Single-gene prognostic signatures for advanced stage serous ovarian cancer based on 1257 patient samples

摘要

Objective. We sought to identify stable single-gene prognostic signatures based on a large collection ofadvanced stage serous ovarian cancer (AS-OvCa) gene expression data and explore their functions.Methods. The empirical Bayes (EB) method was used to remove the batch effect and integrate 8 ovariancancer datasets. Univariate Cox regression was used to evaluate the association between gene andoverall survival (OS). The Database for Annotation, Visualization and Integrated Discovery (DAVID) toolwas used for the functional annotation of genes for Gene Ontology (GO) terms and Kyoto Encyclopediaof Genes and Genomes (KEGG) pathways. Results. The batch effect was removed by the EB method,and 1257 patient samples were used for further analysis. We selected 341 single-gene prognosticsignatures with FDR ＜ 0.05, in which 110 and 231 genes were positively and negatively associated withOS, respectively. The functions of these genes were mainly involved in extracellular matrix organization,focal adhesion and DNA replication which are closely associated with cancer. Conclusion. We used theEB method to remove the batch effect of 8 datasets, integrated these datasets and identified stableprognosis signatures for AS-OvCa.