| 基因表达谱分析肝细胞癌的特征基因 |
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| 引用本文： | 张萃萃1,2,邓为民1. 基因表达谱分析肝细胞癌的特征基因[J]. 天津医科大学学报, 2020, 0(6): 514-517 |
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| 作者姓名： | 张萃萃1 2 邓为民1 |
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| 作者单位： | 1.天津医科大学基础医学院免疫学系，天津300070；2.天津市血液中心发血科，天津300110 |
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| 摘 要： | 目的:生物信息学方法建立全面的肝细胞癌（HCC）差异表达基因谱以及初步筛选基于该恶性肿瘤样本的特征基因。方法:利用R语言中的edgeR包分析不同组之间差异表达的基因（DEGs）。利用STRING数据库分析预测蛋白质的功能联系和蛋白质相互作用。R语言的clusterProfiler包做GO（gene ontology）（包括biological process、molecular function和cellular component）及KEGG（kyoto encyclopedia of genes and genomes）通路富集分析。筛选差异基因显著富集的GO和KEGG分子途径。利用SLC7A11（solute carrier family 7 member 11）和CCDC14（coiled-coil domain-containing 14）基因作为肝细胞癌的标志物，利用ELISA法进行检测（确诊的HCC阳性患者为HCC组，共195例；HCC阴性正常人群为对照组，共107名），计算阳性率。结果:与癌旁对照样本相比，一共鉴定出454个差异表达基因，其中高表达基因234个，低表达基因220个。PPI方法进一步筛选出排名前10的重要潜在基因，其中SLC7A11和CCDC14两个基因排名靠前。利用富集分析，差异基因主要集中在neuroactive ligand-receptor interaction通路中。与对照组相比，HCC组SLC7A11和CCDC14存在显著的高表达[SLC7A11:（70.12±14.3） ng/mL比（23.12±7.11）ng/mL，t=6.17，P<0.001；CCDC14:（6.17±2.31）pg/mL比（2.13±0.84）pg/mL，t=5.35，P<0.001]。SLC7A11针对HCC检测阳性率达到80.2%，而CCDC14达到68.8%。结论:SLC7A11和CCDC14可以作为HCC的两个潜在的有效临床生物标志物。
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| 关 键 词： | 肝细胞癌 PPI方法 ELISA SLC7A11 CCDC14 |
Gene expression profiling reveals important characteristic genes in hepatocellular carcinoma |
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| ZHANG Cui-cui1,' target="_blank" rel="external">2,DENG Wei-min1. Gene expression profiling reveals important characteristic genes in hepatocellular carcinoma[J]. Journal of Tianjin Medical University, 2020, 0(6): 514-517 |
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| Authors: | ZHANG Cui-cui1,' target=" _blank" rel=" external" >2,DENG Wei-min1 |
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| Affiliation: | 1. Department of Immunology, School of Basic Medical Sciences, Tianjin Medical University, Tianjin 300070, China; 2.Department of Blood Distribution, Tianjin Blood Center, Tianjin 300110, China |
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| Abstract: | Objective: To establish a comprehensive differentially expressed gene profile of hepatocellular carcinoma（HCC） by bioinformatics method and to screen the potential characteristic genes based on the malignant tumor samples. Methods: The edge package of R language was performed to analyze the differentially expressed genes（DEGs） among different groups. The STRING database was generated for better predicting functional relationship of protein and protein-protein interaction. Meanwhile, the cluster profiler functional package of R language was processed to generate GO（gene ontology） （including biological process, molecular function and cellular component） and KEGG（kyoto encyclopedia of genes and genomes） enrichment assay. GO and KEGG molecular pathways with significant enrichment of differentially expressed genes were screened. In addition, SLC7A11（solute carrier family 7 member 11） and CCDC14（coiled-coil domain-containing 14） genes were developed as specific biomarkers for HCC. ELISA method was utilized to analyze selected gene expression inexternal clinical experiments （HCC positive patients with 195 cases were HCC group, as well as HCC negative normal people with 107 cases were control group）, and positive rate was calculated. Results: Compared with the control samples, 454 differentially expressed genes were identified, including 234 upregulated genes and 220 downregulated genes. PPI method further screened out the top 10 primary potential genes, of which SLC7A11 and CCDC14 ranked the top. Using the enrichment analysis, the differentially expressed genes were mainly concentrated in neuroactive ligand-receptor interaction pathway.The concentrations of SLC7A11 and CCDC14 were significantly higher in HCC positive patients group[SLC7A11:（70.12±14.3） ng/mL vs. （23.12±7.11） ng/mL，t = 6.17, P< 0.001; CCDC14: （6.17±2.31） pg/mL vs.（2.13±0.84）pg/mL, t= 5.35, P< 0.001]. The positive rate of SLC7A11 was 80.2% and of CCDC14 was 68.8% for HCC respectively. Conclusion: SLC7A11 and CCDC14 can be used as two potential effective clinical biomarkers for HCC detection. |
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| Keywords: | hepatocellular carcinoma PPI method ELISA SLC7A11 CCDC14 |
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