Construction of lncRNA-associated ceRNA networks to identify prognostic lncRNA biomarkers for glioblastoma

Long noncoding RNAs (lncRNAs) serve as competitive endogenous RNAs (ceRNAs) that play significant regulatory roles in the pathogenesis of tumors. However, the role of lncRNAs, especially the lncRNA-related ceRNA regulatory network, in glioblastoma (GBM) has not been fully elucidated. The goal of the current study was to construct lncRNA-microRNA-mRNA-related ceRNA networks for further investigation of their mechanism of action in GBM. We downloaded data from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) databases and identified differential lncRNAs, microRNAs (miRNAs), and messenger RNAs (mRNAs) associated with GBM. A ceRNA network was constructed and analyzed to examine the relationship between lncRNAs and patients’ overall survival. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGGs) were used to analyze the related mRNAs to indirectly explain the mechanism of action of lncRNAs. The potential effective drugs for the treatment of GBM were identified using the connectivity map (CMap). After integrated analysis, we obtained a total of 210 differentially expressed lncRNAs, 90 differentially expressed miRNAs, and 2508 differentially expressed mRNAs (DEmRNAs) from the TCGA and GEO databases. Using these differential genes, we constructed a lncRNA-associated ceRNA network. Six lncRNAs in the ceRNA network were associated with the overall survival of patients with GBM. Through KEGG analysis, it was found that the DEmRNAs involved in the network are related to cancer-associated pathways, for instance, mitogen-activated protein kinase and Ras signaling pathways. CMap analysis revealed four small-molecule compounds that could be used as drugs for the treatment of GBM. In this study, a multi-database joint analysis was used to construct a lncRNA-related ceRNA network to help identify the regulatory functions of lncRNAs in the pathogenesis of GBM.     

胶质母细胞瘤预后相关基因的数据挖掘分析

胶质母细胞瘤（glioblastoma, GBM）是恶性程度最高的颅内恶性肿瘤，目前临床上缺乏有效治疗药物，复发率高且预后差，开发新的抗GBM药物是目前临床上亟待解决的问题。为了筛选与GBM预后密切相关的基因，为寻找新的药物靶点提供线索，采用GEO2R工具从GEO数据库中的269个肿瘤组织和61个正常组织中初步筛选出差异表达基因，然后利用Cluster Profiler数据库进行基因功能富集分析，STRING及Cytoscape进一步筛选出37个差异表达基因，采用GEPIA交互分析对这37个基因在GBM肿瘤组织中的表达进行验证。为了进一步探索这些差异表达基因与患者预后的关系，研究中利用GEPIA工具对TCGA数据库中与患者预后相关的数据进行深入挖掘，最终发现PTTG1、RRM2、E2F7与患者中位生存期呈显著性负相关。研究筛选出的与患者预后密切相关的基因不仅可以为评估患者预后提供参考，同时也为开发新的抗GBM药物提供了潜在的靶点。     

Comparative analyses identify molecular signature of MRI-classified SVZ-associated glioblastoma

Glioblastoma (GBM) is a highly aggressive brain cancer with limited therapeutic options. While efforts to identify genes responsible for GBM have revealed mutations and aberrant gene expression associated with distinct types of GBM, patients with GBM are often diagnosed and classified based on MRI features. Therefore, we seek to identify molecular representatives in parallel with MRI classification for group I and group II primary GBM associated with the subventricular zone (SVZ). As group I and II GBM contain stem-like signature, we compared gene expression profiles between these 2 groups of primary GBM and endogenous neural stem progenitor cells to reveal dysregulation of cell cycle, chromatin status, cellular morphogenesis, and signaling pathways in these 2 types of MRI-classified GBM. In the absence of IDH mutation, several genes associated with metabolism are differentially expressed in these subtypes of primary GBM, implicating metabolic reprogramming occurs in tumor microenvironment. Furthermore, histone lysine methyltransferase EZH2 was upregulated while histone lysine demethylases KDM2 and KDM4 were downregulated in both group I and II primary GBM. Lastly, we identified 9 common genes across large data sets of gene expression profiles among MRI-classified group I/II GBM, a large cohort of GBM subtypes from TCGA, and glioma stem cells by unsupervised clustering comparison. These commonly upregulated genes have known functions in cell cycle, centromere assembly, chromosome segregation, and mitotic progression. Our findings highlight altered expression of genes important in chromosome integrity across all GBM, suggesting a common mechanism of disrupted fidelity of chromosome structure in GBM.     

Systematic Genetic Analysis Identifies Cis-eQTL Target Genes Associated with Glioblastoma Patient Survival

Prior expression quantitative trait locus (eQTL) studies have demonstrated heritable variation determining differences in gene expression. The majority of eQTL studies were based on cell lines and normal tissues. We performed cis-eQTL analysis using glioblastoma multiforme (GBM) data sets obtained from The Cancer Genome Atlas (TCGA) to systematically investigate germline variation’s contribution to tumor gene expression levels. We identified 985 significant cis-eQTL associations (FDR<0.05) mapped to 978 SNP loci and 159 unique genes. Approximately 57% of these eQTLs have been previously linked to the gene expression in cell lines and normal tissues; 43% of these share cis associations known to be associated with functional annotations. About 25% of these cis-eQTL associations are also common to those identified in Breast Cancer from a recent study. Further investigation of the relationship between gene expression and patient clinical information identified 13 eQTL genes whose expression level significantly correlates with GBM patient survival (p<0.05). Most of these genes are also differentially expressed in tumor samples and organ-specific controls (p<0.05). Our results demonstrated a significant relationship of germline variation with gene expression levels in GBM. The identification of eQTLs-based expression associated survival might be important to the understanding of genetic contribution to GBM cancer prognosis.     

Four long noncoding RNAs as potential prognostic biomarkers for hepatocellular carcinoma

The study aimed to identify the long noncoding RNAs (lncRNAs) biomarkers for occurrence and prognosis of patients with hepatocellular carcinoma (HCC), and simultaneously to investigate the potential role of lncRNAs in the oncogenesis of HCC. The lncRNAs expression data and the corresponding clinical information of HCC samples were extracted from The Cancer Genome Atlas (TCGA) database. The differentially expressed genes and lncRNAs were identified and the correlation networks were constructed. In this study, we identified 212 differentially expressed lncRNAs and 7,577 differentially expressed genes between liver HCC tumor tissues and normal tissue samples. And then, combining with clinical information, a total of 11 lncRNAs and 162 genes as HCC biomarkers were identified by comprehensive bioinformatics analysis. Further, through coexpress network analysis, we confirmed four lncRNAs (lncRNA_ANKRD10.IT1, lncRNA_CTD.2583A14.8, lncRNA_RP11.404P21.3, and lncRNA_RP11.488L18.10), which can serve as prognostic biomarkers for HCC. The four lncRNAs identified in this study may serve as a potential therapy target for HCC.     

Cancer Association Study of Aminoacyl-tRNA Synthetase Signaling Network in Glioblastoma

Aminoacyl-tRNA synthetases (ARSs) and ARS-interacting multifunctional proteins (AIMPs) exhibit remarkable functional versatility beyond their catalytic activities in protein synthesis. Their non-canonical functions have been pathologically linked to cancers. Here we described our integrative genome-wide analysis of ARSs to show cancer-associated activities in glioblastoma multiforme (GBM), the most aggressive malignant primary brain tumor. We first selected 23 ARS/AIMPs (together referred to as ARSN), 124 cancer-associated druggable target genes (DTGs) and 404 protein-protein interactors (PPIs) of ARSs using NCI's cancer gene index. 254 GBM affymetrix microarray data in The Cancer Genome Atlas (TCGA) were used to identify the probe sets whose expression were most strongly correlated with survival (Kaplan-Meier plots versus survival times, log-rank t-test <0.05). The analysis identified 122 probe sets as survival signatures, including 5 of ARSN (VARS, QARS, CARS, NARS, FARS), and 115 of DTGs and PPIs (PARD3, RXRB, ATP5C1, HSP90AA1, CD44, THRA, TRAF2, KRT10, MED12, etc). Of note, 61 survival-related probes were differentially expressed in three different prognosis subgroups in GBM patients and showed correlation with established prognosis markers such as age and phenotypic molecular signatures. CARS and FARS also showed significantly higher association with different molecular networks in GBM patients. Taken together, our findings demonstrate evidence for an ARSN biology-dominant contribution in the biology of GBM.     

基于TCGA数据库分析甲状腺癌基因表达谱

为分析甲状腺癌基因表达谱,筛选疾病相关的基因标志物。基于肿瘤基因组图谱(TCGA)数据库中的甲状腺癌基因表达数据,运用R/Bioconductor统计平台进行数据处理与统计学分析。分别应用edgeR算法和limma算法选取肿瘤组织与对照组间倍数改变 > 2,P< 0.05的基因作为差异基因;进一步运用Medcalc统计软件进行受试者工作特征曲线(ROC)分析,鉴定出有诊断标志物潜在应用价值的基因标志物。通过两种运算方法筛选出甲状腺癌组织中存在着1 945个差异基因(上调基因1 033个,下调基因912个);根据差异倍数进一步鉴定出11个基因在肿瘤组织中表达上调,且对鉴别肿瘤组与对照组有较好的应用价值。本研究分析了TCGA中的甲状腺癌表达谱数据,鉴定出了与疾病诊断显著相关的差异表达基因,能够为探索疾病发生发展机制及寻找新型分子标志物提供依据。     

Functional Profiling of Precursor MicroRNAs Identifies MicroRNAs Essential for Glioma Proliferation

Cancer initiation and progression involve microRNAs that can function like tumor suppressors and oncogenes. The functional significance of most miRNAs is currently unknown. To determine systematically which microRNAs are essential for glioma growth, we screened a precursor microRNA library in three human glioblastoma and one astroglial cell line model systems. The most prominent and consistent cell proliferation–reducing hits were validated in secondary screening with an additional apoptosis endpoint. The functional screening data were integrated in the miRNA expression data to find underexpressed true functional tumor suppressor miRNAs. In addition, we used miRNA-target gene predictions and combined siRNA functional screening data to find the most probable miRNA-target gene pairs with a similar functional effect on proliferation. Nine novel functional miRNAs (hsa-miR-129, -136, -145, -155, -181b, -342-5p, -342-3p, -376a/b) in GBM cell lines were validated for their importance in glioma cell growth, and similar effects for six target genes (ROCK1, RHOA, MET, CSF1R, EIF2AK1, FGF7) of these miRNAs were shown functionally. The clinical significance of the functional hits was validated in miRNA expression data from the TCGA glioblastoma multiforme (GBM) tumor cohort. Five tumor suppressor miRNAs (hsa-miR-136, -145, -342, -129, -376a) showed significant underexpression in clinical GBM tumor samples from the TCGA GBM cohort further supporting the role of these miRNAs in vivo. Most importantly, higher hsa-miR-145 expression in GBM tumors yielded significantly better survival (p<0.005) in a subset of patients thus validating it as a genuine tumor suppressor miRNA. This systematic functional profiling provides important new knowledge about functionally relevant miRNAs in GBM biology and may offer new targets for treating glioma.     

Localization of heat shock protein 20 in swine carotid artery

Background  

The gene expression pattern in tumor cells differs from that in corresponding normal cells. In order to identify differentially expressed genes in colorectal tumors and normal colorectal epithelium, a differential display experiment was used to compare RNA expression in normal and tumor tissue samples.     

Expression signature of IFN/STAT1 signaling genes predicts poor survival outcome in glioblastoma multiforme in a subtype-specific manner

Previous reports have implicated an induction of genes in IFN/STAT1 (Interferon/STAT1) signaling in radiation resistant and prosurvival tumor phenotypes in a number of cancer cell lines, and we have hypothesized that upregulation of these genes may be predictive of poor survival outcome and/or treatment response in Glioblastoma Multiforme (GBM) patients. We have developed a list of 8 genes related to IFN/STAT1 that we hypothesize to be predictive of poor survival in GBM patients. Our working hypothesis that over-expression of this gene signature predicts poor survival outcome in GBM patients was confirmed, and in addition, it was demonstrated that the survival model was highly subtype-dependent, with strong dependence in the Proneural subtype and no detected dependence in the Classical and Mesenchymal subtypes. We developed a specific multi-gene survival model for the Proneural subtype in the TCGA (the Cancer Genome Atlas) discovery set which we have validated in the TCGA validation set. In addition, we have performed network analysis in the form of Bayesian Network discovery and Ingenuity Pathway Analysis to further dissect the underlying biology of this gene signature in the etiology of GBM. We theorize that the strong predictive value of the IFN/STAT1 gene signature in the Proneural subtype may be due to chemotherapy and/or radiation resistance induced through prolonged constitutive signaling of these genes during the course of the illness. The results of this study have implications both for better prediction models for survival outcome in GBM and for improved understanding of the underlying subtype-specific molecular mechanisms for GBM tumor progression and treatment response.     

3′-End Sequencing for Expression Quantification (3SEQ) from Archival Tumor Samples

Gene expression microarrays are the most widely used technique for genome-wide expression profiling. However, microarrays do not perform well on formalin fixed paraffin embedded tissue (FFPET). Consequently, microarrays cannot be effectively utilized to perform gene expression profiling on the vast majority of archival tumor samples. To address this limitation of gene expression microarrays, we designed a novel procedure (3′-end sequencing for expression quantification (3SEQ)) for gene expression profiling from FFPET using next-generation sequencing. We performed gene expression profiling by 3SEQ and microarray on both frozen tissue and FFPET from two soft tissue tumors (desmoid type fibromatosis (DTF) and solitary fibrous tumor (SFT)) (total n = 23 samples, which were each profiled by at least one of the four platform-tissue preparation combinations). Analysis of 3SEQ data revealed many genes differentially expressed between the tumor types (FDR<0.01) on both the frozen tissue (∼9.6K genes) and FFPET (∼8.1K genes). Analysis of microarray data from frozen tissue revealed fewer differentially expressed genes (∼4.64K), and analysis of microarray data on FFPET revealed very few (69) differentially expressed genes. Functional gene set analysis of 3SEQ data from both frozen tissue and FFPET identified biological pathways known to be important in DTF and SFT pathogenesis and suggested several additional candidate oncogenic pathways in these tumors. These findings demonstrate that 3SEQ is an effective technique for gene expression profiling from archival tumor samples and may facilitate significant advances in translational cancer research.