[1]尹　阳,孙巨军,李　越,等.基于TCGA数据库筛选微小RNA（miRNA）用于原发性乳腺癌早期诊断的生物信息学分析[J].现代检验医学杂志,2021,36(05):33-37.[doi:10.3969/j.issn.1671-7414.2021.05.007]
　YIN Yang,SUN Ju-jun,LI Yue,et al.Bioinformatics Analysis of Screening miRNA for Early Diagnosis ofPrimary Breast Cancer Based on TCGA Database[J].Journal of Modern Laboratory Medicine,2021,36(05):33-37.[doi:10.3969/j.issn.1671-7414.2021.05.007]
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基于TCGA数据库筛选微小RNA（miRNA）用于原发性乳腺癌早期诊断的生物信息学分析()
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《现代检验医学杂志》[ISSN:/CN:]

卷:
第36卷
期数:
2021年05期
页码:
33-37
栏目:
论　著
出版日期:
2021-10-14

文章信息/Info

Title:
Bioinformatics Analysis of Screening miRNA for Early Diagnosis ofPrimary Breast Cancer Based on TCGA Database
文章编号:
1671-7414（2021）05-033-05
作者:
尹　阳; 孙巨军; 李　越; 等
（1. 西安交通大学第二附属医院检验科，西安 710004；2. 西电集团医院检验科，西安 710077）
Author(s):
YIN Yang1; 2;  SUN Ju-jun2;  LI Yue1;  LI Xin2;  HE Qian1
（1.Department of Clinical Laboratory, the Second Affiliated Hospital of Xi’an Jiaotong University, Xi’an 710004,China；2.Department of Clinical Laboratory, Xi’an XD Group Hospital, Xi’an 710077, China）
关键词:
原发性乳腺癌; 微小RNA（miRNA）; 生物信息学分析
分类号:
R737.9；R730.43
DOI:
10.3969/j.issn.1671-7414.2021.05.007
文献标志码:
A
摘要:
目的　基于肿瘤基因图谱（the cancer genome atlas，TCGA）数据库筛选微小RNA（miRNA）用于原发性乳腺癌的早期诊断。方法　从TCGA上下载原发性乳腺癌miRNA表达数据，将癌症组与正常组比较获得差异表达miRNA。用miRwalk2.0软件分析差异miRNA的靶基因。在c-Bioportal数据库中筛选出原发性乳腺癌突变发生率大于5%的突变基因。分析差异miRNA作用的靶基因与乳腺癌高频突变基因之间的关系，得到备选miRNA，将备选miRNA与乳腺癌前20 名差异表达的miRNA求交集，得到目标miRNA，将目标miRNA做受试者工作曲线（ROC曲线）分析。结果　TCGA数据包含原发性乳腺癌组织1 075例，正常对照乳腺组织95例，共有1 870条miRNA的表达数据。共得到差异表达显著miRNA 129个（P＜0.05），其中乳腺癌组织中表达升高至3倍以上的miRNA 90个，下调至1/3的miRNA 39个，预测到相对应18 413个靶基因，筛选出原发性乳腺癌突变基因12个。18 413个靶基因中包含12个高频基因，此12个基因是差异miRNA的靶基因同时也是高频基因，故将此12个基因对应的63个miRNA作为备选miRNA。将备选miRNA与乳腺癌前20 名差异表达的miRNA求交集得到目标miRNA 6个：hsa-mir-4732，hsa-miR-486，hsa-miR-592，hsa-miR-449b，hsa-miR-187和hsa-miR-196a，将这 6个miRNA构建ROC曲线（P＜0.05），预测其作为肿瘤标志物的诊断能力。结论　基于TCGA数据库的生物信息学方法可简便而可靠地筛选目标miRNA进行后续研究，有较高的参考价值。
Abstract:
Objective　To screen microRNA (miRNA) for the early diagnosis of primary breast cancer based on the cancergenome atlas (TCGA) database. Methods　The data of miRNA expression in primary breast cancer were downloaded fromTCGA. The tumor samples and normal samples were compared to screen the differential miRNA. Target genes of the differentialmiRNA were analyzed based on miRWalk 2.0, and mutation genes with mutation rate of more than 5% related to primary breastcancer were acquired from c-Bioportal database. Analyzed the relationship between the target genes and high mutation genes forobtaining candidate miRNA. The miRNA were screened by intersecting the candidate miRNA and the top 20 differentiallyexpressed miRNAs. The screened miRNA were evaluated by ROC curves. Results　The TCGA data included 1 075 cases ofprimary breast cancer tissues and 95 cases of normal breast tissues with 1 870 miRNA detected. A total of 129 miRNAs withsignificant differential expression were obtained（P<0.05）. 90 miRNAs increased to more than 3 times in breast cancer tissues,and 39 miRNAs decreased to more than 1/3. 18 413 target genes related to the differential miRNA were predicted. 12 mutationgenes of primary breast cancer were screened, which were included in the 18 413 target genes. 63 candidate miRNAs whosetarget genes contained breast cancer related mutation gene were selected. 6 miRNAs (hsa-miR-4732, hsa-miR-486, hsa-miR-592,hsa-miR-449b, hsa-miR-187 and hsa-miR-196a) were screened by intersecting the candidate miRNAs and the top 20differentially expressed miRNAs in breast cancer. The ROC curve of these six miRNAs was evaluated (P< 0.05) to predict theirdiagnostic ability as tumor markers. Conclusion　The bioinformatics method based on the TCGA database can screen miRNA simply and reliably for follow-up research, which has high reference value.

参考文献/References:

[1] SIEGEL R L, MILLER K D, JEMAL A. Cancerstatistics, 2018[J]. CA-A Cancer Journal for Clinicians,2018, 68(1): 7-30.
[2] CEDOLINI C, BERTOZZI S, LONDERO A P, etal. Type of breast cancer diagnosis, screening, andsurvival[J]. Clinical Breast Cancer, 2014, 14(4): 235-240.
[3] KROL J, LOEDIGE I, FILIPOWICZ W. Thewidespread regulation of microRNA biogenesis,function and decay[J]. Nature Reviews Genetics, 2010,11(9): 597-610.
[4] HAYES J, PERUZZI P P, LAWLER S. MicroRNAs incancer: biomarkers, functions and therapy[J]. Trends inMolecular Medicine, 2014, 20(8): 460-469.
[5] HAMAM R, HAMAM D, ALSALEH K A, et al.Circulating microRNAs in breast cancer: noveldiagnostic and prognostic biomarkers[J]. Cell Death &Disease, 2017, 8(9): e3045.
[6] WANG Zhining ,JENSEN M A,ZENKLUSEN J C. Apractical guide to the Cancer Genome Atlas (TCGA) [J].Methods Mol Biol,2016,1418:111-141.
[7] DWEEP H, STICHT C, PANDEY P, et al. miRWalk--database: prediction of possible miRNA binding sitesby “walking” the genes of three genomes[J]. Journalof Biomedical Informatics, 2011, 44(5): 839-847.
[8] XIN Hua,LI Xiaoli,YANG Bin, et al. Blood-basedmultiple-microRNA assay displays a better diagnosticperformance than single-microRNA assay in thediagnosis of breast tumor[J]. Tumour Biology, 2014,35(12): 12635-12643.
[9] BERTOLI G, CAVA C, CASTIGLIONI I. MicroRNAs:new biomarkers for diagnosis, prognosis, therapyprediction and therapeutic tools for breast cancer[J].Theranostics, 2015, 5(10): 1122-1143.
[10] 洪宏，袁建芬，喻海忠．血清miR-765 和CA153联合检测对乳腺癌的诊断价值[J]．现代检验医学杂志,2018,33(3):92-94.HONG Hong, YUAN Jianfen, YU Haizhong.Diagnostic value of combined detection of serum miR-765 and CA153 in breast cancer [J]. Journal of ModernLaboratory Medicine,2018,33(3):92-94.
[11] SOCHOR M, BASOVA P, PESTA M, et al. OncogenicmicroRNAs: miR-155, miR-19a, miR-181b, and miR-24 enable monitoring of early breast cancer in serum[J].BMC Cancer, 2014, 14: 448.

备注/Memo

备注/Memo:
作者简介：尹阳 ( 1988-)，女，硕士研究生，主管检验技师，主要从事临床生化、免疫学研究，E-mail：yinyang910@163.com。
通讯作者：何谦（1971-），女，博士，主任技师，主要从事生化和分子生物学研究， E-mail：qianh0511@163.com。
更新日期/Last Update: 1900-01-01