The Identification of New Biomarkers for Breast Cancer: a Study Based on TCGA and GEO Datasets

Wang Jian ¹　Shao Rongjin ¹　Gong Weida ¹　Lv Xu¹　Li Jinping ^2*

1 Cancer Hospital of Yixing City, Yixin, 214200; 2 Cancer Hospital of Ningxia Medical University, Yinchuan, 750004

*Corresponding author, ayou423x1@hotmail.com

Abstract　The purpose of this study was to forecast and explore the possible mechanism and clinical value of genetic markers in the evolution of breast cancer with a merged database to screen the prognostic genes of breast cancer. First,we screened the overlapped differentially expressed genes (DEGs) of GSE22820 and TCGA breast caner datasets by R language. Second, subsequent protein–protein interactions network analysis recognized the hub genes and top three modules among these DEGs in Cytoscape software. Then more functional analysis including Gene Ontology and KEGG pathway analysis and gene set enrichment analysis were processed to investigate the role of these genes and potential underlying mechanisms in BC. And finally Kaplan–Meier analysis and Cox hazard ratio analysis were performed to elucidate the diagnostic and prognostic effects of these genes. Analysis of relevant data shows that the expression levels of fifteen genes were interrelated with survival prognosis, and the overall survival time of the patients with high expression of the gene was shorter than those with low expression (p <0.05). But the Cox proportion hazard ratio analysis that the 3 genes were Significance, UBE2T, ERCC6L, and RAD51 could be considered independent factors for prognosis survival(p<0.05). Gene set enrichment analysis showed that the cell cycle, basic transcription factors and oocyte meiosis were significantly enriched in UBE2T, ERCC6L and RAD51 genes. Finally, we got a Conclusion that The high expression of three genetic markers is a poor prognostic factor for breast cancer and can be used as an effective biomarker to predict metastasis and prognosis of breast cancer patients.

Keywords　Bioinformatics, Biomarker, Breast cancer, Prognostic markers

乳腺癌是全世界女性中最常见的癌症类型之一，也是与癌症相关的死亡的主要原因(DeSantis et al., 2017; Torre et al., 2017)。尽管随着技术的发展，乳腺癌的治疗取得了巨大进展，但仍然是女性恶性肿瘤死亡的常见原因(Clegg et al., 2009)。在2018年诊断出近210万新病例，占女性所有癌症的25%，导致全球每年超过600,000例死亡(Bray et al., 2018)。在美国，它是与癌症相关的死亡的第二大最常见原因(Rugo et al., 2016)。因此，早期诊断乳腺癌是提高患者生存率的关键。

近年来，关于癌基因、抑癌基因，各种异源蛋白和肿瘤抗原的研究很多，针对特定肿瘤标志物的药物的开发和应用也取得了一定进展(Baselga et al., 2017)。越来越多的证据表明，分子靶向治疗是癌症治疗的有前途的研究方向(Koshiba, 2016)。因此，迫切需要揭示乳腺癌发生和发展的详细分子机制。

随着微阵列技术的广泛应用，公共数据库用户可以获得大量数据。基于整合生物信息学方法，发现了有效、新颖和可靠的分子标记物。癌症基因组图谱(the cancer genome atlas, TCGA)是最广泛测序结果的数据库，可为研究人员提供有关肿瘤分期、患者年龄、生存、转移、性别和相应临床数据的全面癌症基因组数据集。公共基因芯片数据库(Gene expression omnibus, GEO)是美国国家生物技术信息中心(National center of biotechnology information, NCBI)中全面的基因表达数据库，该数据库是世界上最大的基因芯片数据库之一。

我们从TCGA和GEO数据库中寻找可靠的生物标志物，发现UBE2T、ERCC6L、RAD51可能是乳腺癌的生物标志物，它们均与乳腺癌患者的预后有关。我们的研究可能是未来乳腺癌治疗的新的诊断标志物和潜在治疗靶标。

1结果与分析

1.1差异表达基因的筛选

通过从TCGA下载乳腺癌数据集获得了基因表达谱(1066例肿瘤组织和112个正常组织)。使用R×64 3.6.0软件Limma软件包分析了TCGA乳腺癌数据集。总共鉴定出2775个差异表达的mRNA(1113个上调，1662个下调)(图1)。随后，使用火山图分析直接鉴定mRNA的差异基因(图2)。筛选来自GEO的GSE22820微阵列数据集，筛选标准p<0.05和|FC|>2，其中包括639个上调基因和476个下调基因，以作进一步研究。最后，我们得到了两个数据集的重叠差异表达基因，包括182个上调基因和267个下调基因(图3)。

图1 TCGA乳腺癌数据库中不同表达的基因 Figure 1 Differentially expressed genes from TCGA breast caner

图2 TCGA乳腺癌数据库中差异表达基因的火山图 注: p<0.05和|FC|>2被用作截止标准 Figure 2 Volcano plot of differentially expressed genes from TCGA breast cancer Note: p<0.05 and |FC|>2 were used as the cut off criteria

图3 TCGA和GEO数据库差异表达基因的筛选 注: A: TCGA乳腺癌和GSE22820数据集的差异表达基因; B: 上调基因; C: 下调基因 Figure 3 Screening of differentially expressed genes from TCGA and GEO databases Note: A.: Differentially expressed genes in TCGA breast cancer and GSE22820 databases; B: Upregulated genes; C: Downregulated genes

1.2差异表达基因功能富集及通路分析

通过功能富集分析，我们发现这些差异表达基因主要富含蛋白质细胞外基质的细胞成分和细胞外基质成分，包括细胞-细胞连接(cell-cell junction)，基底膜(basement membrane)，基底膜和纺锤体(basement membrane and spindle)。关于生物过程(biological process, BP)，差异表达基因富含细胞外结构组织(extracellular structure organization)，细胞外基质组织(extracellular matrix organization)，泌尿生殖系统发育(urogenital system development)，肾脏系统发育(renal system development)，肾脏发育(kidney development)，骨化(ossification)和姐妹染色单体分离(sister chromatid segregation)。至于分子功能，糖胺聚糖结合(glycosaminoglycan binding)和跨膜受体蛋白激酶活性(transmembrane receptor protein kinase activity)包含在前十个富集类别中，p<0.05(图4)(表1)。

图4 差异表达基因功能分析 Figure 4 Gene ontology analysis of differentially expressed genes

表1 乳腺癌差异表达基因功能分析 注: BP: 生物过程; CC: 细胞成分; GO: 基因本体; MF: 分子功能 Table 1 Gene ontology analysis of DEGs in breast cancer Note: BP: Biological process; CC: Cellular component; GO: Gene ontology; MF: Molecular function

在进行KEGG通路分析时，我们发现这些差异表达基因主要富集于黏着斑(focal adhesion)，ECM-受体相互作用(ECM-receptor interaction)，PI3K-Akt信号通路，轴突导向(Axon guidance)，细胞周期(Cell cycle)，脂肪细胞中脂肪降解调节(Regulation of lipolysis in adipocytes)，Rap1信号通路，人乳头瘤病毒感染(Human papillomavirus infection)，MAPK信号通路和PPAR信号通路(图5;表2)。

图5 差异表达基因KEGG通路分析 Figure 5 KEGG pathway analysis of differentially expressed genes

表2 差异表达基因KEGG通路分析 Table 2 KEGG pathway analysis of differentially expressed genes in breast cancer

1.3蛋白质相互作用网络的构建和关键基因的筛选

通过将重叠的差异基因放入在线工具STRING中，我们获得了这些基因的蛋白质相互作用网络图(Protein–protein interaction,PPI)。随后，将这些PPI网络图导入Cytoscape软件中，以进一步体现这些基因编码蛋白质之间的相互作用(图6)，并且我们在PPI网络中检测顶层关键基因。具有最高连通度的前20个基因被视为顶层关键基因(图7)。此外，我们使用Cytoscape中的MCODE应用程序提取了PPI网络中最重要的三个模块(图8)。

图6 重叠差异表达基因的蛋白质互作网络分析 Figure 6 PPI network analysis of differentially expressed genes in breast cancer

图7 蛋白质互作网络中顶层前20个基因 Figure 7 Top 20 genes in PPI network

图8 蛋白质互作网络图中最重要的3个模块 Figure 8 The most important first three modules in PPI network

1.4预后生存Kaplan-Meier法分析

为了验证这些顶层关键基因和前三个模块，进行了R×64 3.6.0软件中的Kaplan-Meier分预后生存析。结果显示：SLC27A6、CXCL2、CX3CL1、SAA1、NTRK2、ACAN、SDC1、KRT14、EXO1、MND1、RAD54L、MCM10、UBE2T、ERCC6L和RAD51的表达水平较高的患者的总生存期较差，而其他患者则无明显变化(图9)。

图9 15个差异表达基因的Kaplan-Meier分析 注: p<0.05有统计学意义 Figure 9 Kaplan–Meier analysis of 15 differentially expressed genes Note: p<0.05 was considered as statistically significant

1.5临床病理特征的Cox风险比例分析

随后，我们进行了Cox风险比例分析，以确认这些关键基因的预后价值。分析结果表明：UBE2T(HR, 1.01; p=0.039)，ERCC6L(HR，1.16; p=0.008)和RAD51(HR, 1.07; p=0.031)的表达状态与患者的总体生存率相关。为了确保这三个关键基因在乳腺癌组织中的表达水平，我们提取了数据并进行分析并绘制了图表。在肿瘤组织中UBE2T，ERCC6L和RAD51的表达增加，正常组织被下调。单变量分析表明，除性别与乳腺癌患者的总体生存率没有显着相关性，其他都有相关性。之后我们分别对每个基因进行了多变量分析后，除年龄之外，所有变量均与乳腺癌患者的总体生存率无显着相关性(图10)。但是，单因素及多因素分析都表达诊断三个关键基因有统计学意义，最后，我们得出UBE2T、ERCC6L和RAD51表达水平可被视为预后生存的独立因素(表3)。

图10 UBE2T, ERCC6L和RAD51在癌组织和正常组织的表达分析 Figure 10 Expression levels of UBE2T, ERCC6L and RAD51 in normal tissues and tumor tissues

表3 UBE2T, ERCC6L和RAD51的Cox风险比例分析 注: HR:风险比; CI: 置信区间 Table 3 Cox risk ratio analysis of UBE2T, ERCC6L and RAD51 Note: HR: Hazard ratio ; CI: Confidence intervals

1.6差异基因功能富集分析

使用GSEA分析，我们发现在UBE2T、ERCC6L和RAD51基因中细胞周期(cell cycle)、基础转录因子(basal transcription factors)和卵母细胞减数分裂(oocyte meiosis)明显富集。此外，p53信号传导途径、DNA复制(DNA replication)、蛋白酶体(proteasome)、嘌呤代谢(purine metabolism)也在三个基因中高表达。另外还发现了下调的UBE2T基因在MARK信号通路中富集(图11)。

图11 UBE2T, ERCC6L和RAD51的基因功能富集分析 注: A,B: UBE2T; C,D: ERCC6L; E,F: RAD51; p<0.05 and FDR<0.05有统计学意义 Figure 11 Gene set enrichment analysis of UBE2T, ERCC6L和RAD51 Note: A,B: UBE2T; C,D: ERCC6L; E,F: RAD51; p<0.05 and FDR<0.05 were considered significantly enriched

2讨论

乳腺癌是世界上最常见的恶性肿瘤之一(Ghoncheh et al., 2016)。由于早期临床症状较少，因此绝大多数乳腺癌患者诊断明确时都是晚期。所以，探讨乳腺癌的发病机制和发展过程，寻找有效的肿瘤标志物具有重要意义(Park et al., 2010, Januskeviciene and Petrikaite, 2019)。

这项研究我们一共发现499个乳腺癌差异表达基因，并且，我们获得了这些基因的蛋白质互作网络图。随后，我们筛选了网络图中的前20个基因和前三个模块。我们对这些关键基因的整体生存分析以及这些基因在正常组织和肿瘤组织中的表达分析。结果表明这有15个基因的表达水平与生存预后相关，基因低表达的患者的总生存时间长于基因高表达的患者。但是COX风险比例分析提示只有UBE2T、ERCC6L和RAD51这3个基因被认为是乳腺癌的独立预后指标。这些研究可能为乳腺癌提供新的诊断方法和治疗目标，从而可以改善乳腺癌患者的预后。

泛素结合酶E2T(ubiquitin-conjugating enzyme E2T, UBE2T)是泛素结合酶E2家族的成员。早期的研究发现UBE2T可以通过调节范可尼贫血互补群基因D2(fanconi anemia complementation group D2, FANCD2)单泛素化水平来影响细胞DNA损伤修复程序并触发肿瘤发生(Machida et al., 2006)。UBE2T在多种肿瘤中均上调，例如在肺癌、前列腺癌、膀胱癌、鼻咽癌、乳腺癌和骨肉瘤中，UBE2T的上调可调节细胞的增殖、侵袭、转移、细胞周期和细胞凋亡(Alpi et al., 2016)。Ueki等(Ueki et al., 2009)发现UBE2T与BRCA1 / BARD1(BRCA1-associated RI NG domain1)在乳腺癌细胞中共同表达，并能抑制乳腺癌中BRCA1基因的表达，从而促进乳腺癌细胞的侵袭和转移。沉默UBE2T表达可以上调BRCA1，推测出UBE2T或其抑制剂可能是乳腺癌治疗的靶标。从浸润前导管内癌进展到浸润性导管癌的跨物种基因组学分析表明，这一过程伴随UBE2T基因改变(Colak et al., 2013)。perez等(Perez-Pena et al., 2017)发现在基底样和管腔型乳腺癌患者中UBE2T过度表达与发病结果相关，该基因在约12%的乳腺肿瘤中被扩增。

切除修复交叉互补6 (excision repair cross-com plementation group 6, ERCC6)基因是与SWI/SNF复合物相关的三磷酸腺苷酶家族成员之一，与许多疾病相关(Xu et al., 2014 , Liu et al., 2016)。Nielsen等(Nielsen et al., 2015)鉴定到ERCC6L与拓扑异构酶II在有丝分裂中协同作用，促进姐妹染色单体分离。研究表明，ERCC6L在多种类型的人类实体瘤中高度表达，因此，它被认为是癌症治疗的潜在目标(Santamaria et al., 2007)。Liu等(Liu et al., 2013)揭示了DNA修复基因ERCC6rs1917799多态性与中国人群的胃癌风险有关。 ERCC6多态性还与口腔癌、肺癌、膀胱癌和结肠直肠癌的易感性有关(Chang et al., 2009 , Ma et al., 2009 , Ramaniuk et al., 2014)。PU等(Pu et al., 2017)证实，ERCC6L高表达与乳腺癌和肾癌的较差的临床存活率显着相关。最近的研究还观察到，ERCC6L在91.51%的乳腺癌患者中高表达(Liu et al., 2018)。这些发现表明，ERCC6L可能是参与肿瘤进展的致癌基因，可能被认为是有效诊断和开发乳腺癌疗法的潜在靶标。

重组蛋白A(Recombination protein A, RAD51)与大肠杆菌RecA的同源物，是减数分裂和有丝分裂重组以及修复双链DNA断裂所必需的(Chen et al., 1998)。近年来，聚腺苷酸二磷酸核糖聚合酶抑制剂[Poly (ADP-ribose) polymerase inhibitors,PARP]已证明在治疗具有同源重组DNA修复缺陷的恶性肿瘤中具有临床实用性。最初，PARP被批准用于治疗乳腺癌易感基因(breast cancer susceptibility genes, BRCA)缺乏的乳腺癌和卵巢癌中(Stewart et al., 2018)。Toh等(Toh et al., 2019)的研究显示BRCA1异二聚体和RAD51共同定位，与它们位于BRCA1和RAD51远端结合域位置是一致的。在临床上，BARD1和BRCA1细胞株的致病变异均富含更具侵袭性的乳腺癌表型，例如三阴性乳腺癌(triple-negative breast cancer, TNBC)，与更高的复发、进展和死亡率相关(Couch et al., 2015 , Mani et al., 2019)。Jia等(Jia et al., 2019)表明，RAD51的高表达是ER阳性乳腺癌患者行新辅助内分泌治疗的不利指标，包括使用芳香化酶抑制剂治疗疗效和生存期方面都很差。

总之，目前我们研究表明这三种基因被认为是乳腺癌潜在肿瘤标志物，具有作为预后指标，对乳腺癌患者的诊断以及治疗具有较高的价值。我们以生物信息学分析的方式检测了这些基因潜在的机制，所以，以上潜在功能基因与乳腺癌的相关性及相关机制的研究仍需在临床样本中进一步的验证。

3材料与方法

3.1数据来源

微阵列数据集GSE22820从GEO数据库(https://www.ncbi.nlm.nih.gov/geo/)获得。该阵列测定了176例原发性乳腺癌样本和10例癌旁正常组织。TCGA乳腺癌数据集以及临床数据(包括112例正常组织和1066例癌组织)可从癌症基因组图谱数据库下载(https://portal.gdc.cancer.gov/)。

3.2差异表达基因筛选

采用R语言Limma数据包(Ritchie et al., 2015)对乳腺癌组织与癌旁正常组织进行差异表达基因筛选，筛选标准为p<0.05，|FC(fold change)|>2；随后，使用在线工具venny2.1.0(http：//http：//bioinformatics.psb.ugent.be/webtools/Venn/)来识别两个基因表达微阵列中的重叠差异表达基因。 随后，分别测量上调和下调的基因。

3.3基因功能富集和注释

基于前述所得的差异表达基因，使用R语言中软件包库进行了注释，依据基因本体(gene ontology,GO)数据库对差异表达基因进行生物学功能注释。同时利用京都基因与基因组百科全书(Kyoto encyclopedia of genes and genomes, KEGG)通路数据库进行差异基因信号通路的富集(Huang et al., 2007)。p<0.05被认为具有统计学意义。

3.4蛋白质相互作用网络构建

STRING是一种在线工具(https://string-db.org/)，可以构建已知的蛋白质与蛋白质互作网络并整合这些信息(Szklarczyk et al., 2017)。在获得蛋白质相互作用网络图后导入Cytoscape软件(Cytoscape_v3.6.1)中进行可视化显示，利用软件中cytoHubba插件筛选网络内顶部前20个基因(Shannon et al., 2003)，并通过MCODE插件筛选出前三个模块。

3.5基因富集分析

基因富集分析(gene set enrichment analysis,GSEA)可以确定预先定义的一组基因在两个生物学状态之间是否有统计学上差异的一种方法(Subramanian et al., 2007)。为了研究UBE2T、ERCC6L和RAD51在乳腺癌中的作用进行了GSEA分析，当正常p<0.05和FDR<0.25时，则认为该基因集显着富集。

3.6统计学分析

通过R×64 3.6.0软件分析了从GEO和TCGA数据集下载的重叠差异表达基因临床信息。Cox风险比例模型进行单变量和多变量分析。Kaplan-Meier(K-M)方法用于生存分析。所有结果均以平均值±标准差表示，p<0.05被认为揭示了统计学上的显着差异。

作者贡献

王建、李金平是本研究设计的执行人；王建、邵荣金及龚伟达完成数据分析，论文初稿的写作；吕旭参与本研究的设计，研究结果分析；李金平是项目的构思者及负责人，指导本研究的设计、数据分析，论文写作与修改。全体作者都阅读并同意最终的文本。

致谢

本研究由宜兴市科技创新(社会发展类)专项资金项目(2019SF23)资助。

参考文献

Alpi A.F., Chaugule V., and Walden H., 2016, Mechanism and disease association of E2-conjugating enzymes: lessons from UBE2T and UBE2L3, Biochem J., 473: 3401-3419

Baselga J., Coleman R.E., Cortes J., and Janni W., 2017, Advances in the management of HER2-positive early breast cancer, Crit. Rev. Oncol. Hematol., 119: 113-122

Bray F., Ferlay J., Soerjomataram I., Siegel R.L., Torre L.A., and Jemal A., 2018, Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries, CA Cancer J. Clin., 68: 394-424

Chang C.H., Chiu C.F., Wang H.C., Wu H.C., Tsai R.Y., Tsai C.W., Wang R.F., Wang C.H., Tsou Y.A., and Bau D.T., 2009, Significant association of ERCC6 single nucleotide polymorphisms with bladder cancer susceptibility in Taiwan, Anticancer Res., 29: 5121-5124

Chen P.L., Chen C.F., Chen Y., Xiao J., Sharp Z.D., and Lee W.H., 1998, The BRC repeats in BRCA2 are critical for RAD51 binding and resistance to methyl methanesulfonate treatment, Proc. Natl. Acad. Sci. USA., 95: 5287-5292

Clegg L.X., Reichman M.E., Miller B.A., Hankey B.F., Singh G.K., Lin Y.D., Goodman M.T., Lynch C.F., Schwartz S.M., Chen V.W., Bernstein L., Gomez S.L., Graff J.J., Lin C.C., Johnson N.J., and Edwards B.K., 2009, Impact of socioeconomic status on cancer incidence and stage at diagnosis: selected findings from the surveillance, epidemiology, and end results: National longitudinal mortality study, Cancer Causes Control, 20: 417-435

Colak D., Nofal A., Albakheet A., Nirmal M., Jeprel H., Eldali A., Al-Tweigeri T., Tulbah A., Ajarim D., Malik O.A., Inan M.S., Kaya N., Park B.H., and Bin Amer S.M., 2013, Age-specific gene expression signatures for breast tumors and cross-species conserved potential cancer progression markers in young women, PLoS One, 8: e63204

Couch F.J., Hart S.N., Sharma P., Toland A.E., Wang X., Miron P., Olson J.E., Godwin A.K., Pankratz V.S., Olswold C., Slettedahl S., Hallberg E., Guidugli L., Davila J.I., Beckmann M.W., Janni W., Rack B., Ekici A.B., Slamon D.J., Konstantopoulou I., Fostira F., Vratimos A., Fountzilas G., Pelttari L.M., Tapper W.J., Durcan L., Cross S.S., Pilarski R., Shapiro C.L., Klemp J., Yao S., Garber J., Cox A., Brauch H., Ambrosone C., Nevanlinna H., Yannoukakos D., Slager S.L., Vachon C.M., Eccles D.M., and Fasching P.A., 2015, Inherited mutations in 17 breast cancer susceptibility genes among a large triple-negative breast cancer cohort unselected for family history of breast cancer, J. Clin. Oncol., 33: 304-311

DeSantis C.E., Ma J., Goding Sauer A., Newman L.A., and Jemal A., 2017, Breast cancer statistics, 2017, racial disparity in mortality by state, CA Cancer J, Clin., 67: 439-448

Ghoncheh M., Pournamdar Z., and Salehiniya H., 2016, Incidence and mortality and epidemiology of breast cancer in the world, Asian Pac. J. Cancer Prev., 17: 43-46

Huang D.W., Sherman B.T., Tan Q., Collins J.R., Alvord W.G., Roayaei J., Stephens R., Baseler M.W., Lane H.C., and Lempicki R.A., 2007, The DAVID gene functional classification tool: a novel biological module-centric algorithm to functionally analyze large gene lists, Genome Biol., 8: R183

Januskeviciene I., and Petrikaite V., 2019, Heterogeneity of breast cancer: The importance of interaction between different tumor cell populations, Life Sci., 239: 117009

Jia Y., Song Y., Dong G., Hao C., Zhao W., Li S., and Tong Z., 2019, Aberrant regulation of RAD51 promotes resistance of neoadjuvant endocrine therapy in ER-positive breast cancer, Sci. Rep., 9: 12939

Koshiba M., 2016, Molecular targeted therapy and laboratory tests, Rinsho. Byori., 64: 709-716

Liu J., Deng N., Xu Q., Sun L., Tu H., Wang Z., Xing C., and Yuan Y., 2016, Polymorphisms of multiple genes involved in NER pathway predict prognosis of gastric cancer, Oncotarget, 7: 48130-48142

Liu J., Sun J., Zhang Q., and Zeng Z., 2018, shRNA knockdown of DNA helicase ERCC6L expression inhibits human breast cancer growth, Mol. Med. Rep., 18: 3490-3496

Liu J.W., He C.Y., Sun L.P., Xu Q., Xing C.Z., and Yuan Y., 2013, The DNA repair gene ERCC6 rs1917799 polymorphism is associated with gastric cancer risk in Chinese, Asian Pac. J. Cancer Prev., 14: 6103-6108

Ma H., Hu Z., Wang H., Jin G., Wang Y., Sun W., Chen D., Tian T., Jin L., Wei Q., Lu D., Huang W., and Shen H., 2009, ERCC6/CSB gene polymorphisms and lung cancer risk, Cancer Lett., 273: 172-176

Machida Y.J., Machida Y., Chen Y., Gurtan A.M., Kupfer G.M., D'Andrea A.D., and Dutta A., 2006, UBE2T is the E2 in the Fanconi anemia pathway and undergoes negative autoregulation, Mol. Cell., 23: 589-596

Mani C., Jonnalagadda S., Lingareddy J., Awasthi S., Gmeiner W.H., and Palle K., 2019, Prexasertib treatment induces homologous recombination deficiency and synergizes with olaparib in triple-negative breast cancer cells, Breast Cancer Res., 21: 104

Nielsen C.F., Huttner D., Bizard A.H., Hirano S., Li T.N., Palmai-Pallag T., Bjerregaard V.A., Liu Y., Nigg E.A., Wang L.H., and Hickson I.D., 2015, PICH promotes sister chromatid disjunction and co-operates with topoisomerase II in mitosis, Nat. Commun., 6: 8962

Park S.Y., Gonen M., Kim H.J., Michor F., and Polyak K., 2010, Cellular and genetic diversity in the progression of in situ human breast carcinomas to an invasive phenotype, J. Clin. Invest., 120: 636-644

Perez-Pena J., Corrales-Sanchez V., Amir E., Pandiella A., and Ocana A., 2017, Ubiquitin-conjugating enzyme E2T (UBE2T) and denticleless protein homolog (DTL) are linked to poor outcome in breast and lung cancers, Sci. Rep., 7: 17530

Pu S.Y., Yu Q., Wu H., Jiang J.J., Chen X.Q., He Y.H., and Kong Q.P., 2017, ERCC6L, a DNA helicase, is involved in cell proliferation and associated with survival and progress in breast and kidney cancers, Oncotarget., 8: 42116-42124

Ramaniuk V.P., Nikitchenko N.V., Savina N.V., Kuzhir T.D., Rolevich A.I., Krasny S.A., Sushinsky V.E., and Goncharova R.I., 2014, Polymorphism of DNA repair genes OGG1, XRCC1, XPD and ERCC6 in bladder cancer in Belarus, Biomarkers., 19: 509-516

Ritchie M.E., Phipson B., Wu D., Hu Y., Law C.W., Shi W., and Smyth G.K., 2015, limma powers differential expression analyses for RNA-sequencing and microarray studies, Nucleic Acids Res., 43: e47

Rugo H.S., Rumble R.B., Macrae E., Barton D.L., Connolly H.K., Dickler M.N., Fallowfield L., Fowble B., Ingle J.N., Jahanzeb M., Johnston S.R., Korde L.A., Khatcheressian J.L., Mehta R.S., Muss H.B., and Burstein H.J., 2016, Endocrine therapy for hormone receptor-positive metastatic breast cancer, american society of clinical oncology guideline, J. Clin. Oncol., 34: 3069-3103

Santamaria A., Neef R., Eberspacher U., Eis K., Husemann M., Mumberg D., Prechtl S., Schulze V., Siemeister G., Wortmann L., Barr F.A., and Nigg E.A., 2007, Use of the novel PIk1 inhibitor ZK-thiazolidinone to elucidate functions of PIk1 in early and late stages of mitosis, Mol. Biol. Cell., 18: 4024-4036

Shannon P., Markiel A., Ozier O., Baliga N.S., Wang J.T., Ramage D., Amin N., Schwikowski B., and Ideker T., 2003, Cytoscape: a software environment for integrated models of biomolecular interaction networks, Genome Res., 13: 2498-2504

Stewart M.D., Zelin E., Dhall A., Walsh T., Upadhyay E., Corn J.E., Chatterjee C., King M.C., and Klevit R.E., 2018, BARD1 is necessary for ubiquitylation of nucleosomal histone H2A and for transcriptional regulation of estrogen metabolism genes, Proc. Natl. Acad. Sci. USA., 115: 1316-1321

Subramanian A., Kuehn H., Gould J., Tamayo P., and Mesirov J.P., 2007, GSEA-P: a desktop application for gene set enrichment analysis, Bioinformatics., 23: 3251-3253

Szklarczyk D., Morris J.H., Cook H., Kuhn M., Wyder S., Simonovic M., Santos A., Doncheva N.T., Roth A., Bork P., Jensen L.J., and Von Mering C., 2017, The STRING database in 2017: quality-controlled protein-protein association networks, made broadly accessible, Nucleic Acids Res., 45: D362-D368

Toh M.R., Chong S.T., Chan S.H., Low C.E., Ishak N.D.B., Lim J.Q., Courtney E., and Ngeow J., 2019, Functional analysis of clinical BARD1 germline variants, Cold Spring Harb. Mol. Case Stud., 5: a004093

Torre L.A., Islami F., Siegel R.L., Ward E.M., and Jemal A., 2017, Global cancer in women: Burden and trends, Cancer Epidemiol Biomarkers Prev., 26: 444-457

Ueki T., Park J.H., Nishidate T., Kijima K., Hirata K., Nakamura Y., and Katagiri T., 2009, Ubiquitination and downregulation of BRCA1 by ubiquitin-conjugating enzyme E2T overexpression in human breast cancer cells, Cancer Res., 69: 8752-8760

Xu Q., Liu J.W., He C.Y., Sun L.P., Gong Y.H., Jing J.J., Xing C.Z., and Yuan Y., 2014, The interaction effects of pri-let-7a-1 rs10739971 with PGC and ERCC6 gene polymorphisms in gastric cancer and atrophic gastritis, PLoS One., 9: e89203