三阴性乳腺癌相关miRNA筛选及其靶基因的生物信息学分析

doi:10.19586/j.2095-2341.2021.0178

生物技术进展 ›› 2022, Vol. 12 ›› Issue (2): 296-304.DOI: 10.19586/j.2095-2341.2021.0178

三阴性乳腺癌相关miRNA筛选及其靶基因的生物信息学分析

胡鑫(), 麻慧慈(), 韩明盛, 原晓红, 杨鸣宇, 马艳琴()

山西农业大学生命科学学院，山西晋中 030801

收稿日期:2021-10-29 接受日期:2021-11-12 出版日期:2022-03-25 发布日期:2022-03-25
通讯作者: 马艳琴
作者简介:胡鑫与麻慧慈为本文共同第一作者。胡鑫 E-mail：huxin15332382535@126.com
麻慧慈 E-mail：mahuici0820@163.com
基金资助:
山西省自然科学基金项目(201901D111232);山西省研究生教育创新项目(2020SY197)

Screening of Triple⁃negative Breast Cancer⁃associated miRNAs and Bioinfor⁃matics Analysis of the Target Genes

Xin HU(), Huici MA(), Mingsheng HAN, Xiaohong YUAN, Mingyu YANG, Yanqin MA()

College of Life Science，Shanxi Agricultural University，Shanxi Jinzhong 030801，China

Received:2021-10-29 Accepted:2021-11-12 Online:2022-03-25 Published:2022-03-25
Contact: Yanqin MA

摘要/Abstract

摘要：

应用生物信息学方法筛选并分析三阴性乳腺癌（triple-negative breast cancer，TNBC）相关miRNA及其靶基因，为TNBC的研究提供潜在的分子靶点。采用GEO2R分析TNBC相关miRNA芯片数据集，筛选差异表达倍数最大的5个上调和5个下调miRNA。miRWalk、TargetScan和miRDB预测靶基因并进行Veen分析取交集。利用DAVID对靶基因进行GO富集分析和KEGG通路分析。利用STRING数据库构建蛋白互作网络，并结合Cytoscape构建miRNA-靶基因调控网络，从而筛选出关键的miRNA及其关键靶基因。利用GEPIA2数据库对靶基因进行生存分析。GEO2R筛选出486个差异miRNA，上调和下调的miRNA分别有298个和188个。对差异倍数最大的5个上调和5个下调miRNA的靶基因进行富集分析显示，靶基因主要参与ErbB信号通路、癌症中转录调控紊乱和cGMP-PKG信号通路等。miRNA-靶基因调控网络显示，表达上调的关键miRNA为miR-611，其关键靶基因为CDC27、UBE2D2、UBR1、SPSB1、HERC2和RLIM；表达下调的关键miRNA为miR-1205，其关键靶基因为WSB1、FBXL8、UBE2W、PTPN11、ARF6、DNAJC6和COPS2。生存分析表明，UBR1（P=0.007 2）和PTPN11（P=0.029）表达上调可显著降低TNBC患者的整体生存率。经筛选获得的关键miRNA及其关键靶基因可作为潜在分子标记物用于TNBC的早期诊断、治疗靶点选择和预后判断，并为后续的研究提供参考依据。

关键词: 三阴性乳腺癌, GEO数据库, 微小RNA, 靶基因

Abstract:

Bioinformatics methods were used to screen and analyze triple-negative breast cancer （TNBC） associated miRNAs and their target genes which could serve as potential molecular targets in the study of TNBC. GEO2R was used to analyze the miRNA chip datasets and screen the five upregulated and five downregulated miRNAs with the highest fold changes. miRWalk， TargetScan and miRDB predicted target genes and performed Veen analysis to obtain the overlap. We used DAVID to perform GO enrichment analysis and KEGG pathway analysis of target genes. The STRING database was used to construct a protein-protein interaction network， and combined with Cytoscape to construct a miRNA-target gene regulatory network， so as to screen out the key miRNAs and their key target genes. The survival of target genes was analyzed by GEPIA2 database. 486 differentially expressed miRNAs were screened by GEO2R， among them the upregulated and downregulated miRNAs were 298 and 188 respectively. Enrichment analysis of the five up-regulated and five down-regulated miRNA target genes with the largest fold difference showed that the target genes were mainly involved in the ErbB signaling pathway， the transcriptional regulation disorder in cancer， and the cGMP-PKG signaling pathway and so on. miRNA-target gene regulatory network showed that the key upregulated miRNA was miR-611 and its key target genes were CDC27， UBE2D2， UBR1， SPSB1， HERC2 and RLIM. The key downregulated miRNA was miR-1205 and its key target genes were WSB1， FBXL8， UBE2W， PTPN11， ARF6， DNAJC6 and COPS2. Survival analysis showed that the upregulated expression of UBR1 （P=0.007 2） and PTPN11 （P=0.029） could significantly decrease the overall survival rate of TNBC patients. The key miRNAs and their target genes obtained in the study can be used as potential candidate targets for early diagnosis， precising therapy and prognosis judgment of TNBC， and can provide guidance for further research.

Key words: triple-negative breast cancer, GEO database, miRNA, target gene

中图分类号:

R737.9

胡鑫, 麻慧慈, 韩明盛, 原晓红, 杨鸣宇, 马艳琴. 三阴性乳腺癌相关miRNA筛选及其靶基因的生物信息学分析[J]. 生物技术进展, 2022, 12(2): 296-304.

Xin HU, Huici MA, Mingsheng HAN, Xiaohong YUAN, Mingyu YANG, Yanqin MA. Screening of Triple⁃negative Breast Cancer⁃associated miRNAs and Bioinfor⁃matics Analysis of the Target Genes[J]. Current Biotechnology, 2022, 12(2): 296-304.

图/表 11

参考文献 19

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miRNA名称		Log₂FC	adj.P值	P值
上调差异表达miRNA	miR⁃1915^*	6.489 288 2	2.11E⁃02	1.61E⁃03
	miR⁃615⁃5p	7.296 028 02	2.65E⁃02	2.14E⁃03
	miR⁃587	7.453 946 75	6.91E⁃03	3.98E⁃04
	miR⁃611	7.969 428 52	1.60E⁃02	1.13E⁃03
	miR⁃452^*	8.587 340 55	1.27E⁃02	8.57E⁃04
下调差异表达miRNA	miR⁃339⁃5p	-8.996 512 23	6.64E⁃03	3.81E⁃04
	miR⁃511	-7.895 743 21	1.97E⁃02	1.48E⁃03
	miR⁃1205	-7.866 457 6	4.80E⁃03	2.57E⁃04
	miR⁃147b	-7.776 133 92	3.42E⁃02	3.04E⁃03
	miR⁃580	-7.360 729 56	2.27E⁃02	1.76E⁃03

miRNA名称		Log₂FC	adj.P值	P值
上调差异表达miRNA	miR⁃1915^*	6.489 288 2	2.11E⁃02	1.61E⁃03
	miR⁃615⁃5p	7.296 028 02	2.65E⁃02	2.14E⁃03
	miR⁃587	7.453 946 75	6.91E⁃03	3.98E⁃04
	miR⁃611	7.969 428 52	1.60E⁃02	1.13E⁃03
	miR⁃452^*	8.587 340 55	1.27E⁃02	8.57E⁃04
下调差异表达miRNA	miR⁃339⁃5p	-8.996 512 23	6.64E⁃03	3.81E⁃04
	miR⁃511	-7.895 743 21	1.97E⁃02	1.48E⁃03
	miR⁃1205	-7.866 457 6	4.80E⁃03	2.57E⁃04
	miR⁃147b	-7.776 133 92	3.42E⁃02	3.04E⁃03
	miR⁃580	-7.360 729 56	2.27E⁃02	1.76E⁃03

GO编号		描述	数目	P值
生物学过程	GO:0006351	转录，DNA模板化	99	1.45E⁃05
	GO:0006355	转录调控，DNA模板化	77	1.20E⁃04
	GO:0007411	轴突导向	16	2.59E⁃04
	GO:0007050	细胞周期停滞	14	8.12E⁃04
	GO:0045944	RNA聚合酶Ⅱ启动子转录的正调控	49	4.17E⁃03
	GO:0007296	卵黄形成	3	6.26E⁃03
细胞成分	GO:0005634	细胞核	211	3.30E⁃04
	GO:0005737	细胞质	190	1.97E⁃03
	GO:0005829	细胞溶质	131	2.02E⁃03
	GO:0005622	胞内	57	3.24E⁃03
	GO:0009986	细胞表面	26	4.85E⁃03
	GO:0030054	细胞连接	25	7.19E⁃03
分子功能	GO:0003700	转录因子活性，序列特异性DNA结合	58	7.19E⁃06
	GO:0003677	DNA结合	83	9.04E⁃05
	GO:0000978	RNA 聚合酶Ⅱ核心启动子近端区域序列特异性 DNA 结合	26	2.48E⁃04
	GO:0001077	转录激活因子活性，RNA聚合酶Ⅱ核心启动子近端区域序列特异性结合	19	7.13E⁃04
	GO:0031624	泛素结合酶结合	6	2.97E⁃03

GO编号		描述	数目	P值
生物学过程	GO:0006351	转录，DNA模板化	99	1.45E⁃05
	GO:0006355	转录调控，DNA模板化	77	1.20E⁃04
	GO:0007411	轴突导向	16	2.59E⁃04
	GO:0007050	细胞周期停滞	14	8.12E⁃04
	GO:0045944	RNA聚合酶Ⅱ启动子转录的正调控	49	4.17E⁃03
	GO:0007296	卵黄形成	3	6.26E⁃03
细胞成分	GO:0005634	细胞核	211	3.30E⁃04
	GO:0005737	细胞质	190	1.97E⁃03
	GO:0005829	细胞溶质	131	2.02E⁃03
	GO:0005622	胞内	57	3.24E⁃03
	GO:0009986	细胞表面	26	4.85E⁃03
	GO:0030054	细胞连接	25	7.19E⁃03
分子功能	GO:0003700	转录因子活性，序列特异性DNA结合	58	7.19E⁃06
	GO:0003677	DNA结合	83	9.04E⁃05
	GO:0000978	RNA 聚合酶Ⅱ核心启动子近端区域序列特异性 DNA 结合	26	2.48E⁃04
	GO:0001077	转录激活因子活性，RNA聚合酶Ⅱ核心启动子近端区域序列特异性结合	19	7.13E⁃04
	GO:0031624	泛素结合酶结合	6	2.97E⁃03

GO编号		描述	数目	P值
生物学过程	GO:0051966	突触传递的调节，谷氨酸能	4	3.08E⁃03
	GO:2000271	成纤维细胞凋亡过程的正调控	3	3.95E⁃03
	GO:0051085	伴侣蛋白介导的蛋白质折叠辅因子	3	7.22E⁃03
	GO:0071560	细胞对转化生长因子β刺激的反应	4	1.09E⁃02
	GO:0045893	转录的正调控，DNA 模板化	10	1.39E⁃02
	GO:0048025	通过剪接体负调控 mRNA剪接	3	2.20E⁃02
细胞成分	GO:0005737	细胞质	54	1.37E⁃02
	GO:0005770	晚期胞内体	5	2.79E⁃02
	GO:0005829	细胞溶质	22	4.10E⁃02
	GO:0014069	突触后密集区	4	4.24E⁃02
分子功能	GO:0003723	RNA结合	10	6.41E⁃03

三阴性乳腺癌相关miRNA筛选及其靶基因的生物信息学分析

Screening of Triple⁃negative Breast Cancer⁃associated miRNAs and Bioinfor⁃matics Analysis of the Target Genes

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Pathway编号		描述	数目	P值
上调差异miRNA靶基因	hsa04012	ErbB信号通路	10	8.63E⁃04
	hsa05202	癌症中的转录失调	13	3.19E⁃03
	hsa04917	催乳素信号通路	8	4.24E⁃03
	hsa05220	慢性粒细胞白血病	8	4.58E⁃03
	hsa05215	前列腺癌	8	1.34E⁃02
下调差异miRNA靶基因	cfa04022	cGMP⁃PKG信号通路	8	1.39E⁃02