A proportional analysis method using non-kinetic real-time PCR

Glycogene Expression Profiling of Hepatic Cells by RNA-Seq Analysis for Glyco-Biomarker Identification

Glycans are primarily generated by “glycogenes,” which consist of more than 200 genes for glycosynthesis, including sugar-nucleotide synthases, sugar-nucleotide transporters, and glycosyltransferases. Measuring the expression level of glycogenes is one of the approaches to analyze the glycomes of particular biological and clinical samples. To develop an effective strategy for identifying the glycosylated biomarkers, we performed transcriptome analyses using quantitative real-time polymerase chain reaction (qRT-PCR) arrays and RNA sequencing (RNA-Seq). First, we measured and analyzed the transcriptome from the primary culture of human liver cells and hepatocarcinoma cells using RNA-Seq. This analysis revealed similar but distinctive expression profiles of glycogenes among hepatic cells as indicated by the qRT-PCR arrays, which determined a copy number of 186 glycogenes. Both data sets indicated that altered expression of glycosyltransferases affect the glycosylation of particular glycoproteins, which is consistent with the mass analysis data. Moreover, RNA-Seq analysis can uncover mutations in glycogenes and search differently expressed genes out of more than 50,000 distinct human gene transcripts including candidate biomarkers that were previously reported for hepatocarcinoma cells. Identification of candidate glyco-biomarkers from the expression profile of the glycogenes and proteins from liver cancer tissues available from public database emphasized the possibility that even though the expression level of biomarkers might not be altered, the expression of the glycogenes modifying biomarkers, generating glyco-biomarkers, might be different. Pathway analysis revealed that ~20% of the glycogenes exhibited different expression levels in normal and cancer cells. Thus, transcriptome analyses using both qRT-PCR array and RNA-Seq in combination with glycome and glycoproteome analyses can be advantageous to identify “glyco-biomarkers” by reinforcing information at the expression levels of both glycogenes and proteins.