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


RESULTS
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%.


CONCLUSIONS
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 the gene sets approach we identified many prognostic gene sets in breast cancer.
Kim
Seon-Young
SY
Human Genomics Laboratory, Functional Genomics Research Center, KRIBB, Daejeon 305-806, Korea. kimsy@kribb.re.kr
Kim
Yong Sung
YS
D001943:Breast Neoplasms / Q000235:genetics*; D019295:Computational Biology / Q000379:methods; D020869:Gene Expression Profiling / Q000379:methods*; D005796:Genes / Q000235:genetics*; D006801:Humans; D016019:Survival Analysis