BACKGROUND: Intrahepatic cholangiocarcinoma arises from the epithelial cells of the bile ducts and is associated with poor prognosis. This study aimed to use bioinformatics analysis to identify molecular biomarkers of intrahepatic cholangiocarcinoma and their potential mechanisms.

MATERIAL AND METHODS: MicroRNA (miRNA) and mRNA microarrays from GSE53870 and GSE32879 were downloaded from the Gene Expression Omnibus (GEO) database. Differentially expressed miRNAs (DEMs) associated with prognosis were identified using limma software and Kaplan-Meier survival analysis. Predictive target genes of the DEMs were identified using miRWalk, miRTarBase, miRDB, and TargetScan databases of miRNA-binding sites and targets. Target genes underwent Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. Hub genes were analyzed by constructing the protein-protein interaction (PPI) network using Cytoscape. DEMs validated the hub genes, followed by construction of the miRNA-gene regulatory network.

RESULTS: Twenty-five DEMs were identified. Fifteen DEMs were upregulated, and ten were down-regulated. Kaplan-Meier survival analysis identified seven upregulated DEMs and nine down-regulated DEMs that were associated with the overall survival (OS), and 130 target genes were selected. GO analysis showed that target genes were mainly enriched for metabolism and development processes. KEGG analysis showed that target genes were mainly enriched for cancer processes and some signaling pathways. Fourteen hub genes identified from the PPI network were associated with the regulation of cell proliferation. The overlap between hub genes and DEMs identified the estrogen receptor 1 (ESR1) gene and hsa-miR-26a-5p.

CONCLUSIONS: Bioinformatics analysis identified ESR1 and hsa-miR-26a-5p as potential prognostic biomarkers for intrahepatic cholangiocarcinoma.

Keywords: Biological Markers, Cholangiocarcinoma, Bile Duct Neoplasms, Biomarkers, Tumor, Computational Biology, Databases, Genetic, Estrogen Receptor alpha, gene ontology, Gene Regulatory Networks, Protein Interaction Maps, RNA, Messenger, Software