利用生物信息学分析鉴定鼻咽癌转移的信号通路及核心基因

目的利用基因芯片技术和生物信息学分析方法,筛选出鼻咽癌转移相关的核心基因和相关信号通路,为寻找鼻咽癌转移早期诊断和靶向治疗潜在标志物提供依据。方法 GSE103611的表达芯片从Gene Expression Omnibus(GEO)数据库中下载,该数据库中包含48个样本,包括24个原发鼻咽癌样本和24个放疗后转移的鼻咽癌样本。整理微阵列数据集获得差异表达基因(DEGs)与本课题组前期构建的相对于CNE-2侵袭转移能力更强的CNE-2SI细胞对比芯片差异基因进行比对获得共同差异基因。利用基因本体论(GO)和京都百科全书基因和基因组数据库(KEGG)对共同差异基因进行富集并利用DAVIDE在线进行分析。共同差异基因的蛋白质互作(PPI)网络由STRING数据库构建。Hub基因分析通过Cytoscape软件中的cytoHubba插件制作。关键基因的生存分析通过Kaplan Meier-plotter数据库分析获得。结果 GSE103611数据集中共鉴定出差异基因共3301个,其中上调506个,2795个基因被下调。本课题组的芯片中差异基因共2691个,其中上调1349个,1342个基因被下调。两个芯片共同上调基因47个,下调基因135个,共计182个。GO分析表明共同差异基因的生物学功能主要集中在基本的生物学过程和细胞黏附;主要的细胞成分包括细胞膜和细胞质;分子功能包括ATP结合等(P 0.05)。KEGG通路分析显示这些共同差异基因主要参与脂类代谢、MAPK信号通路和细胞黏附信号通路(P0.05)。CytoHubba插件分析从PPI网络中找到前20个具有高度关联性的核心基因。生存分析发现CXCL10、CUL7、PLCB2可能与在鼻咽癌不良预后相关(P 0.05)。结论利用生物信息学分析筛查鼻咽癌转移相关DEGs和通路可以帮助了解鼻咽癌转移发生的分子机制,对鼻咽癌转移的早期诊断具有临床意义。     

基于生物信息与机器学习分析类风湿关节炎疾病特征基因与免疫细胞的关系

目的：利用生物信息学与机器学习方法探求类风湿关节炎（RA）的发病机制、特征基因与免疫浸润表现，并寻找特征基因与免疫细胞的相关性。方法：从GEO数据库获取RA相关芯片，利用R语言分析基因差异，并对其进行GO与KEGG富集分析；使用机器学习方法，即LASSO回归与SVM-RFE法筛选疾病特征基因，运用ROC曲线与样本芯片检测特征基因准确性，利用CIBERSORT算法分析RA免疫浸润情况，并分析特征基因与免疫细胞的相关性。结果：GSE12021和GSE55235共获得90个差异基因，包含64个上调和26个下调差异表达基因；GO分析共得到主要条目209个，主要涉及机体白细胞激活、淋巴细胞活化、B细胞受体信号通路等；KEGG分析显示趋化因子信号通路、IL-17信号通路、Toll样受体信号通路、PPAR信号通路等通路与RA密切相关；机械学习方法筛选出IGHM、SLAMF8、CXCL10、FNDC4、AIM2、EGR1、AKR1B10等10个关键基因，ROC曲线与样本芯片检测疾病特征基因为IGHM、SLAMF8、CXCL10、AIM2、AKR1B10；免疫浸润结果显示RA滑膜组织与正常组织中的浆细胞...     

Spock1参与Adipogenesis通路在肥大型与扩张型心肌病中的机制探讨

目的 探讨SPARC(骨素)、cwcv和Kazal样结构域蛋白多糖1(Spock1)在肥大型与扩张型心肌病发生中的作用与分子机制。方法 基于CNKI数据库明确Spock1在机体各组织中的表达情况,并获得Spock1参与信号通路的关联基因,对此关联基因进行GO和KEGG富集分析,对比同类基因与心脏疾病的关系,从而分析获得Spock1参与肥大型与扩张型心肌病的具体关联信息。结果 Spock1在心脏中较高表达,并且其可能通过Adipogenesis信号通路参与肥大型心肌病(HCM)和扩张型心肌病(DCM)的发生与发展。结论 Spock1可能与肥大型和扩张型心肌病等相关心血管疾病的发生密切相关,并可能成为心脏疾病发生发展的生物标记物,甚至可成为治疗心脏疾病的新靶点基因。     

基于重建分析的ceRNA网络在痛风中的调节机制北大核心CSCD

目的:探索有关痛风发病的ceRNA网络调控机制,寻找治疗痛风的潜在靶点。方法:计算机检索GEO数据库,下载lncRNA微阵列芯片GSE160170,分析系列矩阵文件得到差异lncRNA与mRNA。比对高度保守的miRNA家族文件后得出lncRNA-miRNA关联,预测miRNA调控的mRNA,与芯片数据差异分析得到的差异mRNA取交集,得出miRNA-mRNA关联。构建ceRNA网络,运用String数据库分析蛋白互作关系,筛选关键蛋白互作模块。R软件分析关键蛋白模块的功能与相关通路,挖掘关键模块ceRNA网络。结果:痛风疾病组与正常组比较共获得差异表达的354个lncRNAs(140个下调,214个上调)、693个mRNAs(399个下调,294个上调)。在差异表达lncRNA的ceRNA网络中,有86个lncRNAs(35个下调,51个上调)、29个miRNAs、57个mRNAs。GO富集分析涉及的生物过程DNA编码转录、调控细胞增殖凋亡、调控RNA聚合酶Ⅱ启动子转录等。KEGG涉及的信号通路有IL-17信号通路、TNF信号通路、MAPK信号通路等。挖掘关键模块获得9种lncRNAs、11种miRNAs、9种mRNAs。结论:ceRNA网络可能在痛风的发病过程中发挥关键作用,其中TTTY10/hsa-miR-139-5p/AP-1轴可能具有一定研究意义。     

基于生物信息学方法鉴定艾滋病抗病毒治疗病毒学失败的关键基因

目的 探究HIV感染者接受高效抗逆转录病毒治疗失败后的外周血单核细胞基因表达差异和信号通路的表达情况.方法 从GEO数据库中下载了数据集GSE52900,进行差异化基因的筛选,并通过GO、KEGG和PPI等网络分析确定关键基因和重要生物学通路.结果 从高效抗病毒治疗失败的外周血单核细胞基因表达谱中按照P<0.05和|log2(FC)|>1筛选出79个表达差异化基因,包括55个表达上调基因和24个表达下调基因.PPI网络鉴定出5个关键基因:CD4、CCL5、CXCR4、ITGAL、C1 QB.结论 GO和KEGG分析发现差异化基因主要富集在免疫应答通路,提示免疫因素在抗病毒治疗中发挥了重要作用.     

IL-17调控溃疡性结肠炎分子机制的生物信息学分析

目的 利用生物信息学方法分析溃疡性结肠炎(UC)的枢纽基因和关键通路。方法 通过基因表达数据库(GEO)下载UC表达谱芯片GSE134025。利用R语言的Limma包筛选UC组与正常组肠黏膜细胞差异表达基因，对这些基因进行GO和KEGG分析；利用STRING数据库构建蛋白互作网络(PPI)并将结果导入Cytoscape筛选出核心基因；利用KEGG mapper绘制核心基因所在的信号通路图。结果 筛选出190个差异表达基因，其中147个上调，43个下调。GO分析结果表明，差异表达基因主要涉及炎症反应、细胞增殖的正调控等生物学过程，主要富集于细胞膜、质膜等细胞成分，具有肝素结合、生长因子等分子功能。KEGG分析显示差异基因主要富集于趋化因子信号通路，细胞因子受体相互作用等相关信号通路。从PPI网络中筛选出10个枢纽基因：白细胞介素6(IL-6)、CXC趋化因子配体8(CXCL8)、CXCL10、CXCL1、CXCL9、膜联素A1(ANXA1)、IL-1β、CC趋化因子配体20(CCL20)、CXCL2、CXCL11,其中多个基因位于IL-17调控的信号通路下游。结论 发现了10个与UC发病...     

胃相关性疾病伴肠上皮化生差异表达基因及中药预测的生物信息学分析

目的：通过生物信息学方法筛选胃相关性疾病伴肠上皮化生（IM）的关键基因与通路,探讨其发病机制及潜在治疗靶点,进而预测治疗IM的中药。方法：从公共基因芯片数据库（GEO）数据库中下载包含IM患者的胃黏膜基因表达谱数据,利用Rstudio3.5.2筛选出IM组织与正常胃黏膜组织的差异表达基因（DEGs）;使用DAVID 6.8数据库对DEGs进行GO和KEGG富集分析;基于STRING数据库和Cytoscape 3.6.1软件构建蛋白相互作用（PPI）网络,明确关键基因及核心功能模块;通过将关键基因与医学本体信息检索平台（Coremine Medical）相对应,筛选治疗IM的中药。结果：纳入2个包含IM的基因芯片数据集GSE78523和GSE60427,将2个数据集中IM相关的DEGs取交集获得135个基因,其中上调基因90个、下调基因45个。GO分析结果显示,DEGs主要涉及消化、细胞增殖的调控、细胞间黏附、钠离子跨膜转运、钾离子转运、胆囊收缩素信号通路、单核细胞趋化性、白细胞迁移、细胞外泌体等功能。KEGG通路富集结果显示DEGs显著富集于胃酸分泌、氮代谢、肾素-血管紧张素系统、蛋白...     

基于基因共表达网络分析的扩张和限制型心肌病分子特征比较

随着分子生物学的发展,两类原发性心肌病扩张型心肌病(DCM)和限制型心肌病(RCM)的研究已取得一定的进展,但是两者的发病机制和病程发展过程的分子机理尚未明确,在临床上RCM很容易被误诊为DCM。因此,首先对两类心肌疾病的RNA-Seq转录组数据进行基因表达差异显著性分析,筛选出DCM相关的差异表达基因451个、RCM相关的差异表达基因1 326个;然后用两类心肌疾病相关的差异表达基因分别构建共表达网络,并基于网络特征找出两种心肌疾病相关的重要基因(即可能的基因标志物);接着对发现的DCM相关的21个基因标志物和RCM相关的65个基因标志物进行生物功能分析,阐释两类心肌疾病的一些发生发展机制;最后从基因标志物、生物功能和信号通路多个方面比较两类心肌疾病,为在分子水平上区分两类心肌疾病提供新思路。     

基于数据库对子宫肌瘤和子宫肉瘤诊治的关键基因进行生物信息学分析

目的 探讨子宫肌瘤和子宫肉瘤发生发展的相关基因。方法 从GEO数据库下载芯片数据集GSE31699、GSE593、GSE64763、GSE68295,在R语言中分别分析子宫肌瘤瘤组织与正常子宫肌层的差异表达基因(differentially expressed gene, DEGs),子宫肉瘤癌组织与正常子宫肌层的DEGs。使用DAVID数据库对DEGs进行基因本体(gene ontology, GO)和京都基因与基因组百科全书(kyoto encyclopedia of genes and genomes, KEGG)富集分析，并在STRING网站进行蛋白网络分析，通过Cytoscape软件分别筛选出关键基因，基于TCGA和GTEx数据库验证DEGs在子宫肉瘤中的表达和诊断效能。结果 筛选到子宫肌瘤瘤组织与正常子宫肌层的DEGs 45个，其主要参与雌激素激活信号通路；获得子宫肉瘤癌组织与正常子宫肌层的DEGs 104个，其主要参与细胞周期信号通路。获得子宫肌瘤瘤组织和子宫肉瘤癌组织共同表达的DEGs 5个，差异表达的DEGs 7个。在子宫肉瘤癌组织和正常子宫肌层中这12个DEGs的表...     

去分化脂肪肉瘤潜在核心基因的生物信息学预测及分析

目的 探讨去分化脂肪肉瘤的潜在核心基因在其恶性生物学行为中的作用.方法 获取基因表达数据库(gene expres-sion omnibus,GEO)数据库中GSE21122和GSE52390的芯片数据,通过GEO2R筛选差异表达基因,对差异表达基因进行GO功能、KEGG通路富集分析和蛋白互作分析,并用Cytoscap...     

Bioinformatics analysis of gene expression profiles of Inclusion body myositis

Inclusion body myositis (IBM) is a disease with a poor prognosis and limited treatment options. This study aimed at exploring gene expression profile alterations, investigating the underlying mechanisms and identifying novel targets for IBM. We analysed two microarray datasets (GSE39454 and GSE128470) derived from the Gene Expression Omnibus (GEO) database. The GEO2R tool was used to screen out differentially expressed genes (DEGs) between IBM and normal samples. Gene Ontology（GO）function and Kyoto Encyclopedia of Genes and Genomes（KEGG）pathway enrichment analysis were performed using the Database for Annotation, Visualization and Integrated Discovery to identify the pathways and functional annotation of DEGs. Finally, protein-protein interaction (PPI) networks were constructed using STRING and Cytoscape, in order to identify hub genes. A total of 144 upregulated DEGs and one downregulated DEG were identified. The GO enrichment analysis revealed that the immune response was the most significantly enriched term within the DEGs. The KEGG pathway analysis identified 22 significant pathways, the majority of which could be divided into the immune and infectious diseases. Following the construction of PPI networks, ten hub genes with high degrees of connectivity were picked out, namely PTPRC, IRF8, CCR5, VCAM1, HLA-DRA, TYROBP, C1QB, HLA-DRB1, CD74 and CXCL9. Our research hypothesizes that autoimmunity plays an irreplaceable role in the pathogenesis of IBM. The novel DEGs and pathways identified in this study may provide new insight into the underlying mechanisms of IBM at the molecular level.     

Identification of key genes and pathways in castrate-resistant prostate cancer by integrated bioinformatics analysis

ObjectiveTo identify hub genes and pathways involved in castrate-resistant prostate cancer (CRPC).MethodsThe gene expression profiles of GSE70768 were downloaded from Gene Expression Omnibus (GEO) datasets. A total of 13 CRPC samples and 110 tumor samples were identified. The differentially expressed genes (DEGs) were identified, and the gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes pathway (KEGG) enrichment analysis was performed. Protein-protein interaction (PPI) network module analysis was constructed and performed in Cytoscape software. Weighted correlation network analysis (WGCNA) was conducted to determine hub genes involved in the development and progression of CRPC. The gene expression profiles of GSE80609 were used for validation.ResultsA total of 1738 DEGs were identified, consisting of 962 significantly down-regulated DEGs and 776 significantly upregulated DEGs for the subsequent analysis. GO term enrichment analysis suggested that DEGs were mainly enriched in the extracellular matrix organization, extracellular exosome, extracellular matrix, and extracellular space. KEGG pathway analysis found DEGs significantly enriched in the focal adhesion pathway. PPI network demonstrated that the top 10 hub genes were ALB, ACACB, KLK3, CDH1, IL10, ALDH1A3, KLK2, ALDH3B2, HBA1, COL1A1. Also, WGCNA identified the top 5 hub genes in the turquoise module, including MBD4, BLZF1, PIP5K2B, ZNF486, LRRC37B2. Plus, the Venn diagram demonstrated that HBA1 was the key gene in both GSE70768 and GSE80609 datasets.ConclusionsThese newly identified genes and pathways could help urologists understand the differences in the mechanism between CRPC and PCa. Besides, it might be promising targets for the treatment of CRPC.     

Identification of four novel prognosis biomarkers and potential therapeutic drugs for human colorectal cancer by bioinformatics analysis

Colorectal cancer(CRC)is one of the most deadly cancers in the world with few reliable biomarkers that have been selected into clinical guidelines for prognosis of CRC patients.In this study,mRNA microarray datasets GSE113513,GSE21510,GSE44076,and GSE32323 were obtained from the Gene Expression Omnibus(GEO)and analyzed with bioinformatics to identify hub genes in CRC development.Differentially expressed genes(DEGs)were analyzed using the GEO2 R tool.Gene ontology(GO)and KEGG analyses were performed through the DAVID database.STRING database and Cytoscape software were used to construct a protein-protein interaction(PPI)network and identify key modules and hub genes.Survival analyses of the DEGs were performed on GEPIA database.The Connectivity Map database was used to screen potential drugs.A total of 865 DEGs were identified,including 374 upregulated and 491 downregulated genes.These DEGs were mainly associated with metabolic pathways,pathways in cancer,cell cycle and so on.The PPI network was identified with 863 nodes and 5817 edges.Survival analysis revealed that HMMR,PAICS,ETFDH,and SCG2 were significantly associated with overall survival of CRC patients.And blebbistatin and sulconazole were identified as candidate drugs.In conclusion,our study found four hub genes involved in CRC,which may provide novel potential biomarkers for CRC prognosis,and two potential candidate drugs for CRC.     

Identification of cancer-related gene network in hepatocellular carcinoma by combined bioinformatic approach and experimental validation

HCC (hepatocellular carcinoma) is a highly aggressive malignancy that cause a mass of deaths world widely. We chose gene expression datasets of GSE27635 and GSE28248 from GEO database to find out key genes and their interaction network during the progression and metastasis of HCC. GEO2R online tool was used to screen differentially expressed genes (DEGs) between tumor and peri-tumor tissues based on these two datasets. The identified differentially expressed genes were prepared for further analysis such as GO function, KEGG pathway, PPI network analysis using Database for Annotation, Visualization and Integrated Discovery (DAVID) and Retrieval of Interacting Genes (STRING). Two modules were constructed by MOCDE plugin in Cytoscape and 21 genes were selected as hub genes during this analysis. The expression heatmap and GO function of hub genes were performed using R pheatmap package and BiNGO plugin in Cytoscape respectively. Six hub genes including CDC25 A, CDK1, HMMR, MYBL2, TOP2A were recollected for survival analysis and their expression was validated using Kaplan Meier-plotter and GEPIA website. We also investigated the DEGs between metastasis and non-metastasis tissues and two genes (NQO1 and PTHLH) are highly associated with the metastasis in HCC. Further verification using woundhealing and transwell assay confirmed their ability to mediate cell migration and invasion. In summary, our results obtained by bioinformatic analysis and experimental validation revealed the dominant genes and their interaction networks that are associated with the progression and metastasis of HCC and might serve as potential targets for HCC therapy and diagnosis.     

Identification of core genes and potential molecular mechanisms in breast cancer using bioinformatics analysis

BackgroundBreast cancer is the most frequently diagnosed cancer in women worldwide. This study aimed to elucidate the potential key candidate genes and pathways in breast cancer.MethodsThe gene expression profile dataset GSE65212 was downloaded from GEO database. Differentially expressed genes (DEGs) were obtained by the R Bioconductor packages. The Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of DEGs were performed using DAVID database. The protein–protein interaction (PPI) network was then established by STRING and visualized by Cytoscape software. Module analysis of the PPI network was performed by the plug-in Molecular Complex Detection (MCODE). Then, the identified genes were verified by Kaplan–Meier plotter online database and quantitative real-time PCR (qPCR) in breast cancer tissue samples.ResultsA total of 857 differential expressed genes were identified, of which, the upregulated genes were mainly enriched in the cell cycle, while the downregulated genes were mainly enriched in PPAR signaling pathway. Moreover, six hub genes with high degree were identified, including TOP2A, PCNA, CCNB1, CDC20, BIRC5 and CCNA2. Lastly, the Kaplan–Meier plotter online database confirmed that higher expression levels of these hub genes were related to lower overall survival. Experimental validation showed that all six hub genes had the same expression trend as predicted.ConclusionThese results identified key genes, which could be used as a new biomarker for breast cancer diagnosis and treatment.     

Differences in key genes in human alveolar macrophages between phenotypically normal smokers and nonsmokers: diagnostic and prognostic value in lung cancer

Objective: To explore the effect of smoking on gene expression in human alveolar macrophages and the value of identified key genes in the early diagnosis and prognosis of lung cancers. Methods: We downloaded three data sets ({"type":"entrez-geo","attrs":{"text":"GSE8823","term_id":"8823"}}GSE8823, {"type":"entrez-geo","attrs":{"text":"GSE2125","term_id":"2125"}}GSE2125, and {"type":"entrez-geo","attrs":{"text":"GSE3212","term_id":"3212"}}GSE3212) from the Gene Expression Omnibus (GEO) database, including 31 non-smoking and 33 smoking human alveolar macrophage samples. We identified common differentially expressed genes (DEGs), from which we obtained module genes and hub genes by using STRING and Cytoscape. Then we analyzed the protein-protein interaction (PPI) network of DEGs, hub genes, and module genes and used David online analysis tool to carry out functional enrichment analysis of DEGs and module genes. Results: A total of 85 differentially expressed genes was obtained, including 42 up-regulated genes and 43 down-regulated genes. The Human Protein Atlas and Survival analysis showed that GBP1, ITGAM, CSF1, SPP1, COL1A1, LAMB1 and THBS1 may be closely associated with the carcinogenesis and prognosis of lung cancer. Conclusion: DEGs, module, and hub genes identified in the present study help explain the effects of smoking on human alveolar macrophages and provide candidate targets for diagnosis and treatment of smoking-related lung cancer.     

Identification of the key pathways and genes involved in HER2-positive breast cancer with brain metastasis

BackgroundThe risk of brain metastasis (BM) in HER2-positive (+) breast cancer (BC) patients is significantly higher than that in HER2-negative (-) BC patients. The high incidence and mortality rate makes it urgent to elucidate the key pathways and genes involved and identify patients who are more at risk of developing BM.Materials and methodsTo identify the target genes in HER2+BC patients with BM, we analyzed the microarray datasets (GSE43837) derived from the Gene Expression Omnibus (GEO) database. The GEO2R tool was used to extract the differentially expressed genes (DEGs) involved in HER2+ primary BC and BC with BM. Bioinformatics methods including Gene Ontology (GO) functional annotation analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were performed with the screened DEGs. The protein-protein interactions of the DEGs were analyzed using the Search Tool for the Retrieval of Interacting Genes (STRING) database and visualized using Cytoscape software. Finally, GSEA analysis was performed to identify the hub genes and the important pathways.ResultsA total of 751 upregulated and 285 downregulated DEGs were identified. The GO function and KEGG pathway enrichment analyses indicated that the DEGs were all enriched in the protein binding molecular function. The top five hub nodes were screened out, included PHLPP1, UBC, ACACB, TGFB1, and ACTB. The GSEA results demonstrated that the five hub genes are mainly enriched in the ribosomal pathway.ConclusionOur study suggests that the five hub genes (PHLPP1, UBC, ACACB, TGFB1, and ACTB) are associated with HER2+BC with BM. The GSEA analysis revealed that the ribosomal pathway seems to play a very important role in the pathogenesis of HER2+BC with BM.     

基于RNA-seq的db/db小鼠大脑皮质的转录组学分析

目的通过对db/db和野生型(WT)小鼠大脑皮质组织全转录组学分析,探索参与调节2型糖尿病诱导的脑功能障碍的差异表达基因(DEGs)及相关通路和网络。方法取雄性野生型WT和db/db小鼠各9只,在第8和24周检测小鼠的体质量和血糖,之后收集动物大脑皮质进行全转录组测序(RNA-seq),并进行DEGs,GO、KEGG及蛋白互作网络分析。结果与WT组相比,db/db组大脑皮质发生变化的306个转录本中有178个表达上调,128个表达下调。DEGs中,43个上调(如Clcnka和Trim17),59个下调(如Arih1和Nectin-3)。蛋白互作网络图中的13个枢纽基因均下调,且大多属于线粒体编码家族。同时,db/db小鼠在多项GO富集类别中具有显著差异,如细胞过程、细胞部分等。此外,KEGG功能富集结果显示DEGs在代谢、帕金森病(PD)、阿尔茨海默病(AD)等相关通路中高度富集,且这些富集通路中的DEGs主要影响了线粒体氧化磷酸化过程。结论揭示了2型糖尿病与中枢神经系统损伤之间的关系及潜在的相关基因、通路及网络。     

Identification of potential prognostic biomarkers for breast cancer using WGCNA and PPI integrated techniques

In this study, we aimed to detect promising prognostic factors of breast cancer and interpreted the relevant mechanisms using an integrated bioinformatics analysis. RNA sequencing profile of breast cancer was downloaded from The Cancer Genome Atlas (TCGA) and the Genotype-Tissue Expression (GTEx) databases, which were combined as a group (TCGA_GTEx). GSE70947 dataset was from Gene Expression Omnibus. Blue and turquoise modules, respectively identified in TCGA_GTEx database and GSE70947 dataset using weighted co-expression network analysis (WGCNA), were both notably associated with breast cancer. By comparing genes in the two significant modules with differentially expressed genes (DEGs), we obtained a set of 40 shared genes, which were mainly enriched in chromosome segregation and mismatch repair pathway. After protein-protein interaction (PPI) network and overall survival analysis, two hub genes EXO1 and KIF4A were extracted from the set of 40 shared genes, which were up-regulated and associated with the dismal outcome of breast cancer patients. There was a notable negative correlation between EXO1 and KIF4A expression and age of breast cancer patients, whereas a positive relationship with two another clinical traits stage and tumor category was detected. Univariate and multivariate Cox regression analysis revealed that the two hub genes could be independent prognostic factors of breast cancer. Mechanistically, gene correlation analysis suggested that EXO1 and KIF4A exerted their oncogenic role via promoting breast cancer cell proliferation. Overall, our findings identify two promising individual prognostic predictors of breast cancer and pave the new way for diagnosis and therapy strategy of breast cancer.