ABSTRACT
Hepatocellular carcinoma (HCC) is a common cancer and the sixth most lethal malignancy in the world. We chose gene expression profile of GSE14520 from GEO database aiming to find key genes that affect HCC progression. 22 paired tumor and non-tumor samples were included in this analysis. Differentially expressed genes (DEGs) between tumor and non-tumor were selected using GEO2R. Gene ontology (GO) enrichment and protein-protein interaction (PPI) of the DEGs were done using Metascape. There were 357 DEGs, including 70 up-regulated genes and 287 down-regulated genes. These DEGs were enriched in drug metabolic process, organic acid catabolic process, monocarboxylic metabolic process and etc. Three important modules were detected from PPI network using Molecular Complex Detection (MCODE) algorithm. Moreover, the Kaplan-Meier analysis for overall survival and disease-free survival were applied to those genes in top PPI group. In conclusion, this bioinformatic analysis demonstrated that DEGs, such as CYP2C9, might promote the development of HCC, especially in drug metabolism. It could also be used as a new biomarker for diagnosis.
Subject(s)
Biomarkers, Tumor/metabolism , Carcinoma, Hepatocellular/metabolism , Carcinoma, Hepatocellular/mortality , Cytochrome P-450 CYP2C9/metabolism , Liver Neoplasms/metabolism , Liver Neoplasms/mortality , Algorithms , Biomarkers, Tumor/genetics , Carcinoma, Hepatocellular/genetics , Cell Line, Tumor , Cytochrome P-450 CYP2C9/genetics , Databases, Genetic , Disease-Free Survival , Down-Regulation , Gene Expression Regulation, Neoplastic , Gene Ontology , Humans , Kaplan-Meier Estimate , Liver Neoplasms/genetics , Prognosis , Protein Interaction Maps , RNA, Messenger/genetics , RNA, Messenger/metabolism , Transcriptome , Up-RegulationABSTRACT
Cancer cell lines are useful tools to study cancer biology. Choosing proper cell lines based on experimental design for different experiments is vital. Relating tumors and cell lines, and recognizing their similarities and differences are thus very important for translational research. Abundant online databases with genomic and expression profile are suitable resources for conducting the assessment. Pancreatic ductal adenocarcinoma (PDAC) is a severe cancer with grim prognosis. Current effective treatments of PDAC remain limited. In this study, we compared the gene expression profile of 178 PDAC tumor samples from The Cancer Genome Atlas and 44 pancreatic cancer cell lines from Cancer Cell Line Encyclopedia. We showed that all pancreatic cancer cell lines resemble PDAC tumors but the correlation is different. Our study will be used to guide the selection of PDAC cell lines.
