OVARIAN CANCER and US: microarray

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Showing posts with label microarray. Show all posts
Showing posts with label microarray. Show all posts

Friday, March 02, 2012

High-Risk Ovarian Cancer Based on 126-Gene Expression Signature Is Uniquely Characterized by Downregulation of Antigen Presentation Pathway - Japan



Abstract

Purpose: 
High-grade serous ovarian cancers are heterogeneous not only in terms of clinical outcome but also at the molecular level. Our aim was to establish a novel risk classification system based on a gene expression signature for predicting overall survival, leading to suggesting novel therapeutic strategies for high-risk patients. 

Experimental Design: 
In this large-scale cross-platform study of six microarray data sets consisting of 1,054 ovarian cancer patients, we developed a gene expression signature for predicting overall survival by applying elastic net and 10-fold cross-validation to a Japanese data set A (n = 260) and evaluated the signature in five other data sets. Subsequently, we investigated differences in the biological characteristics between high- and low-risk ovarian cancer groups. 

Results: 
An elastic net analysis identified a 126-gene expression signature for predicting overall survival in patients with ovarian cancer using the Japanese data set A (multivariate analysis, P = 4 × 10−20).. ........ Through gene ontology and pathway analyses, we identified a significant reduction in expression of immune-response–related genes, especially on the antigen presentation pathway, in high-risk ovarian cancer patients. 

Conclusions: 
This risk classification based on the 126-gene expression signature is an accurate predictor of clinical outcome in patients with advanced stage high-grade serous ovarian cancer and has the potential to develop new therapeutic strategies for high-grade serous ovarian cancer patients. 

Friday, January 27, 2012

abstract: Implementing an online tool for genome-wide validation of survival-associated biomarkers in ovarian-cancer using microarray data from 1287 patients.



Abstract

"The validation of prognostic biomarkers in large independent patient cohorts is a major bottleneck in ovarian cancer research. We implemented an online tool to assess the prognostic value of the expression levels of all microarray quantified genes in ovarian cancer patients. First, a database was set up using gene expression data and survival information of 1,287 ovarian cancer patients downloaded from GEO and TCGA (Affymetrix HGU133A, HGU133A 2.0 and HGU133+2 microarrays).

After quality control and normalization only probes present on all three Affymetrix platforms were retained (n=22,277). To analyze the prognostic value of the selected gene, the patients are divided into two groups according to various quantile expressions of the gene. These groups are then compared using progression free survival (n=1,090) or overall survival (n=1,287). A Kaplan-Meier survival plot is generated and significance is computed. The tool can be accessed online at www.kmplot.com/ovar. We used this integrative data analysis tool to validate the prognostic power of 37 biomarkers identified in the literature.

Of these, CA125 (p=3.7e-5, HR=1.4), CDKN1B (p=5.4e-5, HR=1.4), KLK6 (p=0.002,HR=0.79), IFNG (p=0.004, HR=0.81), P16 (p=0.02, HR=0.66) and BIRC5 (p=0.00017, HR=0.75) were associated with survival. The combination of several probe sets can further increase prediction efficiency.

In summary, we developed a global online biomarker validation platform that mines all available microarray data to assess the prognostic power of 22,277 genes in 1,287 ovarian cancer patients.

We specifically used this tool to evaluate the effect of 37 previously published biomarkers on ovarian cancer prognosis."

Tuesday, June 28, 2011

ongoing: Research Study in Patients With Advanced Epithelial Ovarian Cancer - Full Text View - ClinicalTrials.gov



Purpose
RATIONALE: Analyzing tissue samples from patients in the laboratory may help doctors learn more about cancer.
PURPOSE: The purpose of this study is to analyze tissue samples from patients with ovarian cancer in the laboratory.

Condition Intervention
Ovarian Cancer Genetic: comparative genomic hybridization
Genetic: cytogenetic analysis
Genetic: gene rearrangement analysis
Genetic: microarray analysis
Genetic: mutation analysis
Genetic: polymerase chain reaction