Identification of key pathways and potential biomarkers for intrahepatic cholangiocarcinoma based on integrated bioinformatics and evaluation of their diagnostic value

Abstract

Objective To identify the key pathways and potential biomarkers with diagnotic value for the development of intrahepatic cholangiocarcinoma (ICC) by integrating bioinformatics analysis. Methods We downloaded 3 high-quality datasets of ICC gene microarray (GSE26566, GSE32879 and GSE45001) from Gene Expression Omnibus (GEO) and the high-throughput sequencing data of cholangiocarcinoma (CCA) from the Cancer Genome Atlas (TCGA) database. Differentially expressed genes (DEGs) were screened using the Limma package in R software; Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were performed with Cluster Profiler package. Protein-protein interaction (PPI) network analysis of DEGs was subsequently carried out using the Search Tool for the Retrieval of Interacting Genes (STRING) database and subsequently visualized with Cytoscape. In addition, the transcriptional expression of hub genes was further verified by Gene Expression Profiling Interactive Analysis (GEPIA), and their diagnostic value was assessed by receiver operating characteristic (ROC) curve. Finally, the methylation level of the hub genes was determined using Diseasemeth 2.0. Results In total, 382 DEGs were screened and validated, including 90 up-regulated and 292 down-regulated genes. The up-regulated genes were noticeably enriched in PI3K-Akt signaling pathway, while the down-regulated in the carbon metabolism pathway. A PPI network was constructed with 289 nodes, and 6 hub genes were identified by PPI analysis: PRSS23, LGALS1, VCAN, COL5A1, ITGB1 and EHHADH. ROC curve results showed that all the hub genes distinguished tumor tissues from non-tumorous tissues. Conclusion Six hub genes are identified by integrative bioinformatics analysis, which may be potential molecular biomarkers for early diagnosis of ICC.

Keywords

• cholangiocarcinoma
• bioinformatics
• biomarker
• gene expression omnibus
• cancer genome atlas database