Please wait a minute...
浙江大学学报（医学版）
浙江大学学报（医学版）  2019, Vol. 48 Issue (2): 148-157    DOI: 10.3785/j.issn.1008-9292.2019.04.05
原著     
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
伦永志(),孙杰
莆田学院药学与医学技术学院医学检验系, 福建 莆田 351100
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
 全文: PDF(1276 KB)   HTML( 15 )
摘要：

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    
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
收稿日期: 2019-02-26 出版日期: 2019-07-24
CLC:  R735.7  
基金资助: 2018年福建省高等学校新世纪优秀人才支持计划;福建省自然科学基金(2018J01584);福建省中青年教师教育科研项目(JT180481)
作者简介: 伦永志(1973-), 男, 博士, 教授, 主要从事感染性疾病的分子生物学研究; E-mail:lunyz@163.com; https://orcid.org/0000-0002-7947-9274
服务　　
把本文推荐给朋友
加入引用管理器
E-mail Alert
RSS
作者相关文章　　
伦永志
孙杰

引用本文:

伦永志,孙杰. 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[J]. 浙江大学学报（医学版）, 2019, 48(2): 148-157.

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.

链接本文:

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

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
表 1  从GEO数据库中筛选出的前10位差异表达基因
图 1  差异表达基因集基因本体(GO)富集分析结果
图 2  差异表达基因集京都基因与基因组百科全书(KEGG)通路富集分析结果
图 3  差异表达基因集互作分析结果
图 4  核心基因互作关系图
图 5  核心基因、关键基因和瓶颈基因的韦恩分析结果
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
表 2  诊断候选基因筛选结果
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
表 3  诊断候选基因上游互作miRNA、lncRNA、circRNA分析结果
图 6  长链非编码RNA/环状RNA-微小RNA-mRNA-通路调控网络
1 KULIK L , EL-SERAG H B . Epidemiology and management of hepatocellular carcinoma[J]. Gastroenterology, 2019, 156 (2): 477- 491.e1
doi: 10.1053/j.gastro.2018.08.065
2 TORRE L A , BRAY F , SIEGEL R L et al. Global cancer statistics, 2012[J]. CA Cancer J Clin, 2015, 65 (2): 87- 108
doi: 10.3322/caac.21262
3 VITALE A , PECK-RADOSAVLJEVIC M , GIANNINIE G et al. Personalized treatment of patients with very early hepatocellular carcinoma[J]. J Hepatol, 2017, 66 (2): 412- 423
doi: 10.1016/j.jhep.2016.09.012
4 BERRETTA M , CAVALIERE C , ALESSANDRINI L et al. Serum and tissue markers in hepatocellular carcinoma and cholangiocarcinoma:clinical and prognostic implications[J]. Oncotarget, 2017, 8 (8): 14192- 14220
5 SHI M , CHEN M S , SEKAR K et al. A blood-based three-gene signature for the non-invasive detection of early human hepatocellular carcinoma[J]. Eur J Cancer, 2014, 50 (5): 928- 936
doi: 10.1016/j.ejca.2013.11.026
6 DAVIS S , MELTZER P S . GEOquery:a bridge between the Gene Expression Omnibus (GEO) and BioConductor[J]. Bioinformatics, 2007, 23 (14): 1846- 1847
doi: 10.1093/bioinformatics/btm254
7 TRIPATHI S , POHL M O , ZHOU Y et al. Meta-and orthogonal integration of influenza "omics" data defines a role for ubr4 in virus budding[J]. Cell Host Microbe, 2015, 18 (6): 723- 735
doi: 10.1016/j.chom.2015.11.002
8 SZKLARCZYK D , MORRIS J H , COOK H et al. The STRING database in 2017:quality-controlled protein-protein association networks, made broadly accessible[J]. Nucleic Acids Res, 2017, 45 (D1): D362- D368
doi: 10.1093/nar/gkw937
9 TOKAR T , PASTRELLO C , AEM R et al. mirDIP 4.1-integrative database of human microRNA target predictions[J]. Nucleic Acids Res, 2018, 46 (D1): D360- D370
doi: 10.1093/nar/gkx1144
10 LI J H , LIU S , ZHOU H et al. starBase v2.0:decoding miRNA-ceRNA, miRNA-ncRNA and protein-RNA interaction networks from large-scale CLIP-Seq data[J]. Nucleic Acids Res, 2014, 42 (Database issue): D92- D97
11 SINGAL A G . The efficacy and effectiveness of hepatocellular carcinoma surveillance in patients with cirrhosis[J]. Hepat Oncol, 2015, 2 (2): 97- 99
doi: 10.2217/hep.14.38
12 CHAUHAN R , LAHIRI N . Tissue-and serum-associated biomarkers of hepatocellular carcinoma[J]. Biomark Cancer, 2016, 8 (Suppl 1): 37- 55
13 SINGH A K , KUMAR R , PANDEY A K . Hepatocellular carcinoma:causes, mechanism of progression and biomarkers[J]. Curr Chem Genom Transl Med, 2018, 12 9- 26
doi: 10.2174/2213988501812010009
14 刘琼, 顾浩, 刘骏 et al. 基于熵值的尿路感染疾病基因网络的模块划分与生物学机制分析[J]. 基因组学与应用生物学, 2018, 37 (10): 4676- 4681
LIU Qiong , GU Hao , LIU Jun et al. Module partition and biological mechanism analysis of genetic network of urinary tract infection based on entropy[J]. Genomics and Applied Biology, 2018, 37 (10): 4676- 4681
15 KLUNGBOONKRONG V , DAS D , MCLENNAN G . Molecular mechanisms and targets of therapy for hepatocellular carcinoma[J]. J Vasc Interv Radiol, 2017, 28 (7): 949- 955
doi: 10.1016/j.jvir.2017.03.002
16 MICHALOPOULOS G K . Liver regeneration after partial hepatectomy:critical analysis of mechanistic dilemmas[J]. Am J Pathol, 2010, 176 (1): 2- 13
doi: 10.2353/ajpath.2010.090675
17 PARADIS V , YOUSSEF N , DARGèRE D et al. Replicative senescence in normal liver, chronic hepatitis C, and hepatocellular carcinomas[J]. Hum Pathol, 2001, 32 (3): 327- 332
doi: 10.1053/hupa.2001.22747
18 SHARMA A K , KUMAR S , CHASHOO G et al. Cell cycle inhibitory activity of Piper longum against A549 cell line and its protective effect against metal-induced toxicity in rats[J]. Indian J Biochem Biophys, 2014, 51 (5): 358- 364
19 EGUCHI A , WREE A , FELDSTEIN A E . Biomarkers of liver cell death[J]. J Hepatol, 2014, 60 (5): 1063- 1074
doi: 10.1016/j.jhep.2013.12.026
20 FABREGAT I . Dysregulation of apoptosis in hepatocellular carcinoma cells[J]. World J Gastroenterol, 2009, 15 (5): 513- 520
doi: 10.3748/wjg.15.513
21 CAZANAVE S C , MOTT J L , ELMI N A et al. JNK1-dependent PUMA expression contributes to hepatocyte lipoapoptosis[J]. J Biol Chem, 2009, 284 (39): 26591- 26602
doi: 10.1074/jbc.M109.022491
22 YANG Y , LIU Q , LU J et al. Exosomes from Plasmodium-infected hosts inhibit tumor angiogenesis in a murine Lewis lung cancer model[J]. Oncogenesis, 2017, 6 (6): e351
doi: 10.1038/oncsis.2017.52
23 BAO L , YUAN L , LI P et al. A FUS-LATS1/2 axis inhibits hepatocellular carcinoma progression via activating hippo pathway[J]. Cell Physiol Biochem, 2018, 50 (2): 437- 451
doi: 10.1159/000494155
24 LIU Q, ZHOU Y, TANG R, et al. Increasing the unneddylated cullin1 portion rescues the csn phenotypes by stabilizing adaptor modules to drive SCF assembly[J/OL]. Mol Cell Biol, 2017, 37(23): e00109-17.
25 LI Y , WU J , ZHANG P . CCL15/CCR1 axis is involved in hepatocellular carcinoma cells migration and invasion[J]. Tumour Biol, 2016, 37 (4): 4501- 4507
doi: 10.1007/s13277-015-4287-0
26 AWAN F M , NAZ A , OBAID A et al. MicroRNA pharmacogenomics based integrated model of miR-17-92 cluster in sorafenib resistant HCC cells reveals a strategy to forestall drug resistance[J]. Sci Rep, 2017, 7 (1): 11448
doi: 10.1038/s41598-017-11943-1
27 BAO L , YUAN L , LI P et al. A FUS-LATS1/2 Axis Inhibits Hepatocellular Carcinoma Progression via Activating Hippo Pathway[J]. Cell Physiol Biochem, 2018, 50 (2): 437- 451
doi: 10.1159/000494155
[1] 杨坤,胡晓晟. 微小RNA-21在心脏疾病中的研究进展[J]. 浙江大学学报（医学版）, 2019, 48(2): 214-218.
[2] 洪萍萍,郭冰洁,林莉,林细华,周嘉强. 葡萄糖激酶W257R突变致青少年发病的成人型糖尿病一家系分析[J]. 浙江大学学报（医学版）, 2019, 48(2): 200-203.
[3] 杜东芬,朱丽霞,王云贵,叶琇锦. 肾母细胞瘤1基因表达及其对急性髓系白血病患者预后的预测价值[J]. 浙江大学学报（医学版）, 2019, 48(1): 50-57.
[4] 唐思阳,叶佳,李月舟. I1363T突变致人骨骼肌电压门控钠通道快失活受损的机制[J]. 浙江大学学报（医学版）, 2019, 48(1): 12-18.
[5] 孙博强,王琼艳,潘冬立. 单纯疱疹病毒潜伏和激活机制研究进展[J]. 浙江大学学报（医学版）, 2019, 48(1): 89-101.
[6] 尹黎,李歌,沈健,刘震杰. 遗传性易栓症筛查及相关基因检测分析[J]. 浙江大学学报（医学版）, 2018, 47(6): 606-611.
[7] 赵慧慧,汤慧芳. 基于基因敲除的炎性肠疾病复合动物模型研究进展[J]. 浙江大学学报（医学版）, 2018, 47(6): 665-670.
[8] 蔺莹,姚璎珈,梁喜才,时悦,孔亮,肖洪贺,吴雨桐,倪颖男,杨静娴. 蛇床子素通过上调微RNA-101a-3p抑制阿尔茨海默病细胞淀粉样前体蛋白表达[J]. 浙江大学学报（医学版）, 2018, 47(5): 473-479.
[9] 王安奇,刘欣跃. CXCL12及CXCR4基因多态性与冠心病发病风险和冠状动脉狭窄程度的相关性[J]. 浙江大学学报（医学版）, 2018, 47(5): 514-519.
[10] 史庭,叶琇锦. CCAAT增强子结合蛋白α与急性髓细胞白血病的发生[J]. 浙江大学学报（医学版）, 2018, 47(5): 552-557.
[11] 陈志强,米贤军,陈昂,段立锋,代新珍,邓文同. 免疫组织化学法检测子宫颈组织p16蛋白表达的石蜡切片厚度探讨[J]. 浙江大学学报（医学版）, 2018, 47(4): 362-366.
[12] 陈挺,赵正言,蒋萍萍,舒强. 高苯丙氨酸血症表型与基因型研究进展[J]. 浙江大学学报（医学版）, 2018, 47(3): 219-226.
[13] 田广烽,高慧,胡莎莎,舒强. 遗传和表观遗传机制在先天性心脏病中的研究进展[J]. 浙江大学学报（医学版）, 2018, 47(3): 227-238.
[14] 龚恒佩,刘祖望,陈妍月,张坚,程汝滨,黄真. 松针的显微及分子鉴别研究[J]. 浙江大学学报（医学版）, 2018, 47(3): 300-306.
[15] 李福山,房冉,饶琳,孟飞龙,赵小立. 外泌体在心血管疾病诊疗中的作用研究进展[J]. 浙江大学学报（医学版）, 2018, 47(3): 320-326.