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Journal of Southern Medical University logoLink to Journal of Southern Medical University
. 2019 May 20;39(5):540–546. [Article in Chinese] doi: 10.12122/j.issn.1673-4254.2019.05.07

miR-129-5p调控的COL1A1作为胃癌潜在治疗靶点的生物信息学分析

Bioinformatics analysis of COL1A1 regulated by miR-129-5p as a potential therapeutic target for gastric cancer

杨 万霞 1, 潘 云燕 1, 管 沛文 1, 李 雪 1, 尤 崇革 1,*
PMCID: PMC6743932  PMID: 31140417

Abstract

目的

应用生物信息学技术探索胃癌发病机制,为胃癌的防治提供生物信息学依据。

方法

用GEO2R在线工具分析GSE79973中胃癌组织和正常胃黏膜组织的差异表达基因(Differentially expressed genes, DEGs),通过DAVID数据库对DEGs进行GO分析和KEGG通路富集分析,然后通过STRING数据库构建蛋白质相互作用网络,用Cytoscape软件进行关键基因(Hub基因)筛选和功能模块分析,并在GEPIA数据库对Hub基因进行验证,用Target Scan数据库预测调控靶基因的microRNAs,并用OncomiR分析microRNAs在胃癌组织中的表达及其与生存预后的关系。

结果

共筛选出181个在胃癌中差异表达的基因。蛋白质互作网络筛选出10个Hub基因。DEGs功能分析主要涉及蛋白质消化吸收、PI3K-Akt信号通路、ECM-受体相互作用、血小板激活信号通路。GEPIA数据库验证显示COL1A1在胃癌组织中高表达,并和胃癌患者的不良预后有关。miR-129-5p与COL1A1 mRNA的3'UTR结合。与正常组织相比,miR-129-5p在胃癌组织中表达明显下调,且与胃癌患者预后具有一定相关性。

结论

miR-129-5p调控的COL1A1是胃癌潜在的治疗靶点。

Keywords: 胃癌, 差异表达基因, COL1A1, miR-129-5p, 生物信息学分析


胃癌是我国第2大肿瘤[1],5年生存率全球仅为10%[2],其高发病率和高死亡率严重威胁着人类健康,随着诊疗技术的发展,胃癌的发病率和死亡率在一些发达国家有稳步下降的趋势[3],然而,亚洲仍有很高的发病率[4],因此,深入探究胃癌的发病机制及新的治疗方法就显得尤为重要。近年来,大量生物标志物已应用于胃癌的早期诊断[5-6],然而,这些生物标志物并没有被很好的整合,并且,这些生物标志物可在多种肿瘤中被检测到[7-8],因此,对胃癌诊断和治疗的特异性靶点还需要进行深入的研究。

COL1A1是胶原家族的重要成员,被认为与癌症发生有关,COL1A1的异常表达在多种癌症中均有报道[9-11]。此外,Zang等[12]发现COL1A1在胃癌中存在差异表达,但COL1A1在胃癌中的临床意义仍不清楚。基因表达谱(GEO)数据库为癌症相关基因表达谱的生物信息学挖掘提供了可能[13]。本研究通过生物信息学方法筛选出胃癌芯片数据GSE79973中胃癌组织和正常胃黏膜组织的DEGs,对DEGs进行GO分析和KEGG通路富集分析,然后通过构建蛋白质-蛋白质相互作用(PPI)网络,筛选出Hub基因并验证,同时预测调控COL1A1的miRNAs,旨在为胃癌分子机制的进一步研究提供生物信息学依据,也为我们进行基因个体化治疗提供新的途径。

1. 材料和方法

1.1. 芯片数据来源

本研究从GEO (<a href="https://www.ncbi.nlm.nih.gov/geo/">https://www.ncbi.nlm.nih.gov/geo/</a>)数据库下载基因芯片数据集GSE79973,芯片总共包含20例样本,其中10例正常胃黏膜组织和10例胃腺癌组织样本,其芯片平台是GPL570[HG-U133_Plus_ 2] Affymetrix Human Genome U133 Plus 2.0 Array,表达数据为expression profiling by array,种属为Homo sapiens。

1.2. 数据处理

用GEO2R(<a href="https://www.ncbi.nlm.nih.gov/geo/geo2r/">https://www.ncbi.nlm.nih.gov/geo/geo2r/</a>)<sup>[<xref ref-type="bibr" rid="b14">14</xref>]</sup>在线工具分析胃癌样本与正常样本基因数据。将胃癌组织芯片GSE79973矩阵数据的探针名转化为基因名,对原始数据进行去重等处理后,以|logFC|>2且<italic>P</italic> < 0.01的标准筛选出DEGs,用SangerBox软件绘制火山图。

1.3. DEGs的富集分析

为深入了解这些DEGs,我们用DAVID(the Database for Annotation, Visualization and Integrated Discovery,<a href="http://david.abcc.ncifcrf.gov/">http://david.abcc.ncifcrf.gov/</a>)在线分析数据库<sup>[<xref ref-type="bibr" rid="b15">15</xref>]</sup>对DEGs进行GO和KEGG通路富集分析<sup>[<xref ref-type="bibr" rid="b16">16</xref>-<xref ref-type="bibr" rid="b17">17</xref>]</sup>,以<italic>P</italic> < 0.05为差异有统计学意义。

1.4. PPI网络构建和关键基因筛选

通过在线分析网站STRING(Search Tool for the Rtrieval of Interacting Genes, <a href="https://string-db.org/">https://string-db.org/</a>)<sup>[<xref ref-type="bibr" rid="b18">18</xref>]</sup>得到DEGs的蛋白互作网络,以TSV格式导出,将所得源文件导入Cytoscape<sup>[<xref ref-type="bibr" rid="b19">19</xref>]</sup>进行可视化分析,用插件cytoHubba进行Hub基因分析,选用MCC算法,选取前10个Hub基因。

1.5. PPI功能模块分析

为进一步明确胃癌可能的信号通路,我们在进行PPI网络构建后,用Cytoscape软件中MCODE插件对PPI网络进行聚类分析后得到PPI功能模块,然后用DAVID数据库将功能模块中的基因进行KEGG pathway分析。

1.6. 关键基因验证分析

为进一步验证Hub基因,我们利用GEPIA(Gene Expression Profiling Interactive Analysis, <a href="http://gepia.cancer-pku.cn">http://gepia.cancer-pku.cn</a>)数据库<sup>[<xref ref-type="bibr" rid="b20">20</xref>]</sup>分析Hub基因在胃癌组织和正常组织中的表达水平,并绘制Hub基因的KaplanMeiter生存曲线。

1.7. COL1A1和microRNAs关系预测

为了解COL1A1参与胃癌的发生发展机制,我们通过在线数据库Target Scan 7.2(<a href="http://www.targetscan.org/">http://www.targetscan.org/</a>)预测与COL1A1相互作用的microRNAs。

1.8. microRNAs在胃癌组织的表达及其与生存预后的关系

基于OncomiR数据库(<a href="http://www.oncomir.org">http://www.oncomir.org</a>)分析miRNA在胃癌组织和正常组织中的表达,并对其进行预后分析。

2. 结果

2.1. 胃癌和正常组织的DEGs

通过对基因芯片GSE79973进行数据分析,结果显示有181个DEGs(胃癌组/正常对照组),其中上调基因和下调基因分别为57个和124个(图 1)。

1.

1

差异表达基因火山图

Volcano plot of the differential expressed genes in gastric cancer

2.2. GO和KEGG通路富集分析

GO可分为生物过程(biological process, BP)、细胞组成(cellular component, CC)和分子功能(molecular function, MF)。采用DAVID对181个DEGs进行GO和KEGG通路富集分析,结果如表 1所示。DEGs主要涉及细胞黏附、细胞外基质组织、氧化还原过程、胶原蛋白分解代谢、异物的代谢等生物过程,细胞学组成分析显示这些基因大多参与细胞外泌体、细胞外基质、细胞外区等的组成。分子功能的变化主要集中在锌、铁离子结合、相同的蛋白结合、细胞外基质结构组成、肝素结合、氧化还原酶活性、血红素结合、氧气结合等。KEGG通路富集分析表明,差异基因主要涉及PI3K-Akt信号通路、ECM-受体相互作用、蛋白质消化吸收、化学致癌作用、视黄醇的新陈代谢、细胞色素P450代谢通路、矿物质的吸收、胃酸分泌等。

1.

胃癌相关差异表达基因的GO和KEGG通路富集分析

Enrichment analysis of GO and KEGG pathway of the differentially expressed genes in gastric cancer

Category ID Term Count P
BP GO:0007155 Cell adhesion 20 1.00E-08
BP GO:0030198 Extracellular matrix organization 17 8.62E-12
BP GO:0055114 Oxidation-reduction process 13 0.004225507
BP GO:0030574 Collagen catabolic process 10 2.99E-09
BP GO:0006805 Xenobiotic metabolic process 8 4.07E-06
BP GO:0001501 Skeletal system development 8 1.57E-04
BP GO:0030199 Collagen fibril organization 7 8.18E-07
BP GO:0008202 Steroid metabolic process 7 1.49E-06
BP GO:0007586 Digestion 7 1.44E-05
BP GO:0034220 Ion transmembrane transport 7 0.008647397
BP GO:0001525 Angiogenesis 7 0.011406595
BP GO:0051216 Cartilage development 6 1.35E-04
CC GO:0005576 Extracellular region 45 3.21E-13
CC GO:0005615 Extracellular space 37 1.51E-10
CC GO:0070062 Extracellular exosome 32 0.047465783
CC GO:0005887 Integral component of plasma membrane 19 0.040478934
CC GO:0005578 Proteinaceous extracellular matrix 15 4.36E-08
CC GO:0031012 Extracellular matrix 14 9.79E-07
CC GO:0005788 Endoplasmic reticulum lumen 13 5.98E-08
CC GO:0005581 Collagen trimer 11 4.00E-09
CC GO:0016324 Apical plasma membrane 11 1.43E-04
CC GO:0009986 Cell surface 11 0.013726185
CC GO:0005604 Basement membrane 6 4.73E-04
CC GO:0031090 Organelle membrane 6 7.37E-04
MF GO:0008270 Zinc ion binding 16 0.049404886
MF GO:0042802 Identical protein binding 13 0.017914572
MF GO:0005201 Extracellular matrix structural constituent 9 6.31E-08
MF GO:0008201 Heparin binding 8 3.16E-04
MF GO:0016491 Oxidoreductase activity 8 0.001188406
MF GO:0020037 Heme binding 7 8.49E-04
MF GO:0019825 Oxygen binding 6 3.69E-05
MF GO:0016705 Oxidoreductase activity, acting on paired donors, with Incorporation or reduction of molecular oxygen 6 9.43E-05
MF GO:0004497 Monooxygenase activity 6 1.03E-04
MF GO:0005178 Integrin binding 6 0.001598275
MF GO:0005506 Iron ion binding 6 0.007975728
MF GO:0008392 Arachidonic acid epoxygenase activity 5 5.14E-06
KEGG pathway hsa04151 PI3K-Akt signaling pathway 12 0.001311204
KEGG pathway hsa04512 ECM-receptor interaction 10 3.88E-07
KEGG pathway hsa04974 Protein digestion and absorption 10 4.29E-07
KEGG pathway hsa04510 Focal adhesion 10 4.07E-04
KEGG pathway hsa05204 Chemical carcinogenesis 9 2.34E-06
KEGG pathway hsa00830 Retinol metabolism 7 6.72E-05
KEGG pathway hsa00982 Drug metabolism-cytochrome P450 7 9.47E-05
KEGG pathway hsa00980 Metabolism of xenobiotics by cytochrome P450 7 1.52E-04
KEGG pathway hsa04978 Mineral absorption 6 1.12E-04
KEGG pathway hsa04971 Gastric acid secretion 6 0.001205941
KEGG pathway hsa05146 Amoebiasis 6 0.006138929
KEGG pathway hsa00010 Glycolysis/Gluconeogenesis 4 0.03749949

2.3. 差异表达基因的PPI网络分析

将181个显著差异基因输入STRING数据库中,然后将所得数据导入Cytoscape中,利用插件cytoHubba找出前10个Hub基因,分别为COL1A1、COL1A2、COL4A1、COL2A1、SERPINH1、COL6A3、COL11A1、COL10A1、COL12A1、COL8A1(图 2)。

2.

2

差异基因编码蛋白质的PPI分析图和关键基因筛选

PPI analysis of the proteins encoded by the differential genes and screening of the key genes. A: PPI network for the DEGs; B: Amplification of the network for PPI associated with COL1A1.

2.4. PPI功能模块分析

我们用Cytoscape软件中MCODE插件对PPI网络进行聚类分析后得到不同的PPI功能模块,Score得分最高的模块如图 3所示。然后我们通过DAVID在线分析工具对模块中包含的基因进行KEGG pathway分析,主要涉及蛋白质消化吸收、PI3K-Akt信号通路、ECM-受体相互作用、血小板激活信号通路(表 2)。

3.

3

功能模块图

Functional module diagram

2.

功能模块内基因的KEGG Pathway分析

KEGG pathway analysis of the genes in the functional modules

Category ID Term Count P
KEGG pathway hsa04974 Protein digestion and absorption 7 3.69E-12
KEGG pathway hsa04512 ECM-receptor interaction 5 3.51E-07
KEGG pathway hsa05146 Amoebiasis 5 7.80E-07
KEGG pathway hsa04510 Focal adhesion 5 1.12E-05
KEGG pathway hsa04151 PI3K-Akt signaling pathway 5 8.61E-05
KEGG pathway hsa04611 Platelet activation 4 1.27E-04

2.5. 关键基因验证

用GEPIA数据库进一步验证分析了10个Hub基因在胃癌组织(408例)和正常组织(211例)的表达水平中的表达情况,发现除了COL2A1在胃癌组织中低表达外,其他9个Hub基因均在胃癌组织中高表达,差异有统计学意义(P < 0.05,图 4)。最后我们用GEPIA数据库绘制了Hub基因高表达胃癌组织和低表达胃癌组织的Kaplan-Meiter生存曲线,结果显示COL1A1、COL4A1、COL12A1高表达的胃癌组织的生存率低于低表达组织,差异具有统计学意义(P < 0.05),与患者不良预后密切相关(图 5)。COL1A1的高表达与不良预后的相关性更加显著。

4.

4

胃癌关键基因在肿瘤组织及正常组织中的表达水平

Expression levels of the key genes in gastric cancer and normal tissues. A: COL1A1 expression level; B: COL4A1expression level; C: COL12A1expression level. *P < 0.05 vs normal tissue. The X axis represents tissue type, T the tumor, and N the normal tissue. The Y axis represents log2(TPM +1). TPM: Number of transcripts per million reads.

5.

5

关键基因对胃癌患者生存分析的验证结果

Validation of the key genes in survival analysis of the patients with gastric cancer. A: COL1A1 validation result; B: COL4A1 validation result; C: COL12A1validation result. The red line represents the high expression group, and the blue line represents the low expression group. HR: Risk ratio.

6.

6

COL1A1 mRNA 3'UTR中miR-129-5p结合位点的预测结果

Prediction of miR-129-5p binding sites in COL1A1 mRNA3'UTR.

2.6. COL1A1与miRNA相互作用预测结果

用Target Scan数据库预测到miR-129-5p直接与COL1A1 mRNA的3'UTR结合,是COL1A1转录后调节因子(图 5)。

2.7. miR-129-5p在胃癌中的表达水平与生存预后分析

经OncomiR数据库检索发现,miR-129-5p在胃癌组织中的表达显著低于正常组织(P=3.32e-05,图 7A)。为分析miR-129-5p与胃癌生存预后之间的关系,我们使用此数据库进一步分析了miR-129-5p在胃癌组织中的表达水平与生存期的关系,结果发现,低表达组生存期时间短于正常组织,但差异不具有统计学意义(P=0.1182,图 7B)。

7.

7

miR-129-5p在胃癌中的表达与其生存预后分析

Expression of miR-129-5p in gastric cancer and analysis of the survival outcomes of the patients. A: Expression of miR-129-5p in gastric cancer (**P < 0.05 vs normal); B: Relationship between miR-129-5p expression level and the survival outcomes.

3. 讨论

胃癌早期诊断具有一定难度,大多数胃癌患者确诊时已是晚期[21],已失去最佳治疗时机,死亡率一直居高不下。因此,探究新的早期肿瘤生物标志物对胃癌的防治具有一定价值。本研究采用生物信息学方法对GEO数据库中的胃腺癌组织和正常胃黏膜组织的基因芯片数据进行分析。首先比较胃癌组织和正常胃黏膜组织中的基因表达情况,共筛选出181个DEGs(胃癌组/正常对照组),其中上调基因和下调基因分别为57个和124个。为进一步了解DEGs,我们进行了GO和KEGG通路富集分析,DEGs的生物过程主要涉及细胞黏附、氧化还原过程、胶原蛋白分解代谢等,细胞学组成分析显示这些基因大多参与细胞外泌体、细胞外基质、细胞外区等的组成。分子功能的变化主要集中在锌、铁离子结合、相同的蛋白结合、细胞外基质结构组成、肝素结合、氧化还原酶活性、血红素结合、氧气结合等。正常情况下,机体的氧化还原过程处于动态平衡状态,而细胞氧化还原环境持续遭到破坏,则可能导致肿瘤的发生[22]。功能模块分析显示:KEGG通路主要涉及蛋白质消化吸收、PI3K-Akt信号通路、ECM-受体相互作用、血小板激活信号通路。这与一项胃癌关键基因的生物信息学分析的研究结果相似[23]。PI3K-Akt通路在许多肿瘤中都具有较高的易感性[24]。PI3K-Akt通路通过促进细胞增殖,在肿瘤细胞侵袭、转移中起着重要的作用[25]

PPI网络筛选出10个Hub基因,由GEPIA验证得知COL1A1(Collagen, type Ⅰ, alpha 1)的高表达与不良预后显著相关,有研究已证实此结果[26]。最近有研究[27]提出了COL1A1可作为胃癌早期筛查的标志。Ⅰ型胶原是纤维胶原家族的主要成分,主要参与细胞外基质结构的组成,被认为是一种肿瘤相关基因[28],可能参与了肿瘤的侵袭和进展[29],有研究表明[30],COL1A1的上调有助于卵巢癌细胞对顺铂耐药。为进一步了解COL1A1参与胃癌发生发展的分子机制,我们预测了调控COL1A1的转录后调节因子miRNAs,miRNA是内源性小型非编码RNA分子,其长度为18-24个核苷酸,可通过诱导mRNA降解或通过与mRNA的3'-UTR的互补结合而抑制mRNA[31]。预测结果显示miR-129-5p可直接与COL1A1 mRNA的3'UTR结合。miR-129-5p是一种有效的肿瘤抑制因子[32-33],为验证胃癌中miR- 129-5p与COL1A1的关系,我们通过OncomiR数据库检索了miR-129-5p在胃癌中的表达与生存预后,结果显示miR-129-5p在胃癌组织中的表达显著低于正常组织(P=3.32e-05),生存期也短于正常组织。由此得出miR-129-5p调控的COL1A1是胃癌潜在的治疗靶点。这与最近的一项miR-129-5p通过抑制COL1A1来抑制胃癌细胞的侵袭和增殖[34]的研究结果一致。

综上所述,我们通过生物信息学分析确定了差异表达的基因,由富集分析和蛋白互作可知,COL1A1在胃癌中是一种高表达分子。此外,在胃癌中预测到miR- 129-5p可下调COL1A1的表达。COL1A1应该是miR- 129-5p调控胃癌治疗的靶点。为了得到更准确的相关性结果,还需要进行一系列的实验来验证预测结果。

Biography

杨万霞,在读硕士研究生,E-mail: lnyangwx@163.com

Funding Statement

甘肃省重点研发计划项目(18YF1FA108);兰大二院萃英计划面上项目(CY2018-MS10);兰大二院萃英计划临床拔尖技术项目(CY2018-BJ04)

Contributor Information

杨 万霞 (Wanxia YANG), Email: lnyangwx@163.com.

尤 崇革 (Chongge YOU), Email: youchg@lzu.edu.cn.

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