subtyping, was associated with improved recurrence-free survival after bacillus Calmette-Guérin (BCG) immunotherapy. We further demonstrated that immune checkpoint gene expression was not associated with higher recurrence rates after BCG.

2021-10-05 | GSE185264 | GEO

Whole-Tissue Gene Expression Study of Pancreatic Ductal Adenocarcinoma

2009-06-13 | GSE15471 | GEO

Transcription profiling of human neuro-invasive pancreatic tumors engineered in-vitro compared to less invasive parental cells

Project description:Perineural invasion (PNI) is considered to be a specific path for the spread of pancreatic cancer cells and PNI is thought to be one of the important factors that determine local recurrence after resection. In addition PNI has been implicated in the pain syndrome that affects the majority of pancreatic ductal adenocarcinoma (PDAC) patients. Here we aimed to investigate the transcriptional differences between neuro-invasive tumors engineered in-vitro compared to less invasive parental cells. Two colour differential hybridisations were performed with human universal reference total RNA (Clontech, #636538).

2009-04-01 | E-TABM-400 | biostudies-arrayexpress

Profiling of pancreatic adenocarcinoma using artificial intelligence-based integration of multi-omic and computational pathology features

Project description:Contemporary analyses focused on a limited number of clinical and molecular features have been unable to accurately predict clinical outcomes in pancreatic ductal adenocarcinoma (PDAC). Here we describe a novel, conceptual approach and use it to analyze clinical, computational pathology, and molecular (DNA, RNA, protein, and lipid) analyte data from 74 patients with resectable PDAC. Multiple, independent, machine learning models were developed and tested on curated singleand multi-omic feature/analyte panels to determine their ability to predict clinical outcomes in patients. The multi-omic models predicted recurrence with an accuracy and positive predictive value (PPV) of 0.90, 0.91, and survival of 0.85, 0.87, respectively, outperforming every singleomic model. In predicting survival, we defined a parsimonious model with only 589 multi-omic analytes that had an accuracy and PPV of 0.85. Our approach enables discovery of parsimonious biomarker panels with similar predictive performance to that of larger and resource consuming panels and thereby has a significant potential to democratize precision cancer medicine worldwide.

2024-01-23 | PXD037038 | Pride

Profiling of pancreatic adenocarcinoma using artificial intelligence-based integration of multi-omic and computational pathology features: lipid

Project description:Contemporary analyses focused on a limited number of clinical and molecular features have been unable to accurately predict clinical outcomes in pancreatic ductal adenocarcinoma (PDAC). Here we describe a novel, conceptual approach and use it to analyze clinical, computational pathology, and molecular (DNA, RNA, protein, and lipid) analyte data from 74 patients with resectable PDAC. Multiple, independent, machine learning models were developed and tested on curated single and multi-omic feature/analyte panels to determine their ability to predict clinical outcomes in patients. The multi-omic models predicted recurrence with an accuracy and positive predictive value (PPV) of 0.90, 0.91, and survival of 0.85, 0.87, respectively, outperforming every single-omic model. In predicting survival, we defined a parsimonious model with only 589 multi-omic analytes that had an accuracy and PPV of 0.85. Our approach enables discovery of parsimonious biomarker panels with similar predictive performance to that of larger and resource consuming panels and thereby has a significant potential to democratize precision cancer medicine worldwide.

2023-10-15 | MSV000093118 | MassIVE

Molecular characteristics and metastasis predictor genes of triple-negative breast cancer

Project description:This microarray dataset contains 51 triple-negative breast cancers with clinical and recurrence information for at least 3 years of follow-up and 106 luminal breast cancers (reanalyzed data from Series GSE24124, GSE9309, and GSE17040). A novel set of 45-gene signature that was statistically predictive of distant metastasis recurrence for triple-negative breast cancer was identified in this study.

2012-11-01 | GSE33926 | GEO

Development of novel diagnostic system for pancreas cancer including early stages measuring mRNA of whole blood cells

Project description:The aim of the study is the assessment of diagnostic efficacy of extracorporeal diagnostic reagent pancreatic cancer diagnosis kit whose principle is the measurement of gene expression affected in pancreatic cancer human patients. Clinical trial registration number: UMIN000020758.

2021-07-12 | GSE125158 | GEO

caArray_EXP-578: Gene Expression Profiles of Pediatric B-Precursor High-Risk Acute Lymphoblastic Leukemia (COG Study AALL0232 - Cohort 1).

Project description:This experiment comprises 283 CEL files generated on the Affymetrix U133 Plus 2.0 gene expression microarray platform, using patient peripheral blood and bone marrow samples from the first cohort of patients accrued to Children's Oncology Group Study AALL0232. No clinical covariate data is provided at this time as the clinical study is not yet published. Researchers who would like to request outcome or other covariate data are asked to contact Dr. Cheryl Willman, cwillman@unm.edu, 505.272.5622 (University of New Mexico) and Dr. Steven Hunger, Stephen.Hunger@childrenscolorado.org (Children's Oncology Group and Children's Hospital Colorado) to arrange a collaboration.

2015-05-13 | GSE68790 | GEO

Whole-Tissue Gene Expression Study of Pancreatic Ductal Adenocarcinoma

Project description:Expression analysis of 36 pancreatic ductal adenocarcinoma tumors and matching normal pancreatic tissue samples from pancreatic cancer patients of the Clinical Institute Fundeni (ICF) using Affymetrix U133 Plus 2.0 whole-genome chips. Pairs of normal and tumor tissue samples were obtained at the time of surgery from resected pancreas of 36 pancreatic cancer patients. Gene expression was analyzed on Affymetrix U133 plus 2.0 whole genome microarrays. For three of the 36 normal-tumor sample pairs we carried out replicate microarray hybridizations in order to gauge the technical measurement errors. We thus performed 78 genechip hybridizations in total. A patient sample pair was excluded from further analysis since one of the samples did not meet the quality controls. The microarray data was subsequently normalized using the RMA algorithm.

2009-06-13 | E-GEOD-15471 | biostudies-arrayexpress

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data