Abstract
The present study aimed to construct a novel signature for indicating the prognostic outcomes of hepatocellular carcinoma (HCC). Gene expression profiles were downloaded from Gene Expression Omnibus (GEO), The Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium (ICGC) databases. The prognosis-related genes with differential expression were identified with weighted gene co-expression network analysis (WGCNA), univariate analysis, the least absolute shrinkage and selection operator (LASSO). With the stepwise regression analysis, a risk score was constructed based on the expression levels of five genes: Risk score = (−0.7736* CCNB2) + (1.0083* DYNC1LI1) + (−0.6755* KIF11) + (0.9588* SPC25) + (1.5237* KIF18A), which can be applied as a signature for predicting the prognosis of HCC patients. The prediction capacity of the risk score for overall survival was validated with both TCGA and ICGC cohorts. The 1-, 3- and 5-year ROC curves were plotted, in which the AUC was 0.842, 0.726 and 0.699 in TCGA cohort and 0.734, 0.691 and 0.700 in ICGC cohort, respectively. Moreover, the expression levels of the five genes were determined in clinical tumor and normal specimens with immunohistochemistry. The novel signature has exhibited good prediction efficacy for the overall survival of HCC patients.
Highlights
Hepatocellular carcinoma (HCC) has been one of the most prevalent cancers in the world with very poor prognosis [1]
There were 2568 up-regulated and 242 down-regulated differentially expressed gene (DEG) identified in The Cancer Genome Atlas (TCGA)-LIHC dataset
The protein genes interacted with DEGs were identified in TCGA and International Cancer Genome Consortium (ICGC) datasets
Summary
Hepatocellular carcinoma (HCC) has been one of the most prevalent cancers in the world with very poor prognosis [1]. The heterogeneity among HCC patients has been recognized and molecular genetic methods have revealed different subgroups associated with distinct overall outcomes. Identification of reliable diagnostic and prognostic biomarkers is critical for HCC treatment. TNM (tumor-node-metastasis) stage system was still commonly used to help predict HCC prognosis [6]. With the rapid development in genome-sequencing technologies and bioinformatic algorithms, many molecular signatures and genetic markers have been identified to improve the prognosis prediction in HCC patients [7,8]. Zhang et al established a 14-gene signature for predicting HCC outcome and recurrence [10]. Pinyol et al discovered a 146-gene signature for improving sorafenib’s effectiveness in treating HCC [11].
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