基于GEO和TCGA数据库分析促癌基因INHBA和抑癌基因CLCA4、CA4在结直肠癌中表达

目的:通过分析GEO数据库结直肠癌相关芯片集,寻找差异基因,并在TCGA数据库和GEO数据库进行验证,为结直肠癌的早期诊断寻找标志物。方法:分析GEO数据库结直肠癌相关芯片集GSE21510、GSE25071、GSE32323。分别分析差异基因,采用文恩图软件查找共同差异基因。进一步在TCGA数据库查找差异基因在结直肠癌中的表达及生存曲线。最后通过GEO数据库GSE24514验证差异基因的表达。结果:GSE21510,包含104例样本,共筛选出251个差异基因,其中上调基因146个,下调基因105个。GSE25071,包含50例样本,共筛选出669个差异基因,其中上调基因312个,下调基因357个。GSE32323,包含10例样本,共筛选出353个差异基因,其中上调基因115个,下调基因238个。在样本中上调基因为促癌基因,下调基因为抑癌基因。经文恩图分析,3个基因集交集共有15个基因,其中上调基因3个,下调基因12个。在TCGA数据库中查找差异基因的表达量和生存曲线,生存曲线选择结肠癌数据集,选取279个样本进行分析。根据差异基因的表达和生存曲线,最终确定促癌基因INHBA和抑癌基因CLCA4、CA4为结直肠癌的标志物。最后在GSE24514芯片集验证差异基因的表达。结论:通过GEO和TCGA数据库筛选及验证,发现在结直肠癌组织中INHBA基因明显上调,CLCA4、CA4基因明显下调。最终确定促癌基因INHBA和抑癌基因CLCA4、CA4可作为结直肠癌早期诊断的标志物。     

脊髓损伤机制研究的生物信息学分析

用生物信息学方法筛选参与脊髓损伤(spinal cord injury, SCI)发展过程的关键分子和通路,可为脊髓损伤发展机制的研究提供指导。从GEO数据库下载基因芯片数据,并将数据集中的样本分为脊髓损伤组(SCI组)和正常组(normal组)。应用R语言处理来自不同数据集样本间的批次效应,同时对基因芯片的表达数据进行标准化处理,并通过PCA分析监测标准化处理后数据的质量。应用R语言中的limma包分析标准化后的基因表达矩阵,以得到差异基因。将差异基因导入DAVID数据库进行GO (gene ontology)分析,并通过KEGG数据库进行通路分析。然后应用STRING数据库构建PPI网络,并通过Cytoscape中的cytoHubba插件分析得到10个hub基因。最后应用箱式图监测hub基因在不同样本中的表达,并用GeneCards数据库查询hub基因的功能。此外,为了补充差异基因筛选的不足,通过R语言对基因表达矩阵进行了GSEA (gene set enrichment analysis)分析。结果显示:TYROBP、ITGB2、PTPRC和FCER1G等基因在脊髓损伤发展过程中发挥重要的作用;细胞外基质的炎症反应、葡糖醛酸基转移酶活性的变化和星形胶质细胞的迁移等与脊髓损伤的发展机制关系密切; TNF信号通路、NF-κB信号通路和p53信号通路在脊髓损伤的发展机制中发挥重要的作用。这些关键的分子和通路在脊髓损伤中的作用值得我们进行更深入的探讨。     

联合TCGA,GEO数据库分析THY1在胃癌中的表达及临床意义

为了研究THY1 (THYmocyte differentiation antigen 1)在胃癌中的表达情况,预测并探讨THY1参与肿瘤发生发展的可能机制及临床价值。本研究从GEO (Gene expression omnibus)数据库中选择GSE33335、GSE56807、GSE63089 3个芯片的数据,利用"limma"、"RobustRankAggreg".R语言包,找到3个芯片中共同的差异基因,并通过DAVID网站对差异基因进行功能通路富集分析,利用"ggplots".R语言包进行可视化分析。通过Kmplotter在线网站筛选跟胃癌生存预后相关的差异基因。利用Oncomine数据库探究THY1基因在不同癌症及胃癌中的差异表达。利用癌症基因组图谱TCGA (Cancer genome atlas)数据库获取胃癌数据集,随后以THY1的表达水平进行患者的生存分析和基因集富集分析(gene set enrichment analysis, GSEA),以期挖掘THY1的潜在临床意义及其分子机制。结果本研究发现THY1的表达水平与胃癌患者的生存预后相关,THY1高表达的患者总生存期明显短于低表达的患者(p0.001) THY1高表达样本富集了细胞黏附、细胞因子受体互作通路、ECM受体通路、粘着斑通路、骨架蛋白调控、癌症通路、TGF-β通路等基因集。研究结果表明,在胃癌中,THY1高表达是一种预后不良因素,可以作为预测患者转移发生、判断预后的有效生物标志物。     

基于单细胞测序构建胶质母细胞瘤预后模型

[目的]基于单细胞测序筛选胶质母细胞瘤特征基因并构建预后模型。[方法]分析GEO数据库单细胞RNA测序数据集GSE84465,筛选出GBM细胞分化相关的差异基因。下载TCGA数据库GBM的基因表达谱和临床数据,采用Lasso回归、Cox回归分析筛选出特征基因构建预后模型,根据独立预后因素构建列线图,GSE83300作为外部验证集。基于风险评分中位数将患者分组,比较两组生存差异。[结果]通过scRNA-seq得到492个分化差异基因,经过回归分析得到基于6个基因(PLAUR、RARRES2、G0S2、MDK、SERPINE2、CD81)的预后模型。其1、3、5年ROC曲线下面积均大于0.7;KM分析显示高低风险组预后存在差异(P<0.001),GSE83300验证结果与TCGA一致。多因素Cox回归分析表明年龄和风险评分可以作为独立影响因素(P<0.01);C-Index(0.679)、校准图显示列线图预测模型有良好的拟合度。GSEA分析示高低风险组差异基因集参与细胞因子受体相互作用、抗原处理与提呈等通路。[结论]由PLAUR、RARRES2、G0S2、MDK、SERPINE...     

基于共表达网络挖掘不同前列腺特异抗原水平下的前列腺癌发展相关基因

目的采用权重基因共表达网络分析方法(WGCNA)挖掘不同前列腺特异抗原(PSA)水平下的前列腺癌发展枢纽基因。方法数据来自NCBI的GEO数据库中下载不同PSA水平下的前列腺癌全基因组表达数据集,经过数据预处理后,用WGCNA构建基因共表达网络,识别不同PSA水平下的前列腺癌发展模块与枢纽基因。结果筛选出的差异基因聚集成一个模块,并且找到10个枢纽基因。结论结合文献发现,SNAI2、TRIM29、LAMB3、CYP3A5和SLC14A1这5个基因很可能影响不同PSA水平下前列腺癌的发展。     

强直性脊柱炎差异表达基因的生物信息学分析

目的探讨强直性脊柱炎(AS)患者差异表达基因,并基于差异基因探讨强直性脊柱炎发病相关的可能生物学过程和信号通路。方法检索基因表达谱数据库(GEO)并筛选AS相关基因表达谱数据集。应用GEO在线分析功能GEO2R分析AS组和正常对照组的差异表达基因,用Cytoscape软件clueGO插件进行基因本体论和京都基因与基因组百科全书分析,采用String蛋白-蛋白相互作用(PPI)数据库分析差异表达基因编码蛋白间的相互作用;应用Cytoscape绘制蛋白相互作用网络图,并软件筛选信号通路关键基因分析。结果选取AS患者全血表达数据集GSE25101为研究对象,分析获得差异表达基因72个。72个差异表达基因分子功能主要为参与高迁移率族盒染色体蛋白1(HMGB1)转导机制;生物学过程主要富集于巨噬细胞迁移、骨髓细胞凋亡过程、线粒体呼吸链复合体装配、ATP合成偶联电子传输、线粒体ATP合成耦合电子输运等;细胞成分主要富集于呼吸链复合体、线粒体呼吸体等。信号通路富集于氧化磷酸化信号通路和帕金森综合征相关信号通路。PPI网络经过cytohubba插件筛选,ATP5J、NDUFS4、UQCRB、UQCRH、NDUFB3、COX7B、LSM3、ATP5EP2、ENY2、PSMA4被筛选为网络中的核心基因。结论通过生物信息学方法进行预测了AS的潜在机制,并筛选出10个潜在的与AS相关的重要分子,其中氧化磷酸化可能在AS发病机制中发挥了重要的作用。     

基于RNA-Seq分析MpigE在红曲霉色素生物合成中的转录调控

【目的】分析MpigE的缺失在转录水平对红曲色素产生的影响。【方法】对实验室保存的野生型紫色红曲霉(Monascus purpureus)Mp-21和△MpigE菌株进行高通量转录组测序、注释、表达差异基因功能富集分析和基因通路富集分析,在转录水平揭示MpigE缺失后红曲霉色素产量变化的原因。【结果】通过RNA-Seq测序,每个样品获得7.5–8.5 Gb的原始数据,经过拼接后得到7219个转录本(Unigenes),其中成功注释的为5692个。差异基因表达富集分析发现基因缺失菌株△MpigE相较于野生型菌株Mp-21上调差异基因达到199个,下调差异基因为293个。【结论】MpigE的缺失能够促进红曲霉中中央碳代谢和乙酰辅酶A代谢相关基因的表达以此影响色素生物合成。     

肾纤维化关键基因的生信筛选及分析

[目的]利用生物信息学技术筛选与肾纤维化相关基因，并预测与基因相关通路，疾病、细胞类型和有关药物。[方法]在公共基因芯片数据库(GEO)中获取与肾纤维化相关三个基因数据集，利用Venn图鉴定出共表达的差异基因，Metascape工具对差异基因进行功能(GO)和通路富集(KEGG)分析，还利用STRING构建蛋白相互作用网络(PPI)网络以及可视化工具Cytoscape筛选关键基因，最后用及Enrichr和Comparative Toxicogenomics Database(CTD)数据分析有关疾病、细胞类型和药物。[结果]与肾纤维化相关的数据集GSE148420、GSE38117、GSE54441经Venn图共得到83个DEGs。经Cytoscape对STRING工具得到的PPI网络可视化后，筛选出10个与肾纤维化相关的关键基因，分别是F13B、ALDH8A1、A1CF、PAH、KMO、ALDH6A1、SPP2、ACAT1、ABAT、CAT。GO和KEGG富集显示这些基因与氧化还原酶活性，血小板致密颗粒，色氨酸、结氨酸、亮氨酸和异亮氨酸等氨基酸代谢通路有关。利用Enrichr和CTD...     

家族性双侧大结节性肾上腺增生症生物标志物的生信分析

为寻找与家族性双侧大结节性肾上腺皮质增生症发展有关的潜在治疗靶点和生物标志物。从GEO数据库中下载GSE171558数据集,筛选受家族影响的肾上腺结节与正常的肾上腺组织之间的差异表达基因(Differentially expressed genes, DEGs),并进行基因功能富集分析和蛋白质-蛋白质相互作用网络分析。通过Cytoscape v3.9.1软件中的插件cytoHubba筛选出关键基因,进一步经NetworkAnalyst分析TF-miRNA共调控网络和蛋白质-化合物相互作用。共鉴定出336个DEGs,这些基因主要富集在细胞粘附过程、细胞增殖的正调节过程和RNA加工过程等生物过程,并涉及钙信号通路、PI3K-Akt信号通路和cAMP信号通路等。通过cytoHubba插件获得5个hub基因,经验证分析,多功能蛋白聚糖(Versican,VCAN)、双糖链蛋白聚糖(Biglycan,BGN)被认为是家族性双侧大结节性肾上腺皮质增生症的潜在生物标志物。进一步的GSEA分析结果显示,VCAN主要与丁酸代谢、ECM-受体相互作用和类固醇生物合成等有关。BGN主要涉及剪接体、皮质醇的合...     

生物信息学分析筛选结直肠癌靶基因及评估预后价值

为寻找与结直肠癌发展和预后相关的潜在关键基因及信号通路.从美国国立信息中心NCBI的GEO数据库获得结直肠癌基因表达数据集GSE106582,通过PCA对样本进行分组,利用GEO2R进行综合分析,筛选结直肠癌与癌旁对照组的差异表达基因;通过DAVID在线工具对差异表达基因进行GO本体分析和KEGG通路富集分析,初步分析...     

Identification of gene coexpression modules,hub genes,and pathways related to spinal cord injury using integrated bioinformatics methods

Spinal cord injury (SCI) is characterized by dramatic neurons loss and axonal regeneration suppression. The underlying mechanism associated with SCI-induced immune suppression is still unclear. Weighted gene coexpression network analysis (WGCNA) is now widely applied for the identification of the coexpressed modules, hub genes, and pathways associated with clinic traits of diseases. We performed this study to identify hub genes associated with SCI development. Gene Expression Omnibus (GEO) data sets GSE45006 and GSE20907 were downloaded and the significant correlativity and connectivity between them were detected using WGCNA. Three significant consensus modules, including 567 eigengenes, were identified from the master GSE45006 data following the preconditions of approximate scale-free topology for WGCNA. Further bioinformatics analysis showed these eigengenes were involved in inflammatory and immune responses in SCI. Three hub genes Rac2, Itgb2, and Tyrobp and one pathway “natural killer cell-mediated cytotoxicity” were identified following short time-series expression miner, protein-protein interaction network, and functional enrichment analysis. Gradually upregulated expression patterns of Rac2, Itgb2, and Tyrobp genes at 0, 3, 7, and 14 days after SCI were confirmed based on GSE45006 and GSE20907 data set. Finally, we found that Rac2, Itgb2, and Tyrobp genes might take crucial roles in SCI development through the “natural killer cell–mediated cytotoxicity” pathway.     

Weighted gene correlation network analysis identifies RSAD2, HERC5, and CCL8 as prognostic candidates for breast cancer

As the most commonly diagnosed malignant tumor in female population, the prognosis of breast cancer is affected by complex gene interaction networks. In this research weighted gene co-expression network analysis (WGCNA) would be utilized to build a gene co-expression network to identify potential biomarkers for prediction the prognosis of patients with breast cancer. We downloaded GSE25065 from Gene Expression Omnibus database as the test set. GSE25055 and GSE42568 were utilized to validate findings in the research. Seven modules were established in the GSE25065 by utilizing average link hierarchical clustering. Three hub genes, RSAD2, HERC5, and CCL8 were screened out from the significant module (R ² = 0.44), which were considerably interrelated to worse prognosis. Within test dataset GSE25065, RSAD2, and CCL8 were correlated with tumor stage, grade, and lymph node metastases, whereas HERC5 was correlated with lymph node metastases and tumor grade. In the validation dataset GSE25055 and RSAD2 expression was correlated with tumor grade, stage, and size, whereas HERC5 was related to tumor stage and tumor grade, and CCL8 was associated with tumor size and tumor grade. Multivariable survival analysis demonstrated that RSAD2, HERC5, and CCL8 were independent risk factors. In conclusion, the WGCNA analysis conducted in this study screened out novel prognostic biomarkers of breast cancer. Meanwhile, further in vivo and in vitro studies are required to make the clear molecular mechanisms.     

Fifteen hub genes associated with progression and prognosis of clear cell renal cell carcinoma identified by coexpression analysis

Renal cell carcinoma (RCC) is the most common type of renal tumor, and the clear cell renal cell carcinoma (ccRCC) is the most frequent subtype. In this study, our aim is to identify potential biomarkers that could effectively predict the prognosis and progression of ccRCC. First, we used The Cancer Genome Atlas (TCGA) RNA-sequencing (RNA-seq) data of ccRCC to identify 2370 differentially expressed genes (DEGs). Second, the DEGs were used to construct a coexpression network by weighted gene coexpression network analysis (WGCNA). Moreover, we identified the yellow module, which was strongly related to the histologic grade and pathological stage of ccRCC. Then, the functional annotation of the yellow module and single-samples gene-set enrichment analysis of DEGs were performed and mainly enriched in cell cycle. Subsequently, 18 candidate hub genes were screened through WGCNA and protein–protein interaction (PPI) network analysis. After verification of TCGA’s ccRCC data set, Gene Expression Omnibus (GEO) data set (GSE73731) and tissue validation, we finally identified 15 hub genes that can actually predict the progression of ccRCC. In addition, by using survival analysis, we found that patients of ccRCC with high expression of each hub gene were more likely to have poor prognosis than those with low expression. The receiver operating characteristic curve showed that each hub gene could effectively distinguish between localized and advanced ccRCC. In summary, our study indicates that 15 hub genes have great predictive value for the prognosis and progression of ccRCC, and may contribute to the exploration of the pathogenesis of ccRCC.     

Identification of pivotal genes associated with the prognosis of gastric carcinoma through integrated analysis

Purpose: Detecting and diagnosing gastric cancer (GC) during its early period remains greatly difficult. Our analysis was performed to detect core genes correlated with GC and explore their prognostic values.Methods: Microarray datasets from the Gene Expression Omnibus (GEO) ({"type":"entrez-geo","attrs":{"text":"GSE54129","term_id":"54129"}}GSE54129) and The Cancer Genome Atlas (TCGA)-stomach adenocarcinoma (STAD) datasets were applied for common differentially co-expressed genes using differential gene expression analysis and Weighted Gene Co-expression Network Analysis (WGCNA). Functional enrichment analysis and protein–protein interaction (PPI) network analysis of differentially co-expressed genes were performed. We identified hub genes via the CytoHubba plugin. Prognostic values of hub genes were explored. Afterward, Gene Set Enrichment Analysis (GSEA) was used to analyze survival-related hub genes. Finally, the tumor-infiltrating immune cell (TIC) abundance profiles were estimated.Results: Sixty common differentially co-expressed genes were found. Functional enrichment analysis implied that cell–cell junction organization and cell adhesion molecules were primarily enriched. Hub genes were identified using the degree, edge percolated component (EPC), maximal clique centrality (MCC), and maximum neighborhood component (MNC) algorithms, and serpin family E member 1 (SERPINE1) was highly associated with the prognosis of GC patients. Moreover, GSEA demonstrated that extracellular matrix (ECM) receptor interactions and pathways in cancers were correlated with SERPINE1 expression. CIBERSORT analysis of the proportion of TICs suggested that CD8⁺ T cell and T-cell regulation were negatively associated with SERPINE1 expression, showing that SERPINE1 may inhibit the immune-dominant status of the tumor microenvironment (TME) in GC.Conclusions: Our analysis shows that SERPINE1 is closely correlated with the tumorigenesis and progression of GC. Furthermore, SERPINE1 acts as a candidate therapeutic target and prognostic biomarker of GC.     

Identification of differentially expressed genes and biological characteristics of colorectal cancer by integrated bioinformatics analysis

Colorectal cancer (CRC) ranks as one of the most common malignant tumors worldwide. Its mortality rate has remained high in recent years. Therefore, the aim of this study was to identify significant differentially expressed genes (DEGs) involved in its pathogenesis, which may be used as novel biomarkers or potential therapeutic targets for CRC. The gene expression profiles of GSE21510, GSE32323, GSE89076, and GSE113513 were downloaded from the Gene Expression Omnibus (GEO) database. After screening DEGs in each GEO data set, we further used the robust rank aggregation method to identify 494 significant DEGs including 212 upregulated and 282 downregulated genes. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed by DAVID and the KOBAS online database, respectively. These DEGs were shown to be significantly enriched in different cancer-related functions and pathways. Then, the STRING database was used to construct the protein–protein interaction network. The module analysis was performed by the MCODE plug-in of Cytoscape based on the whole network. We finally filtered out seven hub genes by the cytoHubba plug-in, including PPBP, CCL28, CXCL12, INSL5, CXCL3, CXCL10, and CXCL11. The expression validation and survival analysis of these hub genes were analyzed based on The Cancer Genome Atlas database. In conclusion, the robust DEGs associated with the carcinogenesis of CRC were screened through the GEO database, and integrated bioinformatics analysis was conducted. Our study provides reliable molecular biomarkers for screening and diagnosis, prognosis as well as novel therapeutic targets for CRC.     

Hypermethylated and downregulated MEIS2 are involved in stemness properties and oxaliplatin-based chemotherapy resistance of colorectal cancer

The resistance against oxaliplatin (L-OHP) based regimens remains a major obstacle for its efficient usage in treating metastatic colorectal cancer (mCRC). In this study, we performed weighted gene coexpression network analysis (WGCNA) to systematically screen the relevant hub genes for L-OHP resistance using the raw microarray data of 30 consecutive mCRC samples from our earlier study (GSE69657). The results were further confirmed through datasets from Gene Expression Omnibus (GEO). From L-OHP resistance module, nine genes in both the coexpression and protein–protein interaction networks were chosen as hub genes. Among these genes, Meis Homeobox 2 (MEIS2) had the highest correlation with L-OHP resistance (r = −0.443) and was deregulated in L-OHP resistant tissues compared with L-OHP sensitive tissues in both our own dataset and GSE104645 testing dataset. The receiver operating characteristic curve validated that MEIS2 had a good ability in predicting L-OHP response in both our own dataset (area under the curve [AUC] = 0.802) and GSE104645 dataset (AUC = 0.746). Then, the down expression of MEIS2 was observed in CRC tissue compared with normal tissue in 12 GEO-sourced datasets and The Cancer Genome Atlas (TCGA) and was correlated with poor event-free survival. Furthermore, analyzing methylation data from TCGA showed that MEIS2 had increased promoter hypermethylation. In addition, MEIS2 expression was significantly decreased in CRC stem cells compared with nonstem cells in two GEO datasets (GSE14773 and GSE24747). Further methylation analysis from GSE104271 demonstrated that CRC stem cells had higher MEIS2 promoter methylation levels in cg00366722 and cg00610348 sites. Gene set enrichment analysis showed that MEIS2 might be involved in the Wnt/β-catenin pathway. In the overall view, MEIS2 had increased promoter hypermethylation and was downregulated in poor L-OHP response mCRC tissues. MEIS2 might be involved in the Wnt/β-catenin pathway to maintain CRC stemness, which leads to L-OHP resistance.     

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.