Yixin Sun1,2, Zhiming Zhang2. 1. School of Medicine, Xiamen University, Xiamen, China. 2. Department of Surgery, The First Affiliated Hospital of Xiamen University, Xiamen, Fujian, China.
Abstract
Background: Hepatocellular carcinoma (HCC) is one of the leading causes of cancer-related deaths worldwide. Patients suffering from HCC are usually diagnosed during an advanced stage, which limits the effectiveness of treatment. This phenomenon has led to an urgent need to discover promising HCC diagnostic biomarkers and to identify novel targets for HCC treatment. Materials and Methods: In this study, the gene expression profiles of the GSE45436 participants were downloaded from the Gene Expression Omnibus database. The HCC differentially expressed genes (HCC_DEGs) were identified through a comparison with healthy controls. The Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses were performed by DAVID, a free website used for annotating genes. Next, we used STRING, an online website, to identify likely protein-protein interactions among the DEGs. Cytoscape software was utilized to construct a protein-protein interaction network. MCODE, a plug-in of the Cytoscape software, was used for a module analysis. Finally, we used the Gene Expression Profiling Interactive Analysis website to determine the module genes' effects on overall survival. Results: A total of 313 genes were identified as differentially expressed, which comprised 118 upregulated genes and 195 downregulated genes. We used these data to identify 67 module genes. These were further verified using The Cancer Genome Atlas database resulting in 57 that remained statistically significant. Foremost, we identified one significant gene, DEP domain-containing protein 1B (DEPDC1B), which should be investigated for its usefulness as a new biomarker for diagnoses and prognoses. Conclusion: To our knowledge, DEPDC1B has not previously been reported as being associated with HCC. These results suggest that in silico methods, such as those employed, can provide valuable and even unique candidate biomarkers for further evaluation.
Background: Hepatocellular carcinoma (HCC) is one of the leading causes of cancer-related deaths worldwide. Patients suffering from HCC are usually diagnosed during an advanced stage, which limits the effectiveness of treatment. This phenomenon has led to an urgent need to discover promising HCC diagnostic biomarkers and to identify novel targets for HCC treatment. Materials and Methods: In this study, the gene expression profiles of the GSE45436 participants were downloaded from the Gene Expression Omnibus database. The HCC differentially expressed genes (HCC_DEGs) were identified through a comparison with healthy controls. The Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses were performed by DAVID, a free website used for annotating genes. Next, we used STRING, an online website, to identify likely protein-protein interactions among the DEGs. Cytoscape software was utilized to construct a protein-protein interaction network. MCODE, a plug-in of the Cytoscape software, was used for a module analysis. Finally, we used the Gene Expression Profiling Interactive Analysis website to determine the module genes' effects on overall survival. Results: A total of 313 genes were identified as differentially expressed, which comprised 118 upregulated genes and 195 downregulated genes. We used these data to identify 67 module genes. These were further verified using The Cancer Genome Atlas database resulting in 57 that remained statistically significant. Foremost, we identified one significant gene, DEP domain-containing protein 1B (DEPDC1B), which should be investigated for its usefulness as a new biomarker for diagnoses and prognoses. Conclusion: To our knowledge, DEPDC1B has not previously been reported as being associated with HCC. These results suggest that in silico methods, such as those employed, can provide valuable and even unique candidate biomarkers for further evaluation.