Glycogene Expression Profiling of Hepatic Cells by RNA-Seq Analysis for Glyco-Biomarker Identification
Glycogene Expression Profiling of Hepatic Cells by RNA-Seq Analysis for Glyco-Biomarker Identification
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DOI：
10.3389/fonc.2020.01224
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发表时间：
2020-07-28
影响因子：
4.7
通讯作者：
Narimatsu, Hisashi
中科院分区：
文献类型：
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作者：
Angata, Kiyohiko;Sawaki, Hiromichi;Narimatsu, Hisashi
聚糖主要由“糖基因”产生，其由200多个用于糖合成的基因组成，包括糖-核苷酸转移酶、糖-核苷酸转运蛋白和糖基转移酶。测定糖基因的表达水平是分析特定生物和临床样品的糖组的方法之一。为了开发用于鉴定糖基化生物标志物的有效策略，我们使用定量实时聚合酶链反应（qRT-PCR）阵列和RNA测序（RNA-Seq）进行转录组分析。首先，我们使用RNA-Seq测量和分析了来自人肝细胞和肝癌细胞的原代培养物的转录组。该分析揭示了如qRT-PCR阵列所示的肝细胞中糖基因的相似但独特的表达谱，其确定了186个糖基因的拷贝数。两个数据集均表明糖基转移酶的表达改变影响特定糖蛋白的糖基化，这与质量分析数据一致。此外，RNA-Seq分析可以发现糖基因中的突变，并从50，000多种不同的人类基因转录物中搜索不同表达的基因，包括先前报道的肝癌细胞的候选生物标志物。从可从公共数据库获得的肝癌组织的糖基因和蛋白质的表达谱中鉴定候选糖生物标志物强调了这样的可能性，即即使生物标志物的表达水平可能不改变，但修饰生物标志物、产生糖生物标志物的糖基因的表达可能不同。通路分析显示，大约20%的糖基因在正常细胞和癌细胞中表现出不同的表达水平。因此，使用qRT-PCR阵列和RNA-Seq的转录组分析与糖组和糖蛋白质组分析相结合，通过加强糖基因和蛋白质表达水平的信息，可以有利于鉴定“糖生物标志物”。
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 similar to 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.