基于TCGA数据库探究肾透明细胞癌关键基因的表达及预后作用

目的:探究肾透明细胞癌中关键基因的表达及预后作用，寻找潜在治疗靶点。方法:从TCGA数据库下载肾透明细胞癌mRNA的表达数据，通过Rstudio软件分析肿瘤与正常组织间差异表达基因，对差异表达基因进行富集分析、蛋白-蛋白相互作用网络构建，并分析出关键基因，最后对关键基因进行预后分析。结果:得到1 855个差异表达基因，其中有1 207个是上调的，648个是下调的，富集分析发现差异基因主要与信号转导、物质代谢、免疫反应等信号通路相关。筛选出10个关键基因中有6个存在预后价值。结论:筛选出的差异基因及信号通路可以帮助我们探索肾透明细胞癌发病的分子机制，同时为靶向治疗的研究提供潜在的理论依据。     

基于细胞焦亡的结肠癌预后预测模式

目的：采用生物信息学方法探索结肠癌组织中与焦亡相关的基因,并探讨其与预后的关系,为结肠癌患者提供新的治疗靶点。方法：分别从TCGA数据库、GEO数据库中下载结肠癌患者的基因表达、转录数据及临床数据。利用R软件提取出TCGA转录数据中细胞焦亡基因的表达量,并找到差异表达基因,构建差异表达基因的蛋白互作网络。采用单因素分析、聚类分析将基因进行分型,比较两种亚型之间生存差异,得到预后相关基因。然后通过Lasso回归分析、交叉验证及优化,得到基因系数（Coef系数）,构建一种结肠癌预后的预测模型。根据该预测模型计算出TCGA样本的中位风险得分,将样本分为高、低风险组。以GEO样本作为验证组,分别对TCGA、GEO样本进行生存分析（Kaplan-Meier分析）、绘制ROC曲线、绘制风险曲线、PCA和t-SNE分析。结合模型中的风险评分,分别采用单因素及多因素分析来寻找结肠癌患者的独立预后因素。对高、低风险组进行GO和KEGG分析。最后行ssGSEA分析,对每个样本进行免疫细胞及免疫相关功能打分,得到高、低风险组之间免疫细胞及免疫细胞相关功能的差异。结果：共鉴定了52个焦亡基因在结肠癌及正常结肠...     

前列腺癌诊断/预后和耐药分子标志物的筛选及其临床意义

目的：基于已发表的芯片数据通过生物信息学方法筛选差异表达基因，以发现前列腺癌诊断/预后和耐药相关分子标志物。方法：筛选GEO数据库中已发表的前列腺癌mRNA芯片数据GSE6956和前列腺癌细胞多烯紫杉醇耐药mRNA芯片数据GSE33455进行差异表达分析；通过生物学功能注释、基因通路富集分析、蛋白质相互作用网络（protein-protein interaction，PPI）分析等生物信息学方法发现和识别与差异表达基因相关的生物学功能和信号通路；比对TCGA数据库，验证差异表达基因在前列腺癌组织及癌旁组织中的表达，并通过Kaplan-Meier分析差异表达基因对前列腺癌患者生存率的影响；用qPCR方法验证差异表达基因在前列腺癌细胞株PC3及多烯紫杉醇耐药细胞PC3-DTX中的表达情况。结果：共筛选出227个在前列腺癌和前列腺癌多烯紫杉醇耐药细胞芯片数据中共同差异表达基因。差异表达基因主要富集到了癌症相关通路（Lysosome、Sphingolipid、FoxO、Acute myeloid leukemia），并主要参与细胞黏附、自噬和胞内蛋白转运等生物学过程。构建PPI网络选取18个连接度最高的基因作为Hub基因。Hub基因和共同差异表达基因中，上调基因CITED2、LRP12和RPL17-C18orf32与前列腺癌患者的不良预后显著相关。qPCR验证显示CITED2在多烯紫杉醇耐药细胞PC3-DTX中高表达。结论：通过生物信息学方法筛选出在前列腺癌组织和耐药细胞中共同差异表达，且与前列腺癌患者的不良预后密切相关的基因，为前列腺癌诊断/预后和耐药分子标志物的研究提供了新的思路。     

Identification of key pathways and genes in colorectal cancer using bioinformatics analysis

Colorectal cancer (CRC) is the most common malignant tumor of digestive system. The aim of this study was to identify gene signatures during CRC and uncover their potential mechanisms. The gene expression profiles of GSE21815 were downloaded from GEO database. The GSE21815 dataset contained 141 samples, including 132 CRC and 9 normal colon epitheliums. The gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes pathway (KEGG) enrichment analyses were performed, and protein–protein interaction (PPI) network of the differentially expressed genes (DEGs) was constructed by Cytoscape software. In total, 3500 DEGs were identified in CRC, including 1370 up-regulated genes and 2130 down-regulated genes. GO analysis results showed that up-regulated DEGs were significantly enriched in biological processes (BP), including cell cycle, cell division, and cell proliferation; the down-regulated DEGs were significantly enriched in biological processes, including immune response, intracellular signaling cascade and defense response. KEGG pathway analysis showed the up-regulated DEGs were enriched in cell cycle and DNA replication, while the down-regulated DEGs were enriched in drug metabolism, metabolism of xenobiotics by cytochrome P450, and retinol metabolism pathways. The top 10 hub genes, GNG2, AGT, SAA1, ADCY5, LPAR1, NMU, IL8, CXCL12, GNAI1, and CCR2 were identified from the PPI network, and sub-networks revealed these genes were involved in significant pathways, including G protein-coupled receptors signaling pathway, gastrin-CREB signaling pathway via PKC and MAPK, and extracellular matrix organization. In conclusion, the present study indicated that the identified DEGs and hub genes promote our understanding of the molecular mechanisms underlying the development of CRC, and might be used as molecular targets and diagnostic biomarkers for the treatment of CRC.     

非小细胞肺癌关键基因的生物信息学分析

目的:通过生物信息学方法挖掘非小细胞肺癌(NSCLC)基因表达谱芯片数据,筛选并验证与NSCLC发生和预后相关的关键基因。方法:从Gene Expression Omnibus(GEO)数据库中下载芯片数据(GSE101929和GSE27262)。采用GEO2R在线工具筛选癌组织和癌旁组织中的差异表达基因(DEGs);采用DAVID在线工具对差异表达基因进行GO和KEGG信号通路分析并用Cytoscape和FunRich软件进行可视化;采用GEPIA在线工具对差异表达基因进行验证和预后分析。结果:共筛选出1816个差异表达基因,其中上调基因数651个,下调基因数1165个。上调基因主要富集在“基质金属肽酶活性”,下调基因主要富集在“受体活性”等分子功能。KEGG信号通路分析显示上调基因主要富集在“有丝分裂前中期”等信号通路,而下调基因主要富集在“上皮-间质转化”信号通路。蛋白-蛋白交互作用(PPI)分析显示,上调基因中的前五位为TOP2A、CDK1、CCNB1、CCNA2和KIF11,而下调基因中的前五位为IL6、FGF2、LRRK2、EDN1和IL1B。总生存率分析显示,KIF11低表达与NSCLC预后呈负相关。结论:本研究鉴定出了与NSCLC相关的关键基因,有望作为NSCLC患者潜在治疗靶点或预后判断相关的生物标志。     

整合生物信息学挖掘胃癌潜在关键生物标志物

目的:通过整合生物信息学挖掘胃癌潜在关键生物标志物.方法:本研究从GEO数据库下载数据集联合分析,借助R语言挖掘差异基因集,并功能注释和富集这些基因的相关生物通路;采用String数据库和Cytoscape软件挖掘关键基因,并借由Onconmine数据库验证关键基因.结果:本研究总共确定了98个共有差异基因,包括30个...     

Differentially expressed genes analysis and target genes prediction of miR-22 in breast cancer

Objective miR-22 is highly active in breast cancer, especially in the luminal B and HER2 subtypes. However, the detailed potential of the use of target genes for miR-22 in breast cancer are still unclear. In this study, we aimed to discover potential genes and the miRNA-DEGs network of miR-22 in breast cancer using bioinformatics approaches. Methods Analysis of microarray data GSE17508 (including 3 miR-22 knockout samples and 3 controls) obtained from the Gene Expression Omnibus (GEO) database was performed. Differentially expressed genes (DEGs) between the miR-22 knockout samples and the three control samples were detected using GEO2R. The gene ontology (GO) functional enrichment analysis and protein-protein interaction (PPI) network of DEGs were performed using the online tool Metascape and STRING database, separately. The miR-22 and DEG networks were obtained from the miRNet database. Cytoscape software was used to construct and analyze a merged miRNA-DEG network. The online tools database, mirDIP 4.1, was used to predict miR-22 target genes. Results Certain DEGs and miRNAs may be potential targets for predicting and treating miR-22 expressed breast cancer. Conclusion We constructed a prognostic model of rectal adenocarcinomas based on four immune-related lncRNAs by analyzing the data based on TCGA database, with high prediction accuracy. We also identified two biomarkers with poor prognosis (PXN-AS1 and AL158152.2) and one biomarker with good prognosis (LINC01871).     

胶质母细胞瘤差异表达基因的生物信息学分析

目的:应用生物信息学方法挖掘胶质母细胞瘤(GBM)的相关基因，进而探讨发病机制，为GBM临床诊断和靶向治疗提供理论依据。方法:从GEO(Gene Expression Omnibus)数据库下载基因芯片数据集GSE4290和GSE15824，应用GEO2R筛选GBM的差异表达基因(DEGs)。采用DAVID数据库进行GO富集和KEGG通路富集分析，分别应用STRING数据库和Cytoscape软件构建蛋白质相互作用网络和关键基因模块，筛选GBM靶基因。进一步运用ONCOMINE数据库验证临床组织样本中靶基因与GBM的关系。结果:共筛选出76个DEGs，富集分析结果显示DEGs在血管生成的正调节、抗原的呈递和处理、信号转导、调节自噬等方面存在显著富集。共挖掘出POSTN、TAGLN、CALD1、EPCAM 4个GBM靶基因，经证实均在临床GBM组织样本中存在显著上调且靶基因的上调与患者的不良预后密切相关。结论:通过生物信息学共挖掘出4个与GBM显著相关的靶基因，可能是未来GBM发病机制、临床诊断、治疗的重要研究靶点。     

基于GEO数据库的胃肠上皮化生相关基因与通路的生物信息学分析

目的 挖掘胃黏膜肠化过程中的差异基因、探索其发病机制并验证差异基因是否在胃癌发生过程中持续发挥作用。方法 在美国国立生物技术信息中心（NCBI）的GEO数据库中检索正常胃黏膜肠化表达谱芯片,并通过GEO2R分析得到差异基因,以及在不同芯片数据中均差异表达的关键基因。将差异基因利用生物学信息注释数据库DAVID进行GO生物学过程富集分析和KEGG通路富集分析,探索正常胃组织向肠化转变的相关生物学通路。并通过TCGA数据库分析关键基因在胃癌组织中的差异变化,通过KMplotter分析关键基因与胃癌患者预后的关系。结果 检索到3个涉及正常胃黏膜组织发生肠化有关基因芯片,通过差异分析得到在肠化中差异表达的基因共1188个,其中ALDOB、CLCA1、CLDN7、DMBT1、KRT20、MTTP、OLFM4、REG3A和TFF3这9个关键基因在三个芯片中均差异表达。GO富集及KEGG通路分析显示,差异基因主要参与营养物质的消化吸收、蛋白质的水解与合成、物质转运调节等过程。TCGA数据库分析显示,上述9个关键基因在胃癌组织中亦具有差异变化,且通过KM plotter分析证实其与患者预后密切相关。结论 本研究获取了在肠化中异常表达的差异基因及其相关通路,并证实关键基因与胃癌患者预后密切相关。     

胰腺癌差异表达基因的筛选及其预后价值:生物信息学分析

目的:基于生物信息学方法通过大样本挖掘胰腺导管腺癌(pancreatic ductal adenocarcinoma,PDAC)发生发展的关键基因.从公开生物数据库中挖掘PDAC的关键基因,探讨其在PDAC中的表达情况和预后价值,为PDAC的诊断和靶向治疗奠定理论基础.方法:从基因表达汇编(Gene Expressio...     

ALK融合基因阳性肺腺癌患者耐药核心基因鉴定及药物靶点分析

目的 探讨ALK融合基因阳性肺腺癌患者原发灶及转移灶基因表达谱的差异,从而探索转移灶耐药机制及相应的药物靶点分析。方法 GEO数据库中选取GSE125864,根据肿瘤组织取材部位不同分为原发灶组和转移灶组。首先,比较两组患者之间显著差异基因的表达,并分析这些显著差异基因在生物学功能和富集信号通路等方面的不同;其次,对显著差异基因进行蛋白-蛋白互作网络分析及关键模块、核心基因分析。最后,基于TCGA和癌症治疗反应门户数据库对筛选的10个关键核心进行预后、药物靶点预测等分析。结果 共筛选出227个差异基因,以肺腺癌原发灶为对照组,转移灶中共发现134个上调基因,93个下调差异基因;GO和KEGG富集分析显示,这些差异基因的功能主要涉及补体和凝血级联、化学致癌作用、视黄醇的新陈代谢等信号通路;通过蛋白-蛋白互作网络分析,筛选了10个核心基因,其中HRG、AHSG基因表达与肺腺癌不良预后相关,SERPINC1、HRG、APOA1、FGA、FGG等与多种潜在的小分子药物有一定相关性。结论 显著差异基因涉及的分子功能及信号通路可能引起ALK阳性肺腺癌患者转移灶耐药。     

基因芯片筛选多形性胶质母细胞瘤差异表达基因和通路

目的 利用基因芯片技术和生物信息学分析方法,筛选出多形性胶质母细胞瘤相关的核心基因和信号通路,为寻找多形性胶质母细胞瘤早期诊断和靶向治疗潜在标志物提供依据。方法 从GEO数据库中获取多形性胶质母细胞瘤mRNA表达谱芯片原始数据,利用R软件分析得到明显差异表达基因（differentially expressed genes, DEGs）,对DEGs进行功能注释（GO ontology）和KEGG信号通路（KEGG signaling pathway）富集,进一步构建蛋白质相互作用网络（protein-protein interaction network, PPI）,筛选核心基因,最后利用TCGA肿瘤数据库进行验证。结果 通过Pearson聚类分析发现肿瘤和正常组织聚类区分明显,说明表达谱结果可靠;差异基因共2 142个,其中上调基因968个,下调基因1 174个;GO和KEGG富集结果显示,差异基因的功能主要涉及细胞周期、细胞分裂和增殖、突触传递等生物学功能和通路,通路网络分析表明MAPK信号通路起核心调控地位。通过构建PPI网络筛选出9个与GBM密切相关的核心基因,进一步利用TCGA肿瘤数据库验证,与芯片结果一致。结论 KEGG信号通路和核心基因可能揭示了多形性胶质母细胞瘤发生发展的分子机制,核心基因可能用作多形性胶质母细胞瘤的早期诊断的分子标志物和治疗靶点。     

Establishment and verification of a prognostic tumor microenvironment-based and immune-related gene signature in colon cancer

BackgroundGastrointestinal malignant cancers affect many sites in the intestinal tract, including the colon. In this study, we purposed to improve prognostic predictions for colon cancer (CC) patients by establishing a novel biosignature of immune-related genes (IRGs) based on the tumor microenvironment (TME).MethodsUsing the estimation of stromal and immune cells in malignant tumor tissues using expression data (ESTIMATE) algorithm, we calculated the stromal and immune scores of every CC patient extracted from The Cancer Genome Atlas (TCGA). We then identified 4 immune-related messenger RNA (mRNA) biosignatures through a Cox and least absolute shrinkage and selection operator (LASSO) univariate analysis, and a Cox multivariate analysis. Relationships between tumor immune infiltration and the risk score were evaluated through the CIBERSORT algorithm and Tumor Immune Estimation Resource (TIMER) database.ResultsOur studies showed that individuals who had a high immune score (P=0.017) and low stromal score (P=0.041) had a favorable overall survival (OS) rate. By comparing high/low scores cohort, 220 differentially expressed genes (DEGs) were determined. Then an immune-related four-mRNA biosignature, including PDIA2, NAFTC1, VEGFC, and CD1B was identified. Kaplan-Meier, calibration, and receiver operating characteristic (ROC) curves verified the model’s performance. By using univariate and multivariate Cox analyses, we found each biosignature was an independent risk factor for assessing a CC patient’s survival. Three external GEO cohorts validated its good efficiency in estimating OS among individuals with CC. Moreover, the signature was also related to infiltration of several cells of the immune system in the tumor microenvironment.ConclusionsThe resultant model in our study included 4 IRGs associated with the TME. These IRGs can be utilized as an auxiliary variable to estimate and help improve the prognosis of individuals with CC.     

基于生物信息学和转录组测序分析结直肠癌5-FU耐药关键靶点北大核心

目的:筛选结直肠癌5-氟尿嘧啶(5-FU)耐药靶基因,并探究该基因对结直肠癌耐药细胞的作用及其作用机制。方法:通过GEO数据库分析GSE28702芯片筛选结直肠癌患者5-FU耐药与5-FU敏感的差异表达基因(differentially expressed genes,DEGs);利用GO与KEGG数据库对DEGs进行通路富集分析;通过STRING数据库构建蛋白质相互作用(protein-protein interaction,PPI)网络,利用Cytoscpae的cytohubba工具筛选枢纽基因。构建5-FU耐药细胞株HCT8/5-FU,利用高通量转录组测序鉴定;采用受试者工作特征曲线(receiver operating characteristic curve,ROC)对诊断价值进行评价并基于TCGA数据库COAD数据集分析关键基因表达及预后;构建TF基因过表达载体转染到HCT8/5-FU细胞系,MTT法检测细胞活力;流式细胞术测定细胞凋亡与细胞周期;qRT-PCR与Western Blot检测基因mRNA和蛋白水平。结果:共筛选出239个DEGs;DEGs主要富集在细胞外囊泡、内吞囊泡腔、药物代谢等通路;PPI得到20个枢纽基因;转录组学显示转铁蛋白(transferrin,TF)在耐药株中显著下调(P<0.05)与生物信息学分析得到的DEGs中TF基因改变趋势相同;与正常结直肠组织相比,癌组织中TF低表达(P<0.05);TF高表达患者总体生存期更长(P<0.05);上调TF表达增强了耐药细胞的5-FU敏感性(P<0.05);上调TF表达增加了5-FU诱导的细胞凋亡与G_(0)/G_(1)期的阻滞(P<0.05);上调TF表达抑制了耐药细胞ABCC1的表达(P<0.05)。结论:基于生物信息学和转录组测序筛选出结直肠癌5-FU耐药靶基因TF,TF基因可能通过调控ABCC1表达降低结直肠癌5-FU的耐药性。     

Identification of potential core genes in gastric cancer using bioinformatics analysis

BackgroundGastric cancer is the third leading cause of cancer-related mortality in China. Most patients with gastric cancer have no obvious early symptoms; thus, many of them are in the middle and late stages of gastric cancer at first diagnosis and miss the best treatment opportunity. Molecular targeted therapy is particularly important in changing this status quo.MethodsThree microarray datasets ({"type":"entrez-geo","attrs":{"text":"GSE29272","term_id":"29272"}}GSE29272, {"type":"entrez-geo","attrs":{"text":"GSE33651","term_id":"33651"}}GSE33651, and {"type":"entrez-geo","attrs":{"text":"GSE54129","term_id":"54129"}}GSE54129) were selected from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were screened using GEO2R. The Database for Annotation, Visualization and Integrated Discovery (DAVID) was used to analyze the functional features of these DEGs and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment. The protein-protein interaction (PPI) of these DEGs was visualized by Cytoscape software. The expressions of hub genes were evaluated based on Gene Expression Profiling Interactive Analysis (GEPIA). Moreover, we used the online Kaplan-Meier plotter survival analysis tool to evaluate the prognostic values of hub genes. The Target Scan database was used to predict microRNAs that could regulate the target gene, collagen type IV alpha 1 chain (COL4A1). The OncomiR database was used to analyze the expression levels of three microRNAs, as well as the relationships with tumor stage, grade, and prognosis.ResultsWe identified 78 DEGs, including 53 upregulated genes and 25 downregulated genes. The DEGs were mainly enriched in extracellular matrix organization, extracellular structure organization, and response to wounding. Moreover, three KEGG pathways were markedly enriched, including focal adhesion, complement and coagulation cascades, and extracellular matrix (ECM)-receptor interaction. Among these 78 genes, we selected 10 hub genes. The overexpression levels of these hub genes were closely related to poor prognosis and the development of gastric cancer (except for COL3A1, LOX, and CXCL8). Moreover, we found that microRNA-29a-3p, miR-29b-3p, and miR-29c-3p were the potential microRNAs that could regulate the target gene, COL4A1.ConclusionsOur results showed that FN1, COL1A1, TIMP1, COL1A2, SPARC, COL4A1, and SERPINE1 could contribute to the development of novel molecular targets and biomarker-driven treatments for gastric cancer.     

Identification of Differentially Expressed Plasma lncRNAs As Potential Biomarkers for Breast Cancer

BackgroundBreast cancer is the most common malignant tumor in women and is not easy to diagnose. Increasing evidence has underscored that long non-coding RNAs (lncRNAs) play important regulatory roles in the occurrence and progression of many cancers, including breast cancer. We aimed to identify lncRNAs in plasma as potential biomarkers for breast cancer.Patients and MethodsWe analyzed the Gene Expression Omnibus (GEO) datasets GSE22820, GSE42568, and GSE65194 to identify the common differential genes between cancer tissues and adjacent tissues. Then 14 lncRNAs were identified among the common differential genes and validated by using real-time quantitative polymerase chain reaction in 92 patients with breast cancer and 100 healthy controls. Receiver operating characteristic (ROC) curves were constructed to evaluate their diagnostic value for breast cancer.ResultsIntegrated analysis of the GEO datasets identified three significantly upregulated and 11 downregulated lncRNAs in breast cancer tissues. Compared with healthy controls, MIAT was significantly upregulated in breast cancer patient plasma, and LINC00968 and LINC01140 were significantly downregulated. ROC curve analysis suggested that these three lncRNAs can discriminate breast cancer from healthy individual with high specificity and sensitivity.ConclusionThis research identified three differentially expressed lncRNAs in breast cancer patient plasma. Our data suggest that these three lncRNAs can be used as potential diagnostic biomarkers of breast cancer.     

乳腺浸润性导管癌潜在核心基因的生物信息学分析

目的从分子层面分析乳腺浸润性导管癌(IDC)的差异表达基因(DEGs),识别潜在致病基因和分子机制。方法分析来自美国国立生物技术信息中心(NCBI)的公共基因芯片数据库(GEO)的微阵列数据集(GSE29044)。GEO2R工具筛选出IDC与正常乳腺组织间DEGs。利用DAVID在线数据库对DEGs进行相关基因功能富集分析,同时通过STRING在线数据库构建蛋白质-蛋白质互作网络(PPI),并通过Cytoscape软件进行可视化,使用Kaplan-Meier在线绘图仪生存分析工具来评估枢纽基因的预后价值。结果筛选出398个DEGs,其中有110个上调差异基因和288个下调差异基因,从PPI网络中鉴别出HMMR、CDK1、PBK、CCNB2、TPX2、AURKA、DLGAP5、NUSAP1、TOP2A和CEP55等10个枢纽基因。枢纽基因在IDC中均高表达,且与乳腺癌总体存活不利相关。结论利用生物信息学方法筛选IDC的DEGs和信号通路可以帮助识别IDC潜在致病机制,同正常乳腺组织比较,HMMR、CDK1、PBK、CCNB2、TPX2、AURKA、DLGAP5、NUSAP1、TOP2A和CEP55均在IDC中高表达,且与乳腺癌总体存活不利相关。此外,它还可作为乳腺癌不良预后的生物标志物和药物合成的靶点。     

Exploration of Molecular Mechanisms of Diffuse Large B-cellLymphoma Development Using a Microarray

Objective: We aimed to identify key genes, pathways and function modules in the development of diffuse largeB-cell lymphoma (DLBCL) with microarray data and interaction network analysis. Methods: Microarray datasets for 7 DLBCL samples and 7 normal controls was downloaded from the Gene Expression Omnibus (GEO)database and differentially expressed genes (DEGs) were identified with Student’s t-test. KEGG functionalenrichment analysis was performed to uncover their biological functions. Three global networks were establishedfor immune system, signaling molecules and interactions and cancer genes. The DEGs were compared with thenetworks to observe their distributions and determine important key genes, pathways and modules. Results: Atotal of 945 DEGs were obtained, 272 up-regulated and 673 down-regulated. KEGG analysis revealed that twogroups of pathways were significantly enriched: immune function and signaling molecules and interactions.Following interaction network analysis further confirmed the association of DEGs in immune system, signalingmolecules and interactions and cancer genes. Conclusions: Our study could systemically characterize geneexpression changes in DLBCL with microarray technology. A range of key genes, pathways and function moduleswere revealed. Utility in diagnosis and treatment may be expected with further focused research.