CN116312802A - 一种三阴性乳腺癌预后特征基因的筛选方法及其应用 - Google Patents
一种三阴性乳腺癌预后特征基因的筛选方法及其应用 Download PDFInfo
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Abstract
本发明提供了一种三阴性乳腺癌预后特征基因的筛选方法及其应用，属于特征基因筛选技术领域。本发明利用ESTIMATE算法研究公开的TNBC数据使用多个数据集验证所选基因的可靠性和普遍性。ESTIMATE算法适用于微阵列表达数据集、新的微阵列和基于RNA‑seq的转录组图谱。本发明采用ESTIMATE算法筛选得到了4个特征基因BIRC3、CD8A、GNLY和TRIM22可以作为三阴性乳腺癌预后标志物。
Description
技术领域
本发明涉及特征基因筛选技术领域，尤其涉及一种三阴性乳腺癌预后特征基因的筛选方法及其应用。
背景技术
乳腺癌是妇女中发病率和死亡率最高的癌症。2018年，全球估计有210万新诊断女性乳腺癌病例，占女性癌症病例的近四分之一。采用免疫组化方法，通过雌激素受体(ER)、孕激素受体(PR)、人表皮生长因子受体2(HER2)三种受体扩增性表达缺失诊断三阴性乳腺癌(TNBC)。TNBC占所有乳腺癌的15％～20％，其转移模式比其他亚型更明显，患者预后较差。人们对TNBC的分子机制进行了许多研究。癌症基因组图谱(Cancer GenomeAtlas,TCGA)项目有助于全面理解乳腺癌特异性分子异质性和驱动突变，包括TNBC。到目前为止，TNBC的病因和分子机制尚未得到很好的解释。因此，寻找TNBC患者的预后生物标志物，探索TNBC高发病率和高死亡率的分子机制显得尤为重要。
TME是指与肿瘤的发生、生长和转移密切相关的肿瘤细胞内外环境。在TME中，肿瘤细胞能够适应和增殖，而宿主免疫监视的检测和清除大大减少。除肿瘤细胞外，TME主要包括免疫细胞和间质细胞两种非肿瘤成分，被认为对肿瘤诊断和预后具有重要意义。目前，激活治疗性抗肿瘤免疫最有希望的方法是阻断免疫检查点以减少免疫抑制。以程序性细胞死亡蛋白1(PD-1)和程序性细胞死亡蛋白1配体1(PD-L1)为靶点的肿瘤免疫疗法正在改变传统的肿瘤治疗。此外，抗PD-1/PD-L1抗体已被证明在超过15种癌症中具有重要的临床意义，包括黑色素瘤、非小细胞肺癌(NSCLC)和肾细胞癌(RCC)。TNBC免疫微环境的多样性极大地影响了其复发风险、对化疗的反应以及免疫治疗的应用。一种免疫检查点抑制剂现在已经准备好用于TNBC的新辅助和辅助治疗研究。
ESTIMATE算法是一种利用基因表达谱特征预测肿瘤中基质细胞和免疫细胞的比例，并推断组织中肿瘤纯度的工具。目前ESTIMATE分析显示，基质/免疫细胞浸润与各种类型肿瘤患者的预后改善相关，包括胶质母细胞瘤和皮肤黑色素瘤。然而，目前尚未对TNBC免疫/基质评分进行详细分析。
发明内容
本发明的目的在于提供一种三阴性乳腺癌预后特征基因的筛选方法及其应用，为了更好地了解免疫和基质细胞相关基因对TNBC预后的影响，并挖掘与不良预后相关的TME相关基因，以探索潜在的调控机制。
为了实现上述发明目的，本发明提供以下技术方案：
本发明提供了一种三阴性乳腺癌预后特征基因的筛选方法，包括如下步骤：
(1)从GEO数据库下载包括三阴乳腺癌患者的RNA-seq数据和临床病理信息的GSE21653数据集；
(2)使用ESTIMATE算法分析GSE21653数据集，获得GSE21653样本的得分分布结果，根据中位数将得分分布结果分为高分组和低分组；
(3)在高分组和低分组之间筛选差异表达基因，对差异表达基因进行生存分析，获得与无病生存期显著相关的基因集合1；
(4)在KM-plotter网站分析基因集合1，筛选基因表达水平与E-MTAB-365数据集中的无病生存期显著相关的基因集合2；
(5)使用R包"survival"对基因集合2进行单因素Cox回归分析，根据单变量时间序列结果，筛选到与无病生存期显著相关的基因集合3；
(6)采用多变量Cox-LASSO回归分析进一步分析基因集合3与无病生存期的显著性，筛选到三阴性乳腺癌预后特征基因。
优选的，步骤(3)中还对所述差异表达基因进行了功能富集分析。
优选的，所述功能富集分析包括GO富集分析和KEGG通路分析。
优选的，步骤(3)中还通过STRING对所述基因集合1进行了PPI分析、GO富集分析、KEGG通路分析，并基于PPI分析后获得的PPI网络模块进行蛋白相互作用分析。
优选的，步骤(4)中还从GEO数据库中下载包含TNBC样本临床信息的GSE58812数据集，对筛选的基因集合2的预后相关性进行验证。
优选的，步骤(5)中还包括采用免疫组织化学染色和实时荧光定量PCR检测基因集合3在正常乳腺组织和乳腺癌样本中的表达情况。
本发明还提供了一种按照所述的筛选方法得到的特征基因作为三阴性乳腺癌预后标志物的应用。
优选的，所述特征基因为BIRC3、CD8A、GNLY和TRIM22中的一种或几种。
本发明利用ESTIMATE算法研究公开的TNBC数据使用多个数据集验证所选基因的可靠性和普遍性。ESTIMATE算法适用于微阵列表达数据集、新的微阵列和基于RNA-seq的转录组图谱。本发明采用ESTIMATE算法筛选得到了4个特征基因BIRC3、CD8A、GNLY和TRIM22可以作为三阴性乳腺癌预后标志物。
附图说明
图1为实施例1中ESTIMATE和免疫评分与TNBC的生存相关性。(A)TNBC样本ESTIMATE、免疫和基质评分的分布(小提琴曲线图显示了TNBC样本与ESTIMATE，免疫和基质评分之间的显著相关性)。(B)将TNBC样本的3个评分分为高、低评分组(以中位数为标准)，采用各样本对应的临床随访资料进行生存分析，结果显示，ESTIMATE评分与GEO数据库中TNBC样本的DFS显著相关(p<0.05)。(C)免疫评分的高分组和低分组相关关系结果。(D)基质评分的高分组和低分组相关关系结果。
图2为实施例2中基于TNBC ESTIMATE评分的差异表达基因鉴定结果。DEGs(|log2FC|>1,p<0.05)，每一行代表一个基因，每一列代表一个样本；根据ESTIMATE评分从高到低和从左到右对样本进行排序，左边的蓝绿色组是ESTIMATE高分组的样本，右边的粉红色组是ESTIMATE低分组的样本；根据差异表达分析的p值从低到高对基因进行排序，红色代表高表达基因，蓝色代表低表达基因，红色或蓝色越深，基因表达的差异程度越大。
图3为实施例2中利用STRING数据库对所有278个DEGs进行GO注释的前10位分子功能。
图4为实施例2中利用STRING数据库对所有278个DEGs进行GO注释的前10位生物过程。
图5为实施例2中利用STRING数据库对所有278个DEGs进行GO注释的前10位细胞组分。
图6为实施例2中利用STRING数据库对所有278个DEGs进行KEGG通路富集的前10位富集结果。
图7为实施例2中与TNBC预后相关的171个差异表达基因(DEGs)中6个代表基因的生存曲线与DFS显著相关(p<0.05)。
图8为实施例2中使用STRING工具构建了171个DFS相关基因的PPI网络，共包含145个节点和1438条边。PPI网络中节点的颜色反映日志(FC)值，节点的大小表示度。边缘的厚度反映了节点之间相互作用程度的综合评分。
图9为实施例2中利用STRING数据库对PPI网络进行GO注释的的前12位分子功能。
图10为实施例2中利用STRING数据库对PPI网络进行GO注释的前10位生物学过程。
图11为实施例2中利用STRING数据库对PPI网络进行GO注释的前10位细胞组分。
图12为实施例2中利用STRING数据库对PPI网络进行KEGG通路富集的前10位富集结果。
图13为实施例2中171个DFS相关基因与TNBC预后的关系。PPI网络中节点的颜色反映log(FC)值，节点的大小表示度。边缘的厚度反映了节点之间相互作用程度的综合评分。PPI网络模块1包含24个节点和245条边。
图14为实施例2中171个DFS相关基因与TNBC预后的关系。PPI网络模块2包含20个节点和70条边。
图15为实施例2中171个DFS相关基因与TNBC预后的关系。PPI网络模块3包含20个节点和64条边。
图16为实施例2中171个DFS相关基因与TNBC预后的关系。PPI网络模块4包含12个节点和28条边。
图17为实施例2中免疫微环境相关基因与TNBC患者预后的关系Kaplan-Meier生存分析生存时间与14个基因表达的关系通过R包生存标签。
图18为三阴性乳腺癌免疫微环境相关基因的验证。(A)正常乳腺组织和乳腺癌标本中BIRC3、CASP1、CD8A、EOMES和TRIM22的免疫组织化学染色。(B)在人类蛋白质图谱数据库中，正常乳腺组织和TNBC之间具有预后价值的基因的RNA表达。num(N)：正常样本量；Tumor(T)：肿瘤样本量。***p<0.001，双尾非配对t检验。(C)通过RT-qPCR检测MCF-10A、MDA-MB-231和Hs 578T细胞中BIRC3、CASP1、CLIC2、EOMES、GZMB、IL2RB和TRIM22的表达水平。mRNA水平归一化为GAPDH。误差条代表三个独立实验的平均值±标准差(**p<0.01，***p<0.001，双尾非配对t检验)。
图19为免疫微环境相关基因与无病生存期之间关联的多变量Cox-LASSO回归分析。
图20为ROC曲线，显示4个免疫微环境相关基因对时间依赖性无病生存率的预测能力。
图21为Vector和过表达TRIM22后，细胞系MDA-MB-231表达谱变化特征的热图。
图22为对差异表达基因进行KEGG通路富集分析
图23为GSEA基因集富集分析揭示上调和下调通路。
图24为7条通路(A-G)的GSEA富集分析谱：免疫系统、NOTCH信号通路、免疫效应器、细胞增殖、细胞对细胞因子的反应、上皮细胞分化、对肿瘤坏死因子的反应。
图25为Vector和过表达TRIM22后细胞系MDA-MB-231中所选差异基因的RT-qPCR分析。
具体实施方式
下面结合实施例对本发明提供的技术方案进行详细的说明，但是不能把它们理解为对本发明保护范围的限定。
实施例1
从GEO数据库(https://www.ncbi.nlm.nih.gov/geo/)下载TNBC样本的临床信息(GSE21653)。分别使用ESTIMATE、免疫和基质评分分析GSE21653样本。得分分布如图1A所示。然后根据中位数将TNBC的GSE21653样本分为高分组和低分组，利用高低分组3个评分对应的临床随访信息进行生存分析。生存曲线显示ESTIMATE评分的高分组比低分组有更高的生存率(log-rank检验p＝0.0028)，表明ESTIMATE评分与GEO数据库中三阴性乳腺癌样本的无病生存(DFS)显著相关(p<0.05)(图1B)。在免疫和基质评分的高分组和低分组中观察到类似现象(图1C-1D)。
由于ESTIMATE评分是对免疫和基质评分的综合评估，因此后续基于ESTIMATE评分进一步探索与TNBC预后相关的基因。
实施例2
基于实施例1中ESTIMATE算法下高分组和低分组之间筛选差异表达基因(DEGs)。使用R包limma(Version:3.42.2)进行差异表达分析，DEGs的筛选条件为|log2FC(foldchange)|>1和FDR<0.05。共鉴定出278个差异表达基因。高分组和低分组的DEGs如图2所示。
为了研究这些DEGs的功能，我们通过STRING数据库对276个上调基因和2个下调基因进行了功能富集分析，包括GO(分子功能、生物过程和细胞成分)和KEGG通路分析。GO和KEGG通路各部分的前10个富集项如图3～6所示(以Q值的-log10排序)。GO功能显示这些基因主要富集在蛋白结合、免疫系统过程和免疫反应以及膜部分(图3～5)。此外，通过KEGG通路分析还获得了细胞因子-细胞因子受体相互作用、趋化因子信号通路(图6)。
为了筛选与TNBC预后相关的基因，我们对所有DEGs进行了生存分析，其中171个基因与DFS显著相关(p<0.05)。171个基因中p值最低的6个基因的生存曲线如图7所示，分别是SH2D1A、CST7、GPR18、LCP2、CLIC2和ITK。核心蛋白包括CD2、SELL、CCR5、IL10RA和LCP2(图8)。随后对生存分析挖掘的基因进行GO富集分析和KEGG通路分析。数据显示这些基因主要富集在TME和免疫相关通路(图9～12)。
我们通过MCODE分析整合了PPI网络模块中171个DFS相关基因的蛋白相互作用，选取了8个模块中排名靠前的4个模块进行进一步研究。这4个模块与核心节点的蛋白相互作用程度较高。模块1共包含24个节点和245条边(图13)。其中SELL、ITGAL、CD8A、CD52、CD2为细胞黏附标志物，参与免疫应答、肿瘤转移、创面愈合等一系列重要的生理病理过程(hsa04514)。模块2总共包含20个节点和70条边(图14)。其中，C1QB、HLA-DRA、C3、HLA-DPA1与金黄色葡萄球菌感染和系统性红斑狼疮相关，表明这些基因与免疫反应(hsa05150、hsa05322)密切相关。模块3总共包含20个节点和64条边(图15)。其中，CASP1、GBP4、GBP5与nod样受体信号通路有关，是真核生物识别病原体的重要途径(hsa04621)。模块4共包含12个节点28条边(图16)，其中4个(CD4、CCR5、CD3D、ITK)与免疫反应密切相关(hsa04658、hsa04060、hsa04660)。如图13～16所示，SELL、ITGAL、CD69、HLA-DRA节点度较高，提示它们可能是TNBC重要的免疫微环境相关基因。在171个DFS相关基因中，11个与乳腺癌预后显著相关，包括CD3D、CD8A、CORO1A、GZMB、LCK、TRBC1、HLA-DRA、ACSL5、EOMES、IRF4、IRF8。此外，CD3D、CD8A、CORO1A、GZMB、EOMES和IRF8与TNBC的预后相关。其余基因CD247、CD3E、LAX1、LPXN、PRKCB、SIRPG与TNBC预后的关系尚未见报道。这些基因可能是TNBC潜在的免疫微环境相关的预后标志物。
在KM-plotter网站(http://kmplot.com/analysis/index.php？p＝service&cancer＝breast)使用包含48例TNBC样本的E-MTAB-365数据集进一步分析了171个DFS相关基因，其中36个基因的表达水平与E-MTAB-365数据集中的DFS显著相关(p<0.01)。36个基因的详细信息见表1。
表1.GSE21653数据集中与无病生存显著相关的基因，并通过KMplotter进行验证
注：正常字体的基因为已报道的与乳腺癌预后相关的基因。星号(*)标记的基因为已报道与TNBC预后相关的基因；加粗字体的基因尚未见与乳腺癌预后相关的报道。
另外，从GEO数据库中下载包含TNBC样本临床信息的GSE58812数据集，验证上述36个基因与TNBC样本预后的相关性。使用R包"survival"对这些基因进行单因素Cox回归分析。单变量时序结果显示，仅有14个基因与DFS显著相关。14个基因的详细信息见表2。Kaplan-Meier生存分析显示，这些基因表达水平较高的患者无病生存期较好(图17)。其中，SELL、GZMB、IL2RB、LCP2和CD8A参与了Module 1，CASP1和TRIM22参与了Module 3，EOMES和ITK参与了Module 4。这些基因可能是TNBC不良预后的潜在基因，可能为未来TNBC的治疗提供价值。
表2.GSE58812数据集中与生存显著相关并通过R包验证的基因
为了进一步证实这些免疫微环境相关基因在TNBC预后中的可靠性，我们验证了部分基因的表达模式。首先，我们使用免疫组织化学染色检测了来自人类蛋白质图谱网站的14个基因在正常乳腺组织和乳腺癌样本中的表达。结果显示BIRC3,CASP1,CD8A,EOMES和TRIM22在乳腺癌样本中显著下调(图18A和18B)。同时采用实时荧光定量PCR(RT-qPCR)检测MCF-10A、MDA-MB-231和Hs 578T细胞中这14个基因的mRNA相对表达水平。结果显示，与MCF-10A细胞相比，BIRC3、CASP1、CLIC2、EOMES、GZMB、IL2RB和TRIM22在MDA-MB-231和Hs 578T细胞中显著下调(图18C)。这些免疫微环境相关基因可能是TNBC良好的预后生物标志物。
多变量Cox-LASSO回归分析进一步验证14个基因的显著性。BIRC3(风险比，0.68；95％CI，0.43-1.1；p＝0.1)、CD8A(风险比，0.89；95％CI，0.67-1.2；p＝0.439)、GNLY(风险比，0.98；95％CI，0.73-1.3；p＝0.895)、TRIM22(风险比，0.72；95％CI，0.44-1.2；p＝0.195)可能与预后密切相关(1图9)。随后完成这4个基因对预后的时间依赖性受试者工作特征(ROC)曲线。预测1年生存率的AUC值为0.95(图20)。总体而言，这4个免疫微环境相关基因可以作为TNBC预后的特征基因(BIRC3基因的核苷酸序列的NCBI号为Gene ID:330，CD8A基因的核苷酸序列的NCBI号为Gene ID:925，GNLY基因的核苷酸序列的NCBI号为Gene ID:10578，TRIM22基因的核苷酸序列的NCBI号为Gene ID:10346)。
为了确定TRIM22如何调控乳腺癌的发展，我们在分别感染Vector和FLAG-TRIM22慢病毒的MDA-MB-231细胞中进行了RNA测序(RNA-seq)实验。与Vector感染的对照组相比，我们在TRIM22过表达细胞中确定了563个上调基因和436个下调基因(图21)。KEGG分析的差异表达基因(http://www.kegg.jp/kegg/pathway.html)显示，差异表达基因富集在环境信息处理、人类疾病、代谢、细胞过程包括TGF-β、脂肪酸降解、致癌相关的免疫微环境活动等通路(图22)。对差异表达的靶基因进行基因集富集分析(Gene Set EnrichmentAnalysis,GSEA)发现，GO富集收集的NOTCH信号、细胞增殖、对肿瘤坏死因子的反应、免疫效应过程、免疫系统过程、上皮细胞分化、细胞对细胞因子刺激的反应(图23)。GO生物过程中差异表达的靶基因(GO-BP)进一步分析如图24所示。接下来，我们选择了10个在肿瘤细胞和免疫系统的相互作用中起作用的与癌变相关的代表性基因，包括GATA6、SIX3、SOX4、CXCL10、PTX3、TNF、TP63、MMP1、CXCL16和DMBT1，并通过RT-qPCR验证了它们对MDA-MB-231细胞中过表达TRIM22的应答(图25)。
在上述实施例中，蛋白质相互作用分析(PPI)是指通过STRING进行PPI分析。然后使用Cytoscape软件重建PPI网络，显示出10个以上节点的网络。节点大小与节点度有关，边的厚度反映节点之间的交互程度得分，节点的颜色反映差异表达程度。使用Cytoscape的MCODE工具进行PPI网络分析，显示DEGs的PPI模块。
功能和通过富集分析是指利用STRING对差异表达基因进行功能富集分析，包括GO、KEGG通路、Reactome通路、UniProt关键词、PFAM蛋白结构域、INTERPRO蛋白结构域和特征、SMART蛋白结构域。GO分析包括分子功能(MF)、生物过程(BP)和细胞成分(CC)。
免疫组织化学染色是指图片从人类蛋白质图谱(The HumanProteinAtlas,https://www.proteinatlas.org/)下载。通过GraphPad Prism(https://www.graphpad.com/)分析正常组织和TNBC之间具有预后价值的基因的RNA表达。
细胞培养过程为所用细胞系来自美国类型培养物收藏。用含生长因子(Lonza)的乳腺上皮生长因子培养基(MEGM)试剂盒培养MCF-10A细胞。用DMEM培养基培养Hs 578T细胞。将细胞维持在37℃与5％CO2平衡的湿化培养箱中。MDA-MB-231细胞在无CO2的L-15培养基中培养。除MEGM外，所有其他培养基均添加10％胎牛血清(FBS)、100单位/ml青霉素和100mg/ml链霉素(Gibco)。
RT-qPCR过程为根据制造商说明书(Invitrogen)，使用Trizol试剂提取总RNA。使用无RNA酶的DNA酶处理(Promega)避免了潜在的DNA污染。用MMLV逆转录酶(Roche)制备cDNA。使用ABI PRISM 7500序列检测系统(Applied Biosystems)实时测量SYBR绿色荧光进行基因相对定量表达。以GAPDH为内对照，采用比较Ct法(2-△△Ct)进行结果分析。该实验至少独立地进行3次。所用引物序列列于表3。
表3RT-qPCR使用的引物序列
统计分析过程为拟合十折交叉验证的Cox生存分析和最小绝对收缩和选择算子回归(Cox-Lasso回归)模型，在R包glmnet和Survival中实现。收集相应的风险比(HR)、95％可信区间(CI)和p值。使用R包survminer绘制森林图。采用时间依赖的受试者工作特征(receiver operating characteristic,ROC)曲线评估风险评分预测预后的准确性，并描述1～10年的AUC，由曲线得出灵敏度、特异度、似然比、预测值及其95％可信区间。P值<0.05为差异有统计学意义。
本发明利用ESTIMATE算法研究公开的TNBC数据使用多个数据集验证所选基因的可靠性和普遍性。现有技术中的"CIBERSORT"方法被用于分析微阵列数据，而不是TCGARNA-seq数据。TIMER方法样本量和相关性有限，无法区分免疫细胞在间质或肿瘤中的定位，也无法捕获肿瘤细胞的异质性。ESTIMATE算法适用于微阵列表达数据集、新的微阵列和基于RNA-seq的转录组图谱。该方法的预测能力已在大型独立数据集中得到验证。本研究的发现有助于更好地理解TNBC复杂的调控网络，以及免疫和基质细胞相关基因在TNBC进展中的作用。这些发现可能为TNBC的治疗提供新的有前景的生物标志物。
本发明筛选得到的特征基因BIRC3、CD8A、GNLY和TRIM22可以作为三阴性乳腺癌预后标志物。其中BIRC3(baculoviral IAP repeat containing 3)通过调节NF-κB信号和其他炎症信号参与免疫活动。在小鼠TME中也作为一种E3泛素蛋白连接酶发挥作用。已有研究表明，TNFa-TNFR2-BIRC3-TRAF1信号通路促进小鼠转移，该通路的激活与胃肠间质瘤患者的不良预后相关。
CD8A(CD8抗原)是大多数细胞毒性T淋巴细胞的细胞表面糖蛋白，在免疫系统中介导有效的细胞间相互作用。作为T淋巴细胞上T细胞受体的辅助受体发挥作用并识别由I类MHC分子的抗原提呈细胞展示的抗原。如前所述，在新辅助治疗GeparSixto试验中，CD8A可预测病理完全缓解(pCR)增加。在几个公共乳腺癌数据集中，CD8A基因与结局改善相关。
GNLY(granulysin)是saposin样蛋白(SAPLIP)家族的成员，定位于T细胞的细胞毒性颗粒，在抗原刺激后释放。GNLY可诱导内质网应激介导的凋亡。它与NK细胞外囊泡(EV)诱导细胞毒性的能力相关。此外，血清GNLY可能是鼻咽癌、早期结直肠腺癌和肌层浸润性膀胱癌的潜在生物标志物。GNLY在TNBC免疫微环境中的作用及免疫治疗值得进一步研究。
TRIM22(刺激性反式作用因子50kDa,staff-50)是一种E3泛素连接酶，属于C-IV三结构域基序(C-IV group oftripartite motif,TRIM)家族成员，在干扰素刺激下被强烈诱导，参与细胞的固有免疫。除抗病毒作用外，TRIM22是NSCLC潜在的治疗靶点和预后标志物。我们对TRIM22在TNBC细胞中的进一步探索也表明了其在肿瘤细胞与免疫系统的相互作用中与癌变相关的作用(图7)。我们将进一步探究TRIM22在TNBC中的表达和功能。
以上所述仅是本发明的优选实施方式，应当指出，对于本技术领域的普通技术人员来说，在不脱离本发明原理的前提下，还可以做出若干改进和润饰，这些改进和润饰也应视为本发明的保护范围。
Claims (8)
1.一种三阴性乳腺癌预后特征基因的筛选方法，其特征在于，包括如下步骤：
(1)从GEO数据库下载包括三阴乳腺癌患者的RNA-seq数据和临床病理信息的GSE21653数据集；
(2)使用ESTIMATE算法分析GSE21653数据集，获得GSE21653样本的得分分布结果，根据中位数将得分分布结果分为高分组和低分组；
(3)在高分组和低分组之间筛选差异表达基因，对差异表达基因进行生存分析，获得与无病生存期显著相关的基因集合1；
(4)在KM-plotter网站分析基因集合1，筛选基因表达水平与E-MTAB-365数据集中的无病生存期显著相关的基因集合2；
(5)使用R包"survival"对基因集合2进行单因素Cox回归分析，根据单变量时间序列结果，筛选到与无病生存期显著相关的基因集合3；
(6)采用多变量Cox-LASSO回归分析进一步分析基因集合3与无病生存期的显著性，筛选到三阴性乳腺癌预后特征基因。
2.如权利要求1所述的是筛选方法，其特征在于，步骤(3)中还对所述差异表达基因进行了功能富集分析。
3.如权利要求2所述的筛选方法，其特征在于，所述功能富集分析包括GO富集分析和KEGG通路分析。
4.如权利要求3所述的筛选方法，其特征在于，步骤(3)中还通过STRING对所述基因集合1进行了PPI分析、GO富集分析、KEGG通路分析，并基于PPI分析后获得的PPI网络模块进行蛋白相互作用分析。
5.如权利要求4所述的筛选方法，其特征在于，步骤(4)中还从GEO数据库中下载包含TNBC样本临床信息的GSE58812数据集，对筛选的基因集合2的预后相关性进行验证。
6.如权利要求5所述的筛选方法，其特征在于，步骤(5)中还包括采用免疫组织化学染色和实时荧光定量PCR检测基因集合3在正常乳腺组织和乳腺癌样本中的表达情况。
7.一种按照权利要求1～6任一项所述的筛选方法得到的特征基因作为三阴性乳腺癌预后标志物的应用。
8.如权利要求7所述的应用，其特征在于，所述特征基因为BIRC3、CD8A、GNLY和TRIM22中的一种或几种。
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