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。     

Affymetrix基因表达谱芯片在新疆维吾尔族与汉族胰腺癌组织样本间差异表达基因筛选中的运用

目的:运用基因表达谱芯片筛选并分析新疆维吾尔族与汉族胰腺癌组织样本间的差异表达基因。方法:收集我院2014年1月至2016年6月间行手术切除的维吾尔族与汉族胰腺导管细胞癌组织并提取总RNA,选取经Nanodrop 2000与Agilent 2100仪器质检合格的样本总RNA采用Affymetrix基因表达谱芯片筛选出差异表达基因并绘制统计图,运用基因本体(GO)分析及信号通路(Pathway)分析对这些差异表达基因的生物信息进行汇总分析。结果:通过基因表达谱芯片分析,新疆维吾尔族与汉族胰腺癌组织样本间共检测到1063个基因存在差异表达,在维吾尔族胰腺癌标本中显著上调表达的基因共281个,差异表达倍数最高的为IGLV1-44基因(差异倍数:9.99)下调表达的基因共782个,差异表达倍数最高的为CPB1基因(差异倍数:33.76);在Gene Ontology数据库中共检索到815个上述差异表达基因具有明确的GO分类,差异表达倍数最高的为CPB1基因(差异倍数:33.76);Pathway分析中共检测到30条信号通路包含有上述差异表达基因,共涉及196个基因,其中以FAK信号通路差异表达基因富集程度最高,差异表达倍数最高的基因为COL11A1基因(差异倍数:5.02)。结论:基因表达谱芯片分析结果显示,在新疆维吾尔族与汉族胰腺癌组织样本间存在大量的差异表达基因,这些基因与胰腺癌的增殖分化、侵袭转移及多药耐药等特性密切相关,且参与了多条生物体内重要信号转导通路的调控。     

利用表达谱基因芯片筛选溃疡性结肠炎差异表达基因

目的:利用人类全基因组表达谱芯片技术,分析溃疡性结肠炎患者和健康者基因表达谱差异,筛选出溃疡性结肠炎相关基因。方法:采用Trizol法提取8例溃疡性结肠炎患者和8例健康对照者结肠粘膜组织总RNA并纯化,逆转录合成c DNA,利用荧光染料Cy3标记aa UTP,转录合成标记的c RNA,并与Agilent人类全基因组表达谱芯片杂交,扫描荧光信号图像,对芯片原始数据进行归一化处理,利用倍数差异和t检验计算筛选出相关差异表达基因,采用DAVID在线分析系统进行基因的功能注释和关联分析,明确差异基因的生物学功能,并对部分差异表达基因进行实时荧光定量PCR验证。结果:筛查出溃疡性结肠炎结肠粘膜组织差异表达基因4132个,其中上调基因2004个,下调基因2128个。选取6条差异表达基因进行PCR验证,结果有3条基因表达上调,3条基因表达下调,表达趋势与芯片结果一致。结论:溃疡性结肠炎患者与健康对照者基因表达存在明显差异,分析这些差异表达基因有助于我们探索溃疡性结肠炎的发病机制,为疾病的治疗提供理论依据。     

生物信息学方法筛选胶质母细胞瘤的核心基因

多形性胶质母细胞瘤(GBM)是成人最常见的恶性神经上皮肿瘤,关于其诊断和治疗的靶点研究一直是困扰研究者的难题。采用生物信息学的方法对GBM的基因表达信息进行分析,从TCGA中共获取169例GBM样本,正常脑组织样本5例,共17 847个基因。筛选出差异基因3 184个,利用Gene Ontology (GO)富集分析和Kyoto Encyclopedia of Genes and Genomes (KEGG)对差异基因的功能进行富集分析。利用STRING工具和cytoscape构建蛋白互作网络,连通性得分最高的被筛选为核心基因。联合ONCOMINE平台对核心基因进行表达分析。采用Kaplan-Meier法绘制核心基因生存曲线,了解核心基因对GBM患者生存时间和生存几率的相关性。共筛选出上调差异基因1 582个,下调差异基因1 601个。BottleNeck算法中连通性得分前十被选为核心基因,联合ONCOMINE分析,KCNAB2在GBM中低表达。KCNAB2的过表达预测了GBM患者更短的生存时间。相较于正常脑组织,KCNAB2在GBM中低表达。而当KCNAB2过表达时,GBM患者的生存时间明显缩短。但是,核心基因KCNAB2在GBM患者生存时间中的影响机制和存在价值仍需要进一步的研究去验证。     

幽门螺杆菌感染与胃癌相关基因的筛选及预后分析

目的通过分析幽门螺杆菌感染胃黏膜组织和胃癌细胞系后的差异基因变化,并在癌症基因组图谱(The Cancer Genome Atlas,TCGA)数据库和肿瘤基因芯片(Oncomine)数据库进行验证,探究幽门螺杆菌导致胃癌发生、发展的分子机制。方法分析基因表达汇编(Gene Expression Omnibus,GEO)数据库幽门螺杆菌感染相关芯片集GSE5081与GSE70394,绘制维恩图查找幽门螺杆菌感染后共同上调的差异基因。对共同上调的差异基因进行功能富集分析。通过TCGA和Oncomine数据库验证差异基因在胃癌中的表达。利用Kaplan-Meier Plotter数据库和GEPIA数据库分析差异基因表达高低与胃癌患者预后是否存在相关性。结果通过差异基因筛选和维恩分析,两个芯片集共有21个共同上调差异基因。GO分析发现共同上调差异基因主要富集在对细菌来源分子的反应、趋化因子CXCR受体结合、中性粒细胞趋化作用等相关的基因功能上;KEGG通路主要富集在癌症通路、TNF信号通路、趋化因子信号通路等。STRING以及PPI数据库分析发现21个基因中PRDM1、IL10、NRP1、BIRC3、GNG13、CXCL1、CXCL2、CXCL3、CXCL8基因存在有网络关系,属于关键枢纽基因。通过TCGA和Oncomine数据库筛选及验证,发现在胃癌组织中NRP1、CXCL1、CXCL8基因明显上调,结果差异有统计学意义(TCGA数据库中,三者P值均小于0.05,Oncomine数据库中,NRP1:t=4.607,P0.01;CXCL1:t=5.854,P0.01;CXCL8:t=5.316,P0.01)。在Kaplan-Meier Plotter数据库(210615-at芯片:P0.01;210510-s-at芯片:P0.01;212298-at芯片:P0.01)以及GEPIA数据库(P0.01)两个数据库中,NRP1的高表达均与胃癌的预后负相关。结论不同的数据库均显示NRP1、CXCL1、CXCL8三个基因与幽门螺杆菌感染相关,同时在胃癌中高表达,并且NRP1的高表达与胃癌的不良预后相关,这些结果为进一步探究幽门螺杆菌导致胃癌发生、发展的分子机制提供了重要基础。     

人肝细胞癌预后不良相关基因的生物信息学分析及其临床意义

目的:筛选肝细胞癌(HCC)预后不良相关基因,并探讨其临床意义。方法:在基因表达综合数据库(GEO)中获取符合分析条件的肝细胞癌全基因组表达谱数据并分析得到差异表达基因(DEGs),再运用生物学信息注释及可视化数据库 (DAVID) 和蛋白相互作用数据库 (String) 分别进行功能富集分析和蛋白质互作用网络的构建。利用癌症基因组图谱数据库(TCGA)和Cox比例风险回归模型对相关差异基因进行预后分析。结果:找到一个符合条件的人类HCC数据库 (GSE84402),共筛选出1141个差异表达基因(DEGs),其中上调基因720个,下调基因421个。基因功能富集分析和蛋白质互作用分析结果显示CDK1、CDC6、CCNA2、CHEK1、CENPE 、PIK3R1、RACGAP1、BIRC5、KIF11和CYP2B6为HCC预后的关键基因。TCGA数据库和Cox回归模型分析显示CDC6、PIK3R1、RACGAP1和KIF11的表达升高,CENPE的表达降低与HCC预后不良密切相关。结论:CDC6、CENPE、PIK3R1、RACGAP1和KIF11可能和HCC的预后不良相关,可作为未来HCC预后研究的参考标志物。     

关于食管癌亚型食管基底细胞样鳞癌的基因芯片数据分析

分析食管基底细胞样鳞癌(basaloid squamous cell carcinoma of the esophagus,BSCCE)的组织样本基因表达情况,探讨其病理学过程和肿瘤免疫逃逸机制,同时对BSCCE独特的肿瘤微环境及潜在的临床指标在对其远期预后的病理基础进行讨论,以期为临床工作中更好的治疗BSCCE提供一定的理论基础。利用临床术中获取的组织样本及其基因芯片数据,进一步获取BSCCE组织样本与同体正常组织样本的基因差异表达情况(log|FC|≥2,p0.05),其中BSCCE组织相对于正常组织的上调表达差异表达基因共489个,下调差异表达基因922个,利用DAVID在线分析系统对差异表达基因进行京都基因与基因组百科全书(kyoto encyclopedia of genes and genomes,KEGG)信号通路分析获取对BSCCE组织病理情况相关的分析结果,进一步利用STRING-OL工具对差异表达基因的可信度(可信度≥0.4)进行筛选,获取差异表达基因的蛋白质相互作用分析(protein-protein interaction,PPI)结果,进一步利用Cytoscape软件对筛选后的PPI结果制作相应的基因相互作用网状图,并进一步筛选核心表达的差异基因。在上调差异基因的KEGG分析中获得了88个结果,如p53signaling pathway、Cytokine-cytokine receptor interaction pathway、Toll-like receptor signaling pathway等,在下调的差异基因KEGG分析中获得了125个结果,如Drug metabolism-cytochrome P450 pathway、MAPK signaling pathway、Calcium signaling pathway、Cytokine-cytokine receptor interaction pathway等,这些相关的信号通路对进一步理解BSCCE的肿瘤微环境变化及肿瘤病理相关机制有重要意义,通过PPI分析,我们筛选出了一些与肿瘤远期生存率(overall survival,OS)相关的基因:CDC6、CDC20、TOP2A、NEK2、CDC25A等,这为我们进一步研究BSCCE及其肿瘤病理标志物和远期生存的关系提供了理论基础。     

细胞凋亡和代谢基因在转移倾向结肠癌中的研究

目的:应用基因微阵列技术初步筛选与不同转移倾向结肠癌相关的细胞凋亡和代谢相关基因,研究转移相关基因功能.方法:取结肠癌肝转移和无转移结肠癌组织,采用人全基因组表达谱芯片获得两组织的基因表达谱,分析比较两者之间细胞凋亡和代谢基因的差异表达情况;利用基因数据库检索结肠癌相关基因,分析基因功能.结果:应用含有16450个克隆(其中3869个未知)的cDNA微阵列分析发现,细胞凋亡或肿瘤相关基因中,2倍以上(Ratio值小于0.5或大于2.0)差异基因共216个,上调基因85个,下调基因129个.表达差异5倍以上(Ratio值小于0.2或大于5.0)共32个,上调基因10个,下调基因22个.在细胞代谢相关基因中,2倍以上(Ratio值小于0.5或大于2.O)差异基因共205个,上调基因86个,下调基因119个.表达差异5倍以上(Ratio值小于0.2或大于5.0)共15个,上调基因10个,下调基因5个.利用基因数据库检索分析发现5个基因与结肠癌转移关系密切.结论:结肠癌的发生和转移是多基因参与的,本实验应用基因微阵列技术发现细胞凋亡和代谢相关基因中发现5个基因与结肠癌转移关系密切.     

大肠杆菌F18菌株敏感和抵抗基因型仔猪十二指肠基因表达谱差异分析

文章运用Agilent 双标记表达谱芯片, 基于已建立的苏太猪大肠杆菌F18菌株敏感性和抗性型全同胞配对个体, 分析十二指肠组织基因表达谱差异, 旨在筛选导致仔猪断奶后腹泻和水肿病发生的大肠杆菌F18菌株受体相关基因, 探讨造成大肠杆菌病抗性和敏感性资源家系抗性差异的分子生物学机理。研究结果显示, 以Fold change绝对值大于2倍进行筛选, 在敏感型(GG基因型)对抗性型(AA基因型)配对组中, 差异基因共13个, 其中上调6个, 下调7个, 在以敏感型(AG基因型)对抗性型(AA基因型)配对组中, 共筛选出差异基因6个, 其中上调4个, 下调2个。经GO分析发现差异基因的生物学过程主要涉及免疫应答、胞外区修饰(如糖基化)、细胞黏附、信号转导等。通路发现大肠杆菌F18菌株抵抗性和敏感性差异基因主要涉及糖脂合成代谢以及炎症免疫相关通路, 经芯片筛选出的相关基因的功能还需进一步的研究验证。     

Five candidate biomarkers associated with the diagnosis and prognosis of cervical cancer

Purpose: Cervical cancer (CC) is one of the most general gynecological malignancies and is associated with high morbidity and mortality. We aimed to select candidate genes related to the diagnosis and prognosis of CC.Methods: The mRNA expression profile datasets were downloaded. We also downloaded RNA-sequencing gene expression data and related clinical materials from TCGA, which included 307 CC samples and 3 normal samples. Differentially expressed genes (DEGs) were obtained by R software. GO function analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of DEGs were performed in the DAVID dataset. Using machine learning, the optimal diagnostic mRNA biomarkers for CC were identified. We used qRT-PCR and Human Protein Atlas (HPA) database to exhibit the differences in gene and protein levels of candidate genes.Results: A total of 313 DEGs were screened from the microarray expression profile datasets. DNA methyltransferase 1 (DNMT1), Chromatin Assembly Factor 1, subunit B (CHAF1B), Chromatin Assembly Factor 1, subunit A (CHAF1A), MCM2, CDKN2A were identified as optimal diagnostic mRNA biomarkers for CC. Additionally, the GEPIA database showed that the DNMT1, CHAF1B, CHAF1A, MCM2 and CDKN2A were associated with the poor survival of CC patients. HPA database and qRT-PCR confirmed that these genes were highly expressed in CC tissues.Conclusion: The present study identified five DEmRNAs, including DNMT1, CHAF1B, CHAF1A, MCM2 and Kinetochore-related protein 1 (KNTC1), as potential diagnostic and prognostic biomarkers of CC.     

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。     

Gene expression profiles and pathway enrichment analysis to identification of differentially expressed gene and signaling pathways in epithelial ovarian cancer based on high-throughput RNA-seq data

Epithelial ovarian cancer (EOC) can be considered as a stressful and challenging disease among all women in the world, which has been associated with a poor prognosis and its molecular pathogenesis has remained unclear. In recent years, RNA Sequencing (RNA-seq) has become a functional and amazing technology for profiling gene expression. In the present study, RNA-seq raw data from Sequence Read Archive (SRA) of six tumor and normal ovarian sample was extracted, and then analysis and statistical interpretation was done with Linux and R Packages from the open-source Bioconductor. Gene Ontology (GO) term enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were applied for the identification of key genes and pathways involved in EOC. We identified 1091 Differential Expression Genes (DEGs) which have been reported in various studies of ovarian cancer as well as other types of cancer. Among them, 333 genes were up-regulated and 273 genes were down-regulated. In addition, Differentially Expressed Genes (DEGs) including RPL41, ALDH3A2, ERBB2, MIEN1, RBM25, ATF4, UPF2, DDIT3, HOXB8 and IL17D as well as Ribosome and Glycolysis/Gluconeogenesis pathway have had the potentiality to be used as targets for EOC diagnosis and treatment. In this study, unlike that of any other studies on various cancers, ALDH3A2 was most down-regulated gene in most KEGG pathways, and ATF4 was most up-regulated gene in leucine zipper domain binding term. In the other hand, RPL41 as a regulatory of cellular ATF4 level was up-regulated in many term and pathways and augmentation of ATF4 could justify the increase of RPL41 in the EOC. Pivotal pathways and significant genes, which were identified in the present study, can be used for adaptation of different EOC study. However, further molecular biological experiments and computational processes are required to confirm the function of the identified genes associated with EOC.     

Identification of a six-gene prognostic signature for oral squamous cell carcinoma

Oral squamous cell carcinoma (OSCC) is one of the most common types of malignancies worldwide, and its morbidity and mortality have increased in the near term. Consequently, the purpose of the present study was to identify the notable differentially expressed genes (DEGs) involved in their pathogenesis to obtain new biomarkers or potential therapeutic targets for OSCC. The gene expression profiles of the microarray datasets GSE85195, GSE23558, and GSE10121 were obtained from the Gene Expression Omnibus (GEO) database. After screening the DEGs in each GEO dataset, 249 DEGs in OSCC tissues were obtained. Kyoto Encyclopedia of Genes and Genomes and Gene Ontology pathway enrichment analysis was employed to explore the biological functions and pathways of the above DEGs. A protein–protein interaction network was constructed to obtain a central gene. The corresponding total survival information was analyzed in patients with oral cancer from The Cancer Genome Atlas (TCGA). A total of six candidate genes (CXCL10, OAS2, IFIT1, CCL5, LRRK2, and PLAUR) closely related to the survival rate of patients with oral cancer were identified, and expression verification and overall survival analysis of six genes were performed based on TCGA database. Time-dependent receiver operating characteristic curve analysis yields predictive accuracy of the patient's overall survival. At the same time, the six genes were further verified by quantitative real-time polymerase chain reaction using samples obtained from the patients recruited to the present study. In conclusion, the present study identified the prognostic signature of six genes in OSCC for the first time via comprehensive bioinformatics analysis, which could become potential prognostic markers for OCSS and may provide potential therapeutic targets for tumors.     

Bioinformatic analyses of microRNA-targeted genes and microarray-identified genes correlated with Barrett's esophagus

Barrett's esophagus (BE) is defined as a metaplasia condition in the distal esophagus, in which the native squamous epithelium lining is replaced by a columnar epithelium with or without intestinal metaplasia. It is commonly accepted that BE is a precancerous lesion for esophageal adenocarcinoma. The aim of this study was to investigate the aberrant microRNAs (miRNAs) and differentially expressed genes (DEGs) associated with BE based on online microarray datasets. One miRNA and five gene expression profiling datasets were retrieved from the Gene Expression Omnibus Database. Aberrant microRNAs and DEGs were obtained using R/Bioconductor statistical analysis language and software. 23 dysregulated miRNAs and 632 DEGs demonstrating consistent expression tendencies in the five gene microarrays were identified in BE. Moreover, 1962 target genes of aberrant miRNAs were predicted using three bioinformatic tools, namely TargetScan, RNA22-HSA and miRDB. Ultimately, 93 target DEGs were obtained, after which functional annotation was performed on DAVID Bioinformatics Resources. Among Gene Ontology (GO) biological processes, digestive tract development and epithelial cell differentiation have demonstrated significant associations with BE pathogenesis. In addition, analysis of the KEGG pathways has revealed associations with cancer. To enable further study, one miRNA-target DEGs regulatory network was constructed using Cytoscape. 6 target DEGs demonstrated higher-degree distributions in the network, and ROC analysis indicated that FNDC3B may be the best potential biomarker for BE diagnosis. The data presented herein may provide new perspectives for exploring BE pathogenesis and may offer hits with regard to potential biomarkers in BE diagnosis, prediction and therapeutic evaluation.     

A six-gene-based signature for breast cancer radiotherapy sensitivity estimation

Breast cancer (BRCA) represents the most common malignancy among women worldwide with high mortality. Radiotherapy is a prevalent therapeutic for BRCA that with heterogeneous effectiveness among patients. Here, we proposed to develop a gene expression-based signature for BRCA radiotherapy sensitivity estimation. Gene expression profiles of BRCA samples from the Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) were obtained and used as training and independent testing dataset, respectively. Differential expression genes (DEGs) in BRCA samples compared with their paracancerous samples in the training set were identified by using the edgeR Bioconductor package. Univariate Cox regression analysis and LASSO Cox regression method were applied to screen optimal genes for constructing a radiotherapy sensitivity estimation signature. Nomogram combining independent prognostic factors was used to predict 1-, 3-, and 5-year OS of radiation-treated BRCA patients. Relative proportions of tumor infiltrating immune cells (TIICs) calculated by CIBERSORT and mRNA levels of key immune checkpoint receptors was adopted to explore the relation between the signature and tumor immune response. As a result, 603 DEGs were obtained in BRCA tumor samples, six of which were retained and used to construct the radiotherapy sensitivity prediction model. The signature was proved to be robust in both training and testing sets. In addition, the signature was closely related to the immune microenvironment of BRCA in the context of TIICs and immune checkpoint receptors’ mRNA levels. In conclusion, the present study obtained a radiotherapy sensitivity estimation signature for BRCA, which should shed new light in clinical and experimental research.     

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.     

Prognostic value of tumour microenvironment-related genes by TCGA database in rectal cancer

Rectal cancer is a common malignant tumour and the progression is highly affected by the tumour microenvironment (TME). This study intended to assess the relationship between TME and prognosis, and explore prognostic genes of rectal cancer. The gene expression profile of rectal cancer was obtained from TCGA and immune/stromal scores were calculated by Estimation of Stromal and Immune cells in Malignant Tumors using Expression data (ESTIMATE) algorithm. The correlation between immune/stromal scores and survival time as well as clinical characteristics were evaluated. Differentially expressed genes (DEGs) were identified according to the stromal/immune scores, and the functional enrichment analyses were conducted to explore functions and pathways of DEGs. The survival analyses were conducted to clarify the DEGs with prognostic value, and the protein-protein interaction (PPI) network was performed to explore the interrelation of prognostic DEGs. Finally, we validated prognostic DEGs using data from the Gene Expression Omnibus (GEO) database by PrognoScan, and we verified these genes at the protein levels using the Human Protein Atlas (HPA) databases. We downloaded gene expression profiles of 83 rectal cancer patients from The Cancer Genome Atlas (TCGA) database. The Kaplan-Meier plot demonstrated that low-immune score was associated with worse clinical outcome (P = .034), metastasis (M1 vs. M0, P = .031) and lymphatic invasion (+ vs. -, P < .001). A total of 540 genes were screened as DEGs with 539 up-regulated genes and 1 down-regulated gene. In addition, 60 DEGs were identified associated with overall survival. Functional enrichment analyses and PPI networks showed that the DEGs are mainly participated in immune process, and cytokine-cytokine receptor interaction. Finally, 19 prognostic genes were verified by GSE17536 and GSE17537 from GEO, and five genes (ADAM23, ARHGAP20, ICOS, IRF4, MMRN1) were significantly different in tumour tissues compared with normal tissues at the protein level. In summary, our study demonstrated the associations between TME and prognosis as well as clinical characteristics of rectal cancer. Moreover, we explored and verified microenvironment-related genes, which may be the potential key prognostic genes of rectal cancer. Further clinical samples and functional studies are needed to validate this finding.