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妊娠期乳腺癌相关基因的生物信息学分析
Bioinformatics analysis of the genes related to the pregnancy associated breast cancer
目的 通过生物信息分析途径对妊娠期乳腺癌患者与正常人群的差异基因进行分析，从分子水平探讨妊娠期乳腺癌的发病机制。 方法 从公共数据库基因表达数据库（GEO）中下载妊娠期乳腺癌相关数据集，采用Qlucore Omics Explore(QOE)筛选差异表达基因，用DAIVID、STRING等在线分析工具对差异表达基因进行功能富集分析，信号转导通路分析以及预测蛋白质之间的关系。 结果 共筛选出148个差异表达基因，其中表达上调24个，下调124个，对其进行生物信息学分析发现，TAGLN、ACTG2、TPM2、TPM3、MYLK、ACTA2、MTH11等基因以及MAPK信号通路、黏着斑信号通路、血管平滑肌细胞收缩信号通路等在妊娠期乳腺癌的发生发展中可能起着重要作用。通过STRING分析发现，20个基因处于核心节点位置。 结论 利用生物信息学的方法能有效分析基因芯片数据并获取生物内在信息。
Objective The differentially expressed genes of pregnancy associated breast cancer patients and normal subjects were analyzed by Bioinformatics to reveal the pathogenesis of pregnancy associated breast cancer on the molecules level and to provide new ideas for the further study on breast cancer. Methods The microarray data sets of pregnancy associated breast cancer were downloaded from the public gene expression database (Gene Expression Omnibus, GEO); differential genes of pregnancy associated breast cancer patients and normal subjects were selected by Qlucore Omics Explore (QOE); DAIVID, and STRING were adopted to analyze the function and signal pathway and to predict the protein-protein interaction of the differential genes. Results A total of 148 differentially expressed genes were screened, among which 24 were up-regulated and the other 124 were downregulated. The results of these 148 differential genes bioinformatics analysis showed that the genes TAGLN, ACTG2, TPM2, TPM3, MYLK, ACTA2, MTH11, and mitogen activated protein kinase(MAPK) signaling pathway, and focal adhesion pathway, and vascular smooth muscle contraction pathway may play important roles in the development of pregnancy associated breast cancer. The results of STRING analysis showed that 20 genes were located in the key nodes of the protein interaction network. Conclusion Bioinformatics method can be utilized to analyze microarray data effectively andmining the deeper information of the data, providing valuable clues for the further researches.
［1］Asgeirsson KS. Pregnancy-associated breast cancer［J］. Acta Obstet Gynecol Scand, 2011, 90(2): 158-166.
［2］Petrek J, Seltzer V. Breast cancer in pregnant and postpartum women［J］. J Obstet Gynaecol Can, 2003, 25(11): 944-950.
［3］Conley CA, Fowler VM. Localization of the human 64kD autoantigen D1 to myofibrils in a subset of extraocular muscle fibers［J］. Curr Eye Res, 1999, 19(4): 313-322.
［4］Zhang J, Fei F, Shao ZhM, et al. A pairing control analysis of pregnancy associated with breast cancer［J］. Shanghai Medical Journal, 2003, 26∶124-126.(in Chinese)
张杰，费菲，邵志敏，等. 妊娠哺乳期乳腺癌的配对资料分析［J］. 上海医学， 2003, 26(2): 124-126.
［5］Zhang LM,Zhan J,Zhang HQ, et al. Identification of the downstream signaling pathways of HOXA5 in breast cancer cells［J］.Acta Anatomica Sinica,2015, 46(5):634-650.(in Chinese)
张立美,战军，张宏权, 等.转录因子HOXA5在乳腺癌细胞中的下游信号通路［J］.解剖学报， 2015， 46(5): 634-650.
［6］Fiets WE, Bellot FE, Struikmans H, et al. Prognostic value of mitotic counts in axillary node negative breast cancer patients with predominantly well-differentiated tumours［J］. Eur J Surg Oncol, 2005, 31(2): 128-133.
［7］Harvell DM, Kim J, O’Brien J, et al. Genomic signatures of pregnancy-associated breast cancer epithelia and stroma and their regulation by estrogens and progesterone［J］. Horm Cancer, 2013, 4(3): 140-153.
［8］Huang DW, Sherman BT, Stephens R, et al. DAVID gene ID conversion tool［J］. Bioinformation, 2008, 2(10): 428-430.
［9］Huang DW, Sherman BT, Lempicki RA. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources［J］. Nat Protoc, 2009, 4(1): 44-57.
［10］Salwinski L, Miller CS, Smith AJ, et al. The database of interacting proteins: 2004 update［J］. Nucleic Acids Res, 2004, 32(Database issue): D449-D451.
［11］Mishra GR, Suresh M, Kumaran K, et al. Human protein reference database—2006 update［J］. Nucleic Acids Res, 2006, 34(Database issue): D411-D414.
［12］Zwart W, Flach KD, Rudraraju B, et al. SRC3 phosphorylation at Serine 543 is a positive independent prognostic factor in ER positive breast cancer［J］. Clin Cancer Res, 2016, 22(2):479-491.
［13］Green D, Dalmay T, Fraser WD. Role of miR-140 in embryonic bone development and cancer［J］. Clin Sci, 2015, 129(10): 863-873.
［14］Hayashi N, Manyam GC, Gonzalez-Angulo AM, et al. Reverse-phase protein array for prediction of patients at low risk of developing bone metastasis from breast cancer［J］. Oncologist, 2014, 19(9): 909-914.
［15］Simonova OA, Kuznetsova EB, Poddubskaya EV, et al. DNA methylation in the promoter regions of the laminin family genes in normal and breast carcinoma tissues［J］. Mol Biol, 2015, 49(4): 667-677.
［16］Dorman SN, Baranova K, Knoll JH, et al. Genomic signatures for paclitaxel and gemcitabine resistance in breast cancer derived by machine learning［J］. Mol Oncol, 2015,10(1):85-100.
［17］Danilo C, Gutierrez-Pajares JL, Mainieri MA, et al. Scavenger receptor class B type I regulates cellular cholesterol metabolism and cell signaling associated with breast cancer development［J］. Breast Cancer Res, 2013, 15(5): R87
［18］Conley CA, Fritz-Six KL, Almenar-Queralt A, et al. Leiomodins: larger members of the tropomodulin (Tmod) gene family［J］. Genomics, 2001, 73(2): 127-139.
［19］Finn SP, Smyth P, Cahill S, et al. Expression microarray analysis of papillary thyroid carcinoma and benign thyroid tissue: emphasis on the follicular variant and potential markers of malignancy［J］. Virchows Arch, 2007, 450(3): 249-260.
［20］Zafrakas M, Tarlatzis BC, Streichert T, et al. Genome-wide microarray gene expression, array-CGH analysis, and telomerase activity in advanced ovarian endometriosis: a high degree of differentiation rather than malignant potential［J］. Int J M, 2008, 21(3): 335-344.
国家自然科学基金项目;广东省领军人才基金
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