基于生物信息学分析的宫颈鳞状细胞癌预后相关基因的筛选

为了分析宫颈鳞状细胞癌(cervical squamous cell carcinoma, CESC)与正常组织中的差异表达基因(differentially expressed genes, DEGs),鉴定与CESC预后相关的关键基因,从GEO和TCGA数据库下载CESC的基因表达谱数据,利用R软件筛选CESC组织与正常组织中的DEGs,并对这些DEGs开展功能和通路富集分析;然后构建蛋白质-蛋白质相互作用(protein-protein interaction, PPI)网络,筛选出关键(hub)基因;最后对hub基因进行LASSO COX回归及总体生存率(overall survival, OS)分析。研究共筛选出167个DEGs,这些基因主要涉及染色体分离、DNA复制等生物过程,介导染色质结合、G蛋白偶联受体结合等分子功能,富集于染色体区域、纺锤体和MCM复合体。GSEA分析结果显示,富集的通路主要涉及DNA复制和细胞周期信号通路。此外,从PPI网络中筛选出20个hub基因, LASSO COX回归结果显示MAD2L1、ZWINT、RRM2、TTK、CDC6、PBK、TOP2A、KIF11、KIF20A、NCAPG、NUSAP1、CCNB1及CDK1与CESC患者的预后相关; Kaplan-Meier曲线显示, ZWINT、DTL、CCNB1、CDC6、TOP2A、CDK1、PBK、RFC4及NUSAP1的m RNA表达水平与CESC患者生存预后相关。本研究结果表明, ZWINT、CDC6、PBK、TOP2A、NUSAP1、CCNB1和CDK1为CESC的预后关键基因,为阐明CESC的分子机制提供了理论依据。     

一种新的泛素化-铁死亡相关基因特征用于肝细胞癌患者预后分析

本研究通过筛选泛素化-铁死亡相关基因构建肝细胞癌(hepatocellular carcinoma, HCC)预后模型,并探究泛素化-铁死亡相关基因对HCC预后的影响。使用R包DESeq2对TCGA-LIHC数据集进行差异分析,获取HCC差异表达基因(HCC＿DEGs)。将HCC＿DEGs、泛素化相关基因(ubiquitination related genes, URGs)和铁死亡差异表达基因(ferroptosis＿differentially expressed genes, FERR＿EDGs)取交集获得泛素化和铁死亡相关基因(UBFE＿EDGs),将UBFE＿EDGs依次进行LASSO回归分析、单因素Cox回归分析和多因素Cox回归分析以筛选出最佳独立预后基因用于构建HCC预后模型(ubiquitination and ferroptosis-related prognostic model, UBFE)。由生存曲线、 ROC曲线和校正曲线评估UBFE的预测准确性。单因素Cox回归分析、多因素Cox回归分析评估UBFE风险评分是否为HCC的预后独立危险因素,并构建列线图模型。...     

基于TCGA数据分析研究TMEM206在HCC中的表达及其与预后的相关性

[目的]探讨TMEM206表达在肝细胞癌(hepatocellular carcinoma,HCC)诊断和预后中的价值。[方法]从肿瘤基因组图谱(The Cancer Genome Atlas,TCGA)数据库下载肝细胞癌数据集表达谱及临床病理特征资料。采用KaplanMeier和Cox分析,观察TMEM206表达对肝细胞癌患者生存和预后的影响。使用TCGA数据集进行基因集富集分析(GSEA)。[结果]TMEM206在HCC肿瘤组织中的表达高于癌旁组织(P 0.000 1),并且TMEM206的表达与HCC的肿瘤分级、肿瘤分期都有显著相关。生存分析结果表明TMEM206表达越高,HCC患者的总体生存时间越短。单因素和多因素Cox分析表明,TMEM206 mRNA的表达可能是判断HCC预后的有效生物标志物。GSEA鉴定了TMEM206在HCC中的高表达可能与泛素介导的蛋白质水解、胞吞作用、RNA降解、小细胞肺癌、癌症通路、胰腺癌、胞质DNA传感途径、慢性粒细胞白血病、自然杀伤细胞介导的细胞毒性、细胞周期等反应有关。[结论]TMEM206 mRNA高表达是肝细胞癌的诊断和预后中的有效生物标志物。     

肝细胞癌患者自噬相关基因的预后作用

构建由自噬相关基因组成的预后模型,预测肝细胞癌(HCC)患者的生存预后情况,为其个性化诊疗和临床研究提供依据.利用TCGA数据库中HCC的测序信息与人类自噬数据库联合,筛选差异表达的自噬相关基因,对其进行GO富集与KEGG通路分析;通过单因素与多因素Cox分析筛选与患者生存预后明显相关的风险基因,构建预后风险评分模型;...     

联合TCGA和GEO数据库筛选乳腺癌易感基因

本研究是利用公共基因芯片数据库筛选乳腺癌的预后基因,预测和探索这些基因在乳腺癌进展中的可能机制和临床价值.首先,我们筛选了公共基因芯片数据库(gene expression omnibus,GEO)GSE22820和癌症基因组图谱(the cancer genome atlas,TCGA)乳腺癌数据库的重叠差异表达基因,联合R语言分析乳腺癌组织与癌旁正常组织差异表达的基因;其次,基于STRING数据库及Cytoscape软件构建蛋白质相互作用网络图,分析并识别了中枢基因和前3个模块;之后进行了更多的功能分析,包括基因本体(gene ontology,GO)和京都基因与基因组百科全书(kyoto encyclopedia of genes and genomes,KEGG)通路分析以及基因集富集分析(gene set enrichment analysis,GSEA),以研究这些基因的作用以及潜在的潜在机制;最后进行了Kaplan-Meier分析和Cox比例风险分析,以阐明这些基因的诊断和预后效果.相关数据分析表明15个基因的表达水平与生存预后相关,高表达基因患者的总生存时间短于低表达患者(P＜0.05);Cox比例风险分析表明UBE2T、ER-CC6L和RAD51这3个基因是预后生存的独立因素(P＜0.05);GSEA分析表明在UBE2T、ERCC6L和RAD51基因中细胞周期、基础转录因子和卵母细胞减数分裂明显富集.最终,我们得出结论,这3种基因标志物的高表达是乳腺癌预后不良因素,可作为预测乳腺癌患者转移和预后的有效生物标志物.     

COPD 差异表达基因的生物信息学分析及在LUSC样本中的表达

为确定慢性阻塞性肺病(COPD)的分子标记物及COPD与肺鳞状细胞癌(LUSC)共存的差异表达基因,探寻COPD合并肺癌的预测因子,发现新的治疗靶点。本研究采用生物信息学方法,从GEO数据库中筛选3套基因芯片数据集,挖掘COPD患者小气道上皮细胞(SAEC)的差异表达基因(DEG)以及潜在的生物标记物,并通过基因本体(GO)、京都基因与基因组百科全书(KEGG)富集分析预测DEGs的功能及参与的代谢途径。继而对DEGs构建PPI网络,使用Cytoscape软件筛选子模块和Hub基因,并将Hub基因通过TCGA数据库分析其在LUSC中的差异表达情况及差异基因间的相关性。结果共获得52个上调基因和24个下调基因,代谢通路主要集中在细胞色素P450对外源物质的代谢、化学致癌、花生四烯酸代谢及甲状腺激素合成四条途径上,通过Cytoscape软件从PPI网络中筛选得到2个功能模块和10个Hub基因,进一步验证发现其中5个基因在TCGA数据库中的LUSC样本中同样差异表达。由此推测SPP1、ALDH3A1、SPRR3、KRT6A和SPRR1B 可能为COPD 分子标记物及COPD与LUSC共存的DEGs,从而为研究COPD和LUSC的发病机制及二者潜在关系奠定良好的基础。     

基于拓扑分析方法鉴定与胃癌相关的生物标志物

胃癌(gastric cancer, GC)是最常见的恶性肿瘤之一。由于GC发病隐匿的特性,其早期检测困难。因此,研究与GC早期诊断和预后相关的生物标志物至关重要。从GEO数据库下载了3组基因表达数据集GSE79973、 GSE19826和GSE13911,通过Limma包筛选差异表达基因(differentially expressed genes, DEGs),并使用DEGs、 STRING V11数据库和Cytoscape构建了DEGs的蛋白质-蛋白质相互作用(protein-protein interaction, PPI)网络,通过4种拓扑分析方法取交集筛选hub基因,并通过单变量Cox分析、多变量Cox分析、 Lasso回归分析、生存分析、通路分析以及文献法验证hub基因。从3个数据集中分别筛选了1 599个、 333个和662个DEGs。通过拓扑分析方法筛选了4个hub基因,即CDK1、AURKA、PTTG1和UBE2C。GO和KEGG富集分析结果表明4个hub基因参与了细胞外基质-受体相互作用、糖尿病并发症中的AGE-RAGE信号通路、小细胞肺癌和蛋白质消化吸收等通路。...     

胰腺癌中CDK1的表达与预后的生物信息学分析

为探讨胰腺癌的发病机制并为胰腺癌的防治提供生物信息学依据,用GEO2R在线工具分析GSE16515中胰腺癌患者肿瘤组织和相应正常组织的差异表达基因(differentially expressed genes, DEGs),通过DAVID数据库对DEGs进行GO分析和KEGG通路富集分析,然后通过STRING数据库构建蛋白质相互作用(protein-protein interaction, PPI)网络,用Cytoscape软件进行关键基因(hub基因)筛选和功能模块分析,并在GEPIA数据库对hub基因进行验证,用CCLE数据库检测靶基因在胰腺癌组织及细胞系中的表达水平。分析结果显示胰腺癌中筛选出的376个DEGs主要涉及细胞周期、p53信号通路、蛋白质消化吸收、ECM-受体相互作用、PI3K-Akt信号通路、血小板激活信号通路。GEPIA数据库验证结果显示10个hub基因均在胰腺癌组织中高表达,其中8个hub基因与胰腺癌患者的不良预后有关。CCLE数据库检测结果显示周期蛋白依赖性激酶1 (cyclin-dependent kinase 1, CDK1)在胰腺癌组织和细胞中均有较高的表达水平。本研究结果表明CDK1可能与胰腺癌的发生发展最为相关,为进一步探究胰腺癌的发病机制提供了生物信息学依据。     

肝细胞癌相关差异基因的生物信息学及预后分析

肝细胞癌是全球癌症相关死亡的主要原因,目前对肝细胞癌的发病机制研究尚不完善,探索肝细胞癌发生、发展相关的分子标志物及其预后具有重要意义。从GEO数据库获得肝细胞癌组织和非癌组织的基因表达阵列数据GSE84402,利用GEO2R筛选差异表达基因;采用DAVID数据库对差异基因进行GO富集分析和KEGG通路分析;通过STRING数据库和Cytoscape软件构建差异表达基因对应的蛋白质相互作用网络,并从网络中筛选出核心基因(hub genes);结合KM plotter数据库的临床信息对hub genes进行预后分析。结果显示:共得到1 307个差异表达基因,其中上调基因741个,下调基因566个,这些差异表达基因主要涉及细胞分裂、细胞周期、DNA复制及物质代谢等生物学过程及生物通路。通过GO、KEGG及蛋白质相互作用网络筛选出BUB1、BUB1B、CCNA2、CCNB1、CCNB2、CDC20、CDK1、MAD2L1、PLK1等9个hub genes,进一步分析发现hub genes均与细胞周期的调控相关,表明细胞周期的调控失常在肝细胞癌的发生、发展过程中具有重要作用。生存分析显示9个hub genes在肝细胞癌患者中均为表达上调的基因,且与患者预后不良相关,这为寻找肝细胞癌患者预后相关生物标志物的研究提供了线索。     

Identification and validation of key genes associated with non-small-cell lung cancer

Non-small-cell lung cancer (NSCLC) is one of the main causes of death induced by cancer globally. However, the molecular aberrations in NSCLC patients remain unclearly. In the present study, four messenger RNA microarray datasets (GSE18842, GSE40275, GSE43458, and GSE102287) were downloaded from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) between NSCLC tissues and adjacent lung tissues were obtained from GEO2R and the overlapping DEGs were identified. Moreover, functional and pathway enrichment were performed by Funrich, while the protein–protein interaction (PPI) network construction were obtained from STRING and hub genes were visualized and identified by Cytoscape software. Furthermore, validation, overall survival (OS) and tumor staging analysis of selected hub genes were performed by GEPIA. A total of 367 DEGs (95 upregulated and 272 downregulated) were obtained through gene integration analysis. The PPI network consisted of 94 nodes and 1036 edges in the upregulated DEGs and 272 nodes and 464 edges in the downregulated DEGs, respectively. The PPI network identified 46 upregulated and 27 downregulated hub genes among the DEGs, and six (such as CENPE, NCAPH, MYH11, LRRK2, HSD17B6, and A2M) of that have not been identified to be associated with NSCLC so far. Moreover, the expression differences of the mentioned hub genes were consistent with that in lung adenocarcinoma and lung squamous cell carcinoma in the TCGA database. Further analysis showed that all the six hub genes were associated with tumor staging except MYH11, while only the upregulated DEG CENPE was associated with the worse OS of patients with NSCLC. In conclusion, the current study showed that CENPE, NCAPH, MYH11, LRRK2, HSD17B6, and A2M might be the key genes contributed to tumorigenesis or tumor progression in NSCLC, further functional study is needed to explore the involved mechanisms.     

环氧化物水解酶2在肝细胞癌中的表达水平与功能作用

本研究通过公共数据和实验数据,全面分析环氧化物水解酶2(epoxide hydrolase 2, EPHX2)在肝细胞癌中的表达情况、功能作用以及预后意义。利用GEO和MitoCarta数据集,筛选肝细胞癌中呈差异表达的线粒体相关基因;利用TCGA数据库分析EPHX2及其相关基因在肝细胞癌中的表达水平;运行R包绘制Kaplan-Meier生存曲线和功能富集分析;基于STRING和GSEA构建蛋白质互作网络和基因集富集分析;荧光定量PCR和GEO数据集验证EPHX2在肝细胞癌中的表达水平。本研究共筛选得到15个在肝细胞癌中呈差异表达的线粒体相关基因。EPHX2在肝细胞癌组织中的表达水平显著降低(P<0.01)。EPHX2表达水平与肝癌患者性别、分期和级别有关,而与年龄、T分期等因素无关。与EPHX2低表达组肝癌患者相比,EPHX2高表达组肝癌患者预后较好。功能富集结果显示,EPHX2与补体途径、脂肪酸降解等信号通路有关。蛋白质互作网络结果显示,EPHX2与HAO1、AGXT、ACOX1、GSTκ1、SCP-2、CAT、CYP2C8,CYP2C9,CYP2B6,和CYP2J2等密切相关。GSEA结果显示,EPHX2低表达组与肝癌细胞增殖、肝癌复发等基因集正相关。荧光定量PCR和GEO数据集验证结果显示,EPHX2在肝细胞癌组织和肝癌细胞株中呈显著低表达。EPHX2在肝细胞癌中呈显著低表达,提示其可能在肝细胞癌发生发展过程中发挥抑癌基因作用,但具体作用机制还需进一步验证。     

Identification of core genes and outcomes in hepatocellular carcinoma by bioinformatics analysis

Hepatocellular carcinoma (HCC) is the most common malignant liver disease in the world. However, the mechanistic relationships among various genes and signaling pathways are still largely unclear. In this study, we aimed to elucidate potential core candidate genes and pathways in HCC. The expression profiles GSE14520, GSE25097, GSE29721, and GSE62232, which cover 606 tumor and 550 nontumour samples, were downloaded from the Gene Expression Omnibus (GEO) database. Furthermore, HCC RNA-seq datasets were also downloaded from the Cancer Genome Atlas (TCGA) database. The differentially expressed genes (DEGs) were filtered using R software, and we performed gene ontology (GO) and Kyoto Encyclopedia of Gene and Genome (KEGG) pathway analysis using the online databases DAVID 6.8 and KOBAS 3.0. Furthermore, the protein-protein interaction (PPI) network complex of these DEGs was constructed by Cytoscape software, the molecular complex detection (MCODE) plug-in and the online database STRING. First, a total of 173 DEGs (41 upregulated and 132 downregulated) were identified that were aberrantly expressed in both the GEO and TCGA datasets. Second, GO analysis revealed that most of the DEGs were significantly enriched in extracellular exosomes, cytosol, extracellular region, and extracellular space. Signaling pathway analysis indicated that the DEGs had common pathways in metabolism-related pathways, cell cycle, and biological oxidations. Third, 146 nodes were identified from the DEG PPI network complex, and two important modules with a high degree were detected using the MCODE plug-in. In addition, 10 core genes were identified, TOP2A, NDC80, FOXM1, HMMR, KNTC1, PTTG1, FEN1, RFC4, SMC4, and PRC1. Finally, Kaplan-Meier analysis of overall survival and correlation analysis were applied to these genes. The abovementioned findings indicate that the identified core genes and pathways in this bioinformatics analysis could significantly enrich our understanding of the development and recurrence of HCC; furthermore, these candidate genes and pathways could be therapeutic targets for HCC treatment.     

Identification of KIF4A and its effect on the progression of lung adenocarcinoma based on the bioinformatics analysis

Background: Lung adenocarcinoma (LUAD) is the most frequent histological type of lung cancer, and its incidence has displayed an upward trend in recent years. Nevertheless, little is known regarding effective biomarkers for LUAD.Methods: The robust rank aggregation method was used to mine differentially expressed genes (DEGs) from the gene expression omnibus (GEO) datasets. The Search Tool for the Retrieval of Interacting Genes (STRING) database was used to extract hub genes from the protein–protein interaction (PPI) network. The expression of the hub genes was validated using expression profiles from TCGA and Oncomine databases and was verified by real-time quantitative PCR (qRT-PCR). The module and survival analyses of the hub genes were determined using Cytoscape and Kaplan–Meier curves. The function of KIF4A as a hub gene was investigated in LUAD cell lines.Results: The PPI analysis identified seven DEGs including BIRC5, DLGAP5, CENPF, KIF4A, TOP2A, AURKA, and CCNA2, which were significantly upregulated in Oncomine and TCGA LUAD datasets, and were verified by qRT-PCR in our clinical samples. We determined the overall and disease-free survival analysis of the seven hub genes using GEPIA. We further found that CENPF, DLGAP5, and KIF4A expressions were positively correlated with clinical stage. In LUAD cell lines, proliferation and migration were inhibited and apoptosis was promoted by knocking down KIF4A expression.Conclusion: We have identified new DEGs and functional pathways involved in LUAD. KIF4A, as a hub gene, promoted the progression of LUAD and might represent a potential therapeutic target for molecular cancer therapy.     

SARS-CoV-2病毒感染潜在关键分子生物标志物及免疫浸润特征分析

应用生物信息学方法筛选新型冠状病毒肺炎（corona virus disease 2019,COVID-19）感染的潜在关键分子生物标志物并分析其免疫浸润特征。从GEO数据库下载GSE152418数据集,其中COVID-19患者17例,健康对照17例。用加权基因共表达网络分析（weighted gene co-expression network analysis,WGCNA）方法筛选出COVID-19最相关的模块基因。与差异基因取交集得到共同基因,进行功能及信号通路富集分析,构建蛋白互作网络筛选关键基因,构建关键基因的miRNA-TF-mRNA调控网络,用CIBERSORT算法预测样本免疫细胞浸润特征。差异分析得到2 049个差异基因。WGCNA分析7个模块中“土耳其蓝色”模块与COVID-19相关性最高（r=0.91,P<0.001）。模块中基因显著性和模块隶属度呈显著正相关（r=0.96,P<0.001）。得到共同基因766个,主要参与有丝分裂、微管结合、阳离子通道活性及卵母细胞减数分裂、细胞衰老等。蛋白互作网络筛选到前10位关键基因分别为CDK1、BUB1、CCNA2、CDC20、KIF11、BUB1B、CDCA8、TOP2A、CCNB2、KIF20A,构建的miRNA-TF-mRNA网络包含51个miRNA、5个TF、10个mRNA。COVID-19患者较健康对照组幼稚B细胞、嗜酸性粒细胞浸润水平显著降低（P<0.05）,浆细胞、活化肥大细胞浸润水平显著升高（P<0.05）。通过WGCNA及蛋白互作网络分析筛选出10个关键基因,并预测到调控关键基因的5个TF及51个miRNA,且COVID-19患者与健康对照的免疫浸润特征存在统计学差异,这些与免疫细胞相关的分子标志物可能作为COVID-19免疫治疗的潜在靶标。     

Identification of hub genes and pathways in adrenocortical carcinoma by integrated bioinformatic analysis

Adrenocortical carcinoma (ACC), a rare malignant neoplasm originating from adrenal cortical cells, has high malignancy and few treatments. Therefore, it is necessary to explore the molecular mechanism of tumorigenesis, screen and verify potential biomarkers, which will provide new clues for the treatment and diagnosis of ACC. In this paper, three gene expression profiles (GSE10927, GSE12368 and GSE90713) were downloaded from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were obtained using the Limma package. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were enriched by DAVID. Protein‐protein interaction (PPI) network was evaluated by STRING database, and PPI network was constructed by Cytoscape. Finally, GEPIA was used to validate hub genes’ expression. Compared with normal adrenal tissues, 74 up‐regulated DEGs and 126 down‐regulated DEGs were found in ACC samples; GO analysis showed that up‐regulated DEGs were enriched in organelle fission, nuclear division, spindle, et al, while down‐regulated DEGs were enriched in angiogenesis, proteinaceous extracellular matrix and growth factor activity; KEGG pathway analysis showed that up‐regulated DEGs were significantly enriched in cell cycle, cellular senescence and progesterone‐mediated oocyte maturation; Nine hub genes (CCNB1, CDK1, TOP2A, CCNA2, CDKN3, MAD2L1, RACGAP1, BUB1 and CCNB2) were identified by PPI network; ACC patients with high expression of 9 hub genes were all associated with worse overall survival (OS). These hub genes and pathways might be involved in the tumorigenesis, which will offer the opportunities to develop the new therapeutic targets of ACC.     

Identification of potential core genes and pathways predicting pathogenesis in head and neck squamous cell carcinoma

Head and neck squamous cell carcinoma (HNSCC) is the most common subtype of head and neck cancer; however, its pathogenesis and potential therapeutic targets remain largely unknown. In the present study, we analyzed three gene expression profiles and screened differentially expressed genes (DEGs) between HNSCC and normal tissues. The DEGs were subjected to gene ontology (GO), Kyoto encyclopedia of genes and genomes (KEGG), protein–protein interaction (PPI), and survival analyses, while the connectivity map (CMap) database was used to predict candidate small molecules that may reverse the biological state of HNSCC. Finally, we measured the expression of the most relevant core gene in vitro and examined the effect of the top predicted potential drug against the proliferation of HNSCC cell lines. Among the 208 DEGs and ten hub genes identified, CDK1 and CDC45 were associated with unfavorable HNSCC prognosis, and three potential small molecule drugs for treating HNSCC were identified. Increased CDK1 expression was confirmed in HNSCC cells, and menadione, the top predicted potential drug, exerted significant inhibitory effects against HNSCC cell proliferation and markedly reversed CDK1 expression. Together, the findings of the present study suggest that the ten hub genes and pathways identified may be closely related to HNSCC pathogenesis. In particular, CDK1 and CDC45 overexpression could be reliable biomarkers for predicting unfavorable prognosis in patients with HNSCC, while the new candidate small molecules identified by CMap analysis provide new avenues for the development of potential drugs to treat HNSCC.