[1]李　超,朱晓丹,张玲华,等.基于GEO数据库整合miRNA-mRNA表达谱筛选卵巢癌的关键基因分子及生物信息分析[J].现代检验医学杂志,2021,36(05):38-42.[doi:10.3969/j.issn.1671-7414.2021.05.008]
　LI Chao,ZHU Xiao-dan,ZHANG Ling-hua,et al.Screening Key Biomarkers and Bioinformatic Analysis of Ovarian Cancer byIntegrated miRNA-mRNA Expression Profiles Based on GEO Database[J].Journal of Modern Laboratory Medicine,2021,36(05):38-42.[doi:10.3969/j.issn.1671-7414.2021.05.008]
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基于GEO数据库整合miRNA-mRNA表达谱筛选卵巢癌的关键基因分子及生物信息分析()
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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:
38-42
栏目:
论　著
出版日期:
2021-10-14

文章信息/Info

Title:
Screening Key Biomarkers and Bioinformatic Analysis of Ovarian Cancer byIntegrated miRNA-mRNA Expression Profiles Based on GEO Database
文章编号:
1671-7414（2021）05-038-05
作者:
李　超; 朱晓丹; 张玲华; 等
（佛山市妇幼保健院，广东佛山 528000）
Author(s):
LI Chao;  ZHU Xiao-dan;  ZHANG Ling-hua;  YANG Xing-kun
（Foshan Women and Children Hospital, Guangdong Foshan 528000, China）
关键词:
卵巢癌; 差异表达基因; 生物信息分析; 分子网络; GEO。
分类号:
R737.31；R730.43
DOI:
10.3969/j.issn.1671-7414.2021.05.008
文献标志码:
A
摘要:
目的　卵巢癌是妇产科恶性肿瘤死亡的主要原因之一，但其致病分子机制还未被清晰阐明。该研究通过整合生物信息学方法，旨在挖掘其潜在的关键基因分子及生物学功能，以便更全面地揭示其发病机制。方法　从GEO (Gene Expression Omnibus) 数据库下载miRNA和mRNA表达谱芯片集。通过R语言“limma”包筛选差异表达的miRNA和mRNA。通过FunRich软件对筛选的差异miRNA进行靶标预测，并与筛选的差异mRNA取交集得到共有差异基因。通过R语言“clusterProfiler”包对共有差异基因进行富集分析和通路注释以挖掘其生物学功能。 运用string数据库和cytoscape软件进一步构建miRNA-靶基因调控网络，并鉴定分子网络中的关键基因分子。结果　共筛选鉴定出167个共有差异基因。富集分析表明共有差异基因主要涉及细胞外组织、胚胎器官发育、突触后特化、胶原三聚体和DNA结合转录激活等生物过程；通路注释表明共有差异基因主要参与蛋白质的消化吸收和松弛素信号通路行为。分子网络分析筛选鉴定了10个候选关键基因，并发现miR-29c-3p，miR-1271-5p和 miR-133b抑癌分子与共有差异基因存在最广泛的靶向关系，处于调控中枢核心地位。上述关键基因分子在卵巢癌发生发展中扮演了重要角色。结论　该研究采用的系统整合方法学和鉴定的关键基因分子有助于揭示卵巢癌的潜在致病机制，也为卵巢癌的早期筛查提供新的候选标志物。
Abstract:
Objective　Ovarian cancer is one of the leading causes of death in gynecological malignancies, of which molecularmechanism hasn’t been elucidated clearly yet. The research aims to reveal the potential key molecular and biological processes ofovarian cancer by means of integrated bioinformatics, in order to more fully clarify its pathogenesis. Methods　The microarray setsof miRNA and mRNA expression profiles were downloaded from the GEO (Gene Expression Omnibus) database. The differentiallyexpressed miRNAs and mRNAs were screened by the “limma” package of R language. The target prediction was performed onthe differentially expressed miRNAs identified by the FunRich program and overlapped differentially expressed genes (DEGs) wereobtained combined with miRNA and mRNA datasets. The overlapped DEGs in the network were analyzed to explore the biologicalprocesses involved by enrichment and pathway analysis by the “clusterProfiler” package of the R language. The regulatory networkof miRNA-gene was further constructed by string database and cytoscape software, and the key molecular were identified in themolecular protein-protein interaction (PPI) network among DEGs. Results　A total of 167 overlapped DEGs were identified. Theenrichment showed that the overlapped DEGs were mainly involved in process named extracellular related organization, embryonicorgan development, postsynaptic specialization, collagen trimer and DNA?binding transcription activator. The pathway analysisshowed that these DEGs were involved in protein digestion and absorption and relaxin signaling pathway. The PPI molecular networkidentified 10 key genes, and found that miR-29c-3p, miR-1271-5p and miR-133b, existed the most extensive targeting relationshipwith overlapped DEGs, being three key miRNAs of the regulatory network, which played the role of tumor suppressor. These keymolecules may play an important role in the occurrence and development of ovarian cancer. Conclusion　The methodology usedand identification of key molecules in this study contributed to understanding the pathogenesis of ovarian cancer and providing newcandidate biomarkers for early screening of ovarian cancer.

参考文献/References:

[1] BRAY F, FERLAY J, SOERJOMATARAM I, et al.Global cancer statistics 2018: GLOBOCAN estimatesof incidence and mortality worldwide for 36 cancers in185 countries[J]. CA-A Cancer Journal for Clinicians,2018, 68(6): 394-424.
[2] NASRIN R, MOHAMMAD H. Current advancement ondiagnosis of ovarian cancer using biosensing of CA 125biomarker: Analytical approaches(Review)[J]. TrAC -Trends in Analytical Chemistry, 2018, 108:1-12.
[3] 马同敏，赵志强．血清 CA125, STIP1 和 IGF- Ⅰ 联合检测对卵巢癌的早期诊断价值研究[J]. 现代检验医学杂志,2018,33(6):50-54m,58.MA Tongmin ，ZHAO Zhiqiang. Clinical value ofcombined detection of serum CA125, STIP1 and IGF-Ilevels in early diagnosis of ovarian cancer [J]. Journalof Modern Laboratory Medicine, 2018,33(6):50-54，58.
[4] 付妮娜，范婧晖，钟慧．CA125,HE4 和MMP7 联合检测在Ⅰ型和Ⅱ型上皮性卵巢癌患者诊断中的价值[J]. 现代检验医学杂志,2019,34(6):109-112, 156.FU Nina, FAN Jinghui, ZHONG Hui. Diagnostic valueof combined detection of CA125, HE4 and MMP7 intype Ⅰ and Ⅱ epithelial ovarian cancer [J]. Journal ofModern Laboratory Medicine, 2019.34(6):109-112，156.
[5] 吕博文，钱钧，王杰，等．miR-455-5 p 在上皮性卵巢癌中表达研究及其靶基因功能分析[J]．实用肿瘤学杂志,2019,33(2):115-121.L? Bowen, QIAN Jun, WANG Jie, et al. Expression ofmiR-455-5p in epithelial ovarian cancer and its targetgene function [J]. Practical Oncology Journal, 2019,33(2): 115-121.
[6] 郭世民，李红梅. 利用生物信息学发现基因HADH与SRC 为卵巢癌潜在治疗靶基因[J]. 临床医药文献电子杂志,2017,4(97):19011-19012, 19028.GUO Shimin, LI Hongmei. Identification of HADH andSRC as prognostic biomarkers for ovarian cancer basedon bioinformatics approaches [J]. Journal of ClinicalMedical Literature（Electronic Edition）, 2017,4(97):19011-19012，19028.
[7] YANG X, ZHU S, LI L, et al. Identification ofdifferentially expressed genes and signaling pathwaysin ovarian cancer by integrated bioinformaticsanalysis(Article)[J]. Onco Targets and Therapy, 2018,11:1457-1474.
[8] FENG Hao, GU Zhongyi, LI Qin, et al. Identificationof significant genes with poor prognosis in ovariancancer via bioinformatical analysis(Article)[J]. Journalof Ovarian Research, 2019, 12(1):35.
[9] DALMAY T. Mechanism of miRNA-mediatedrepression of mRNA translation.[J]. Essays inBiochemistry, 2013, 54 : 29-38.
[10] LUJAMBIO A, LOWE S W. The microcosmos ofcancer[J]. Nature, 2012, 482(7385): 347-355.
[11] RUPAIMOOLE R, CALIN G A, LOPEZ-BERESTEING, et al. miRNA deregulation in cancer cells and thetumor microenvironment[J]. Cancer Discovery, 2016,6(3): 235-246.
[12] SMYTH G K. LIMMA: Linear Models for MicroarrayData [M]. GENTLEMAN R， CAREY V J，HUBERW, et al, Bioinformatics and Computational BiologySolutions Using R and Bioconductor，New York：Springer, 2005: 397-420.
[13] YU Guangchuang, WANG Ligen, HAN Yanyan, et al.clusterProfiler: an R package for comparing biologicalthemes among gene clusters[J]. OMICS-A Journal ofIntegrative Biology, 2012, 16(5): 284-287.
[14] PATHAN M, KEERTHIKUMAR S, CHISANGA D, etal. A novel community driven software for functionalenrichment analysis of extracellular vesicles data[J].Journal of Extracellular Vesicles, 2017, 6(1): 1321455.
[15] CHIN C H, CHEN S H, WU H H, et al. cytoHubba:identifying hub objects and sub-networks fromComplex interactome[J]. BMC Systems Biology, 2014,8(Suppl 4): S11.
[16] REDDY K B. MicroRNA (miRNA) in cancer[J].Cancer Cell International, 2015, 15(1): 38.
[17] TANG Wei, WAN Shixiang, YANG Zhen, et al. Tumororigin detection with tissue-specific miRNA and DNAmethylation markers[J]. Bioinformatics (Oxford,England), 2018, 34(3): 398-406.
[18] HU Zhenhua, CAI Mingbo，ZHANG Ying， etal.miR-29c-3p inhibits autophagy and cisplatinresistance in ovarian cancer by regulating FOXP1/ATG14 pathway[J]. Cell Cycle, 2020, 19(2):193-206.
[19] WANG Lin, ZHU Miaojun, REN Aimin, et al. A tenmicroRNAsignature identified from a genome-widemicroRNA expression profiling in human epithelialovarian cancer[J]. PLoS One, 2014, 9(5): e96472.
[20] DU H Y, LIU B. MiR-1271 as a tumor suppressorin breast cancer proliferation and progression viatargeting SPIN1[J]. European Review for Medical andPharmacological Sciences, 2018, 22(9): 2697-2706.
[21] LIU Xiaoqin, LI Guohong. MicroRNA-133b inhibitsproliferation and invasion of ovarian cancer cellsthrough Akt and Erk1/2 inactivation by targetingepidermal growth factor receptor[J]. InternationalJournal of Clinical and Experimental Pathology, 2015,8(9): 10605-10614.

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备注/Memo

备注/Memo:
基金项目：佛山市遗传病精准诊断工程技术研究中心项目（NO.2020001003953），佛山市科学技术局审批。
作者简介：李超（1990-），男，医学硕士，主管检验师，研究方向：肿瘤和遗传病的细胞与分子诊断，E-mail:lichao1990@hotmail.com。
通讯作者：杨兴坤，女，医学遗传学博士，副主任医师，研究方向：肿瘤遗传咨询和罕见病的基因诊断，E-mail:yangxingkun@126.com。
更新日期/Last Update: 1900-01-01