Abstract
AimsThe purpose of this study was to propose a pipeline to identify prognostic signature for HCC overall survival (OS) prediction based on HCC gene expression datasets from The Cancer Genome Atlas (TCGA). ResultsDifferential expression analysis identified 3573 genes aberrantly expressed (DEGs) in HCC samples. Univariate cox regression analysis obtained 1605 and 1067 HCC OS and relapse free survival (RFS) related genes, which are abbreviated as OS-Gene and RFS-Gene respectively. Besides, there are 55 overlaps among DEGs, OS-Genes and RFS-Genes. Further prioritization of the 55 overlapping genes through Sure Independence Screening (SIS) resulted in 6 genes, including SRL, TTC26, CPSF2, TAF3, C16orf46 and CSN1S1, and the prognostic signature is the weighted combination of their expression values. Kaplan-Meier analysis based on the prognostic score (PS) of every sample indicates higher PS is associated with better HCC OS. Robustness of the prognostic signature was evaluated through another HCC gene expression datasets from the Gene Expression Omnibus (GEO). What's more, univariate and multivariate cox regression analysis indicate significant associations between stage/PS and HCC OS. ConclusionsOur study provides a pipeline for the identification of prognostic signature for HCC OS prediction, which should also be suit for other types of cancers.
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