Gene expression signature-based prognostic risk score in gastric cancer.
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ABSTRACT:
SUBMITTER: Cho JY
PROVIDER: S-EPMC3078023 | biostudies-literature | 2011 Apr
REPOSITORIES: biostudies-literature
Publications
Gene expression signature-based prognostic risk score in gastric cancer.
Cho Jae Yong JY Lim Jae Yun JY Cheong Jae Ho JH Park Yun-Yong YY Yoon Se-Lyun SL Kim Soo Mi SM Kim Sang-Bae SB Kim Hoguen H Hong Soon Won SW Park Young Nyun YN Noh Sung Hoon SH Park Eun Sung ES Chu In-Sun IS Hong Waun Ki WK Ajani Jaffer A JA Lee Ju-Seog JS
Clinical cancer research : an official journal of the American Association for Cancer Research 20110329 7
<h4>Purpose</h4>Despite continual efforts to develop a prognostic model of gastric cancer by using clinical and pathologic parameters, a clinical test that can discriminate patients with good outcomes from those with poor outcomes after gastric cancer surgery has not been established. We aim to develop practical biomarker-based risk score that can predict relapse of gastric cancer after surgical treatment.<h4>Experimental design</h4>Microarray technologies were used to generate and analyze gene ...[more]
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2011-07-18 | E-GEOD-13861 | biostudies-arrayexpress
Project description:Despite continual efforts to establish pre-operative prognostic model of gastric cancer by using clinical and pathological parameters, a staging system that reliably separates patients with early and advanced gastric cancer into homogeneous groups with respect to prognosis does not exist. With use of microarray and quantitative RT-PCR technologies, we exploited series of experiments in combination with complementary data analyses on tumor specimens from 161 gastric cancer patients. Various statistical analyses were applied to gene expression data to uncover subgroups of gastric cancer, to identify potential biomarkers associated with prognosis, and to construct molecular predictor of risk from identified prognostic biomarkers.Two subgroups of gastric cancer with strong association with prognosis were uncovered. The robustness of prognostic gene expression signature was validated in independent patient cohort with use of support vector machines prediction model. For easy translation of our finding to clinics, we develop scoring system based on expression of six genes that can predict the likelihood of recurrence after curative resection of tumors. In multivariate analysis, our novel risk score was an independent predictor of recurrence (P=0.004) in cohort of 96 patients, and its robustness was validated in two other independent cohorts. We identified novel prognostic subgroups of gastric cancer that are distinctive in gene expression patterns. Six-gene signature and risk score derived from them has been validated for predicting the likelihood of survival at diagnosis.
2011-07-19 | GSE13861 | GEO
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