A gene sets approach for identifying prognostic gene signatures for outcome prediction.

BMC Genomics
Seon-Young Kim, Yong Sung Kim

Abstract

Gene expression profiling is a promising approach to better estimate patient prognosis; however, there are still unresolved problems, including little overlap among similarly developed gene sets and poor performance of a developed gene set in other datasets. We applied a gene sets approach to develop a prognostic gene set from multiple gene expression datasets. By analyzing 12 independent breast cancer gene expression datasets comprising 1,756 tissues with 2,411 pre-defined gene sets including gene ontology categories and pathways, we found many gene sets that were prognostic in most of the analyzed datasets. Those prognostic gene sets were related to biological processes such as cell cycle and proliferation and had additional prognostic values over conventional clinical parameters such as tumor grade, lymph node status, estrogen receptor (ER) status, and tumor size. We then estimated the prediction accuracy of each gene set by performing external validation using six large datasets and identified a gene set with an average prediction accuracy of 67.55%. A gene sets approach is an effective method to develop prognostic gene sets to predict patient outcome and to understand the underlying biology of the developed gene set. Using...Continue Reading

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Citations

Mar 3, 2010·BMC Genomics·Marina BessarabovaTatiana Nikolskaya
Mar 6, 2012·BMC Genomics·Chang ChangTieliu Shi
Jul 1, 2009·BMC Cancer·Jonathan D Mosley, Ruth A Keri
May 9, 2014·IEEE Journal of Biomedical and Health Informatics·Stelios SfakianakisDimitrios Kafetzopoulos
Jul 27, 2021·Computational and Structural Biotechnology Journal·Jeffrey Yung-Chuan ChaoWei-Chung Cheng
Sep 23, 2020·Journal of Biomedical Informatics·Mohammadreza MomenzadehHossein Rabbani

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Software Mentioned

Affymetrix
MAS5
python programming language
GSEA
R language
GEPAS

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