1. Gene Biomarkers Derived from Clinical Data of Hepatocellular Carcinoma.
- Author
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Qi J, Zhou J, Tang XQ, and Wang Y
- Subjects
- Autoantigens genetics, Autoantigens metabolism, Biomarkers, Tumor, Carcinoma, Hepatocellular metabolism, Cell Cycle Proteins genetics, Cell Cycle Proteins metabolism, Cytoskeletal Proteins genetics, Cytoskeletal Proteins metabolism, Databases, Factual, GTPase-Activating Proteins genetics, GTPase-Activating Proteins metabolism, Gene Expression Profiling, Genetic Markers, Humans, Intracellular Signaling Peptides and Proteins genetics, Intracellular Signaling Peptides and Proteins metabolism, Kinesins genetics, Kinesins metabolism, Liver Neoplasms metabolism, Membrane Proteins genetics, Membrane Proteins metabolism, Microtubule-Associated Proteins genetics, Microtubule-Associated Proteins metabolism, Protein Serine-Threonine Kinases genetics, Protein Serine-Threonine Kinases metabolism, Carcinoma, Hepatocellular genetics, Gene Expression, Genes, Neoplasm, Liver Neoplasms genetics, Protein Interaction Maps
- Abstract
Hepatocellular carcinoma (HCC) is a common cancer of high mortality, mainly due to the difficulty in diagnosis during its clinical stage. Here we aim to find the gene biomarkers, which are of important significance for diagnosis and treatment. In this work, 3682 differentially expressed genes on HCC were firstly differentiated based on the Cancer Genome Atlas database (TCGA). Co-expression modules of these differentially expressed genes were then constructed based on the weighted correlation network algorithm. The correlation coefficient between the co-expression module and clinical data from the Broad GDAC Firehose was thereafter derived. Finally, the interactive network of genes was then constructed. Then, the hub genes were used to implement enrichment analysis and pathway analysis in the Database for Annotation, Visualization and Integrated Discovery (DAVID) database. Results revealed that the abnormally expressed genes in the module played an important role in the biological process including cell division, sister chromatid cohesion, DNA repair, and G1/S transition of mitotic cell cycle. Meanwhile, these genes also enriched in a few crucial pathways related to Cell cycle, Oocyte meiosis, and p53 signaling. Via investigating the closeness centrality of the interactive network, eight gene biomarkers including the CKAP2, TPX2, CDCA8, KIFC1, MELK, SGO1, RACGAP1, and KIAA1524 gene were discovered, whose functions had been indeed revealed to be correlated with HCC. This study, therefore, suggests that the abnormal expression of those eight genes may be taken as gene biomarkers of HCC.
- Published
- 2020
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