Identification of key genes and pathways for cholangiocarcinoma using an integrated bioinformatics analysis

Abstract

INTRODUCTION: The scope of this study was to identify potential genes as a promising biomarker in diagnosing cholangiocarcinoma (CCA) or differentiating the subtypes of CCA. In this study, we used Gene Expression Omnibus (GEO)-NCBI data sets as promising open sources to perform integrative analysis. METHODS: The gene expression data sets of intrahepatic CCA (iCCA) and extrahepatic CCA (eCCA) were retrieved from GEO, and the statistical analysis of GSE45001 (iCCA), GSE76311 (iCCA), and GSE132305 (eCCA) was performed to identify significantly expressed genes. The association of listed genes with CCA was checked via text-mining approaches. For CCA, the details were provided by discussing its relations with our results. Then, the pathway analysis was performed to identify common pathways both in iCCA and eCCA. RESULTS: The pathway analysis reveals that although there are common pathways between iCCA and eCCA, the associated genes within these pathways are different from one another. According to the results of upregulated gene sets, integrin cell surface interaction (R-HSA 216083), MET activates PTK2 signaling (R-HSA-8874081), degradation of the extracellular matrix (ECM) (R-HSA-1474228), nonintegrin membrane–ECM interaction (R-HSA-3000171), and assembly of collagen fibrils and other multimeric structures (R-HSA-2022090) are found as common pathways among these data sets, yet there is no reported common pathway within downregulated gene sets. A detailed study of common pathway analysis shows that COL1A1 and COL1A2 genes, whose associations with CCA have not been reported, seem promising to differentiate iCCA from eCCA. The pathway analysis also reveals that although there are common pathways between iCCA and eCCA, the associated genes within these pathways are different from one another. DISCUSSION AND CONCLUSION: Focusing on pathways rather than genes is more promising for revealing the potential biomarkers together with providing a deeper understanding by highlighting significant pathways.

Keywords

• col1a1
• col1a2
• ecca
• gene expression study
• geo data sets
• icca