Abstract
Hepatocellular carcinoma (HCC) is one of the top three cancer killers worldwide. To identify CNV-driven differentially expressed genes (DEGs) in HBV related HCC, this study integrated analysis of copy number variations (CNVs) and gene expression profiling. Significant genes in regions of CNVs were overlapped with those obtained from the expression profiling. 93 CNV-driven genes exhibiting increased expression in the duplicated regions and 45 showing decreased expression in the deleted regions were obtained, which duplications and deletions were mainly documented at chromosome 1 and 4. Functional and pathway enrichment analyses were performed using DAVID and KOBAS, respectively. They were mainly enriched in metabolic process and cell cycle. Protein-protein interaction (PPI) network was constructed by Cytoscape, then four hub genes were identified. Following, survival analyses indicated that only high NPM1 expression was significantly and independently associated with worse survival and increased recurrence in HCC patients. Moreover, this correlation remained significant in patients with early stage of HCC. In addition, we showed that NPM1 was overexpressed in HCC cells and in HCC versus adjacent non-tumor tissues. In conclusion, these results showed that integrated analysis of genomic and expression profiling might provide a powerful potential for identifying CNV-driven genes in HBV related HCC pathogenesis.
Highlights
Gene expression profiling by microarray analysis has been shown to be a powerful tool for the identification of cancer-related genes
A total of 13,839 copy number variations (CNVs) were identified in the 33 Hepatocellular carcinoma (HCC) genomes, including 5,457 copy number deletions and 8,382 copy number duplications (Supplementary Table 1)
CNVs were scattered across chromosome 1 to 22, and both of the highest duplication and deletion were found in chromosome 1
Summary
Gene expression profiling by microarray analysis has been shown to be a powerful tool for the identification of cancer-related genes. A large number of differentially expressed genes (DEGs) can be obtained through the analysis. Several studies have been conducted through integrated analysis of CNVs and gene expression profiling in HCC, but they were limited to the use of small sized tumor samples or relatively lower-resolution platforms[12, 13]. The gene expression profiling data were obtained from our previous studies (GSE14520). By integrating the analysis of CNVs and gene expression profiling to identify CNV-driven DEGs, we may light further insights of HBV related HCC development at a molecular level, and explore a clinically useful candidate gene for diagnosis, prognosis, and drug targets
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