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浙江大学学报（医学版）
J Zhejiang Univ (Med Sci)  2019, Vol. 48 Issue (2): 148-157    DOI: 10.3785/j.issn.1008-9292.2019.04.05
    
Identification of differentially expressed genes in peripheral blood mononuclear cells of patients with hepatocellular carcinoma and its regulatory network analysis
LUN Yongzhi(),SUN Jie
Department of Laboratory Medicine, School of Pharmacy and Medical Technology, Putian University, Putian 351100, Fujian Province, China
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Abstract  

Objective: To identify the differentially expressed genes (DEGs) in peripheral blood mononuclear cells (PBMC) of patients with hepatocellular carcinoma (HCC) and to analyze their regulatory network. Methods: The DEGs in PBMCs of HCC patients were screened based on GEO database. The functional enrichment analysis and interaction analysis were carried out for DEGs. MCODE algorithm was used to screen core genes of DEGs, and the mirDIP and starBase online tools were used to predict upstream miRNAs and lncRNAs of the core genes. Results: A total of 265 DEGs with a high credibility were identified, which were mainly enriched in the biological activity, such as regulation of cell proliferation, metabolic regulation, cell communication and signaling, and inflammatory diseases according to Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway, and the two analyses were correlated. Four diagnostic candidate genes were identified, including FUS RNA binding protein, C-X-C motif chemokine ligand 8, cullin 1 and RNA polymerase Ⅱ subunit H. Subsequently, 10 miRNAs, 1 lncRNAs and 38 circRNAs were predicted, and finally a lncRNA/circRNA-miRNA-mRNA-pathway regulatory networks was constructed. Conclusion: The diagnostic candidate genes and its regulatory network in HCC PBMC have been identified based on data mining, which could provide potential tumor biomarkers for early diagnosis and treatment of HCC.

Key words： Carcinoma, hepatocellular/pathology      Leukocytes, mononuclear/metabolism      MicroRNAs      Genes      Gene expression profiling      Oligonucleotide array sequence analysis      Gene expression regulation, neoplastic      Computer communication networks      Automatic data processing     
Received: 26 February 2019      Published: 24 July 2019
CLC:  R735.7  
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Cite this article:

LUN Yongzhi,SUN Jie. Identification of differentially expressed genes in peripheral blood mononuclear cells of patients with hepatocellular carcinoma and its regulatory network analysis. J Zhejiang Univ (Med Sci), 2019, 48(2): 148-157.

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http://www.zjujournals.com/med/10.3785/j.issn.1008-9292.2019.04.05     OR     http://www.zjujournals.com/med/Y2019/V48/I2/148


肝细胞癌患者外周血单个核细胞诊断候选基因的筛选及其调控网络分析

目的: 通过筛选肝细胞癌（HCC）患者外周血单个核细胞（PBMC）诊断候选基因并分析其上游互作微小RNA（miRNA）、长链非编码RNA（lncRNA）、环状（circRNA）和参与的通路，探讨HCC发生、发展过程中的调控机制并寻找可用于临床诊疗的分子靶点。方法: 利用GEO数据库筛选HCC患者PBMC中的差异表达基因集，分别进行功能富集及互作分析，继而利用网络模块划分方法寻找差异表达基因中的诊断候选基因，再利用mirDIP、starBase在线工具对诊断候选基因的上游miRNA、lncRNA、circRNA进行预测。结果: 获得高可信度的差异表达基因265个，差异表达基因主要富集于增殖调控、代谢调节、细胞通信、炎症疾病等功能，基因本体及KEGG通路富集结果相互关联。筛选获得4个诊断候选基因，包括RNA结合蛋白FUS、C-X-C基序趋化因子配体8、卡林蛋白和RNA聚合酶Ⅱ亚单位H。预测到10个miRNA、1个lncRNA和38个circRNA符合筛选标准，最后构建出一个lncRNA/circRNA-miRNA-mRNA-通路调控网络。结论: 本研究基于数据挖掘方法筛选获得HCC患者PBMC中的诊断候选基因及其调控网络，为HCC的早期诊断和合理治疗提供了理论依据，有助于寻找新的肿瘤标志物。


关键词： 癌, 肝细胞/病理学,  白细胞, 单核/代谢,  微RNAs,  基因,  基因表达谱,  寡核苷酸序列分析,  基因表达调控, 肿瘤,  计算机通信网络,  自动数据处理 
基因名称 log差异倍数 P值 调整后的P值
CXCL8 4.628 44 5.79×10-5 9.60×10-4
HBEGF 4.612 83 1.30×10-9 1.27×10-6
GAS2L1 4.472 17 3.33×10-4 3.62×10-3
G0S2 4.317 09 1.27×10-4 1.74×10-3
JUN 4.128 23 2.62×10-7 2.34×10-5
BRE-AS1 3.965 83 1.57×10-7 1.68×10-5
ELF2 3.899 70 1.78×10-8 4.68×10-6
DAO 3.885 54 1.62×10-8 4.42×10-6
PHACTR1 3.838 04 1.13×10-6 6.20×10-5
NR4A2 3.802 09 2.18×10-6 9.65×10-5
HBB -5.193 56 6.29×10-5 1.02×10-3
WBP2 -4.770 88 8.56×10-5 1.28×10-3
HBA2 -4.674 73 2.14×10-7 2.05×10-5
CCNE2 -4.581 23 1.38×10-4 1.85×10-3
ABAT -4.161 59 1.21×10-6 6.50×10-5
CREB5 -3.947 77 1.76×10-7 1.80×10-5
SH3YL1 -3.922 51 8.41×10-7 4.97×10-5
ITGA10 -3.509 37 9.03×10-8 1.18×10-5
KLRD1 -3.437 81 2.94×10-5 5.82×10-4
C8B -3.355 26 2.78×10-8 6.06×10-6
Tab 1 Top 10 differentially expressed genes screened from GEO database
Fig 1 Gene ontology (GO) enrichment analysis results of differentially expressed genes
Fig 2 KEGG pathway enrichment analysis results of differentially expressed genes
Fig 3 Interaction analysis of differentially expressed genes
Fig 4 Interaction diagram of core genes
Fig 5 Venn analysis of core genes, hub genes and bottleneck genes
基因类别 基因名称
加粗显示的为诊断候选基因.
核心基因 GPSM2、CCR1、FPR2、P2RY12、LPAR5、P2RY13、
CXCL5、CXCL8；POLR2H、CRNKL1、PPIL1、
PRCC、TRA2B、FUS、U2SURP、SRSF3；KBTBD7、
CUL1、CISH、TULP4、FEM1B、KEAP1
关键基因 CXCL8、JUN、POLR2H、CUL1、FPR2、LPAR5、
FUS、RHOU、VEGFA、SRSF3、GPSM2、U2SURP、
P2RY13、P2RY12、RHOH、SOS1、RHOB、CXCL5、
PRCC、CCR1、KEAP1、PPIL1、CRNKL1、TRA2B
瓶颈基因 JUN、POLR2H、VEGFA、FUS、AURKA、SOS1、
CXCL8、CUL1
Tab 2 Screening result of diagnostic candidate genes
基因类别 名称
微小RNA hsa-miR-106a-5p、hsa-miR-106b-5p、hsa-miR-141-3p、hsa-miR-17-5p、hsa-miR-200a-3p、hsa-miR-203a-3p、hsa-miR-20a-5p、hsa-miR-20b-5p、hsa-miR-377-3p、hsa-miR-93-5p
长链非编码RNA MALAT1
环状RNA ADAM9、ANKFY1、ARF1、ARPC5、ATP5G3、BLOC1S3、CAPZB、CHD4、DAB2、DUSP1、EIF3B、EIF3F、FABP5、FAM168B、FLNA、FTL、GLYR1、hsa_circ_002179、hsa_circ_0044175、IBTK、LASP1、MYL6、MYO1D、NAP1L1、NCOA6、NUP205、PANK2、PARP6、PHF5A、PRC1、RBM34、TCONS_l2_00025633、TNPO1、TRIP10、TUBA1A、TUBA1C、U2SURP、ZNF706
Tab 3 Analysis of miRNA, lncRNA, circRNA of upstream interaction of diagnostic candidate genes
Fig 6 Regulatory network of lncRNA/circRNA-miRNA-mRNA-pathway
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