
doi: 10.3233/cbm-190038
pmid: 32116234
BACKGROUND: Cholangiocarcinoma (CCA) is the most common biliary malignancy worldwide. However, the molecular mechanisms of its tumorigenesis and progression are still largely unclear. OBJECTIVE: This study aimed to explore the hub genes and pathways associated with CCA prognosis by coexpression analysis. METHODS: A coexpression network complex was constructed using the top 20% most variant genes in the GSE89748 dataset to find modules associated with prognosis related clinical trait-histology. The hub genes in the clinically significant modules were defined as candidates if they were common in both the coexpression network and protein-protein interaction (PPI) network. Afterwards, survival analysis, expression level analysis and a series of bioinformatic analysis were used to validate the hub genes. RESULTS: Twenty-five modules were obtained, and the cyan, light cyan and red modules regarded as closely associated with histology were selected. Subsequently, combining the PPI network complexes and coexpression networks, we screened 20 candidates. After expression and survival analysis, 10 real hub genes (LIMA1, HDAC1, ITGA3, ACTR3, GSK3B, ITGA2, THOC2, PTGES3, HEATR1 and ILF2) were finally identified. Additionally, functional enrichment analysis revealed that the hub genes were mainly enriched in cell cycle-related pathways. CONCLUSIONS: Overall, this study identified 10 hub genes and cell cycle-related pathways were closely related to CCA development, progression and prognosis, which may contribute to CCA diagnosis and treatment.
Gene Expression Profiling, Cell Cycle, Computational Biology, Prognosis, Cholangiocarcinoma, Survival Rate, Bile Duct Neoplasms, Databases, Genetic, Biomarkers, Tumor, Disease Progression, Humans, Gene Regulatory Networks, Protein Interaction Maps
Gene Expression Profiling, Cell Cycle, Computational Biology, Prognosis, Cholangiocarcinoma, Survival Rate, Bile Duct Neoplasms, Databases, Genetic, Biomarkers, Tumor, Disease Progression, Humans, Gene Regulatory Networks, Protein Interaction Maps
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