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Implementing an online tool for genome-wide validation of survival-associated biomarkers in ovarian-cancer using microarray data from 1287 patients. / Győrffy, Balázs; Lánczky, András; Szállási, Zoltán.

In: Endocrine - Related Cancer, Vol. 19, No. 2, 2012, p. 197-208.

Publication: Research - peer-reviewJournal article – Annual report year: 2012

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Győrffy, Balázs; Lánczky, András; Szállási, Zoltán / Implementing an online tool for genome-wide validation of survival-associated biomarkers in ovarian-cancer using microarray data from 1287 patients.

In: Endocrine - Related Cancer, Vol. 19, No. 2, 2012, p. 197-208.

Publication: Research - peer-reviewJournal article – Annual report year: 2012

Bibtex

@article{be1554c94ecb403f9a54c11ea20847aa,
title = "Implementing an online tool for genome-wide validation of survival-associated biomarkers in ovarian-cancer using microarray data from 1287 patients",
publisher = "Society for Endocrinology",
author = "Balázs Győrffy and András Lánczky and Zoltán Szállási",
year = "2012",
doi = "10.1530/ERC-11-0329",
volume = "19",
number = "2",
pages = "197--208",
journal = "Endocrine - Related Cancer",
issn = "1351-0088",

}

RIS

TY - JOUR

T1 - Implementing an online tool for genome-wide validation of survival-associated biomarkers in ovarian-cancer using microarray data from 1287 patients

A1 - Győrffy,Balázs

A1 - Lánczky,András

A1 - Szállási,Zoltán

AU - Győrffy,Balázs

AU - Lánczky,András

AU - Szállási,Zoltán

PB - Society for Endocrinology

PY - 2012

Y1 - 2012

N2 - 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 1287 ovarian cancer patients downloaded from Gene Expression Omnibus and The Cancer Genome Atlas (Affymetrix HG-U133A, HG-U133A 2.0, and HG-U133 Plus 2.0 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, we divided the patients into two groups according to various quantile expressions of the gene. These groups were then compared using progression-free survival (n=1090) or overall survival (n=1287). A Kaplan–Meier survival plot was generated and significance was 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 (MUC16; P=3.7x10–5, hazard ratio (HR)=1.4), CDKN1B (P=5.4x10–5, 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 1287 ovarian cancer patients. We specifically used this tool to evaluate the effect of 37 previously published biomarkers on ovarian cancer prognosis.

AB - 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 1287 ovarian cancer patients downloaded from Gene Expression Omnibus and The Cancer Genome Atlas (Affymetrix HG-U133A, HG-U133A 2.0, and HG-U133 Plus 2.0 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, we divided the patients into two groups according to various quantile expressions of the gene. These groups were then compared using progression-free survival (n=1090) or overall survival (n=1287). A Kaplan–Meier survival plot was generated and significance was 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 (MUC16; P=3.7x10–5, hazard ratio (HR)=1.4), CDKN1B (P=5.4x10–5, 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 1287 ovarian cancer patients. We specifically used this tool to evaluate the effect of 37 previously published biomarkers on ovarian cancer prognosis.

U2 - 10.1530/ERC-11-0329

DO - 10.1530/ERC-11-0329

JO - Endocrine - Related Cancer

JF - Endocrine - Related Cancer

SN - 1351-0088

IS - 2

VL - 19

SP - 197

EP - 208

ER -