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Identification of key candidate genes and pathways in glioblastoma by integrated bioinformatical analysis.

ABSTRACT: Glioblastoma (GBM), characterized by high morbidity and mortality, is one of the most common lethal diseases worldwide. To identify the molecular mechanisms that contribute to the development of GBM, three cohort profile datasets (GSE50161, GSE90598 and GSE104291) were integrated and thoroughly analyzed; these datasets included 57 GBM cases and 22 cases of normal brain tissue. The current study identified differentially expressed genes (DEGs), and analyzed potential candidate genes and pathways. Additionally, a DEGs-associated protein-protein interaction (PPI) network was established for further investigation. Then, the hub genes associated with prognosis were identified using a Kaplan-Meier analysis based on The Cancer Genome Atlas database. Firstly, the current study identified 378 consistent DEGs (240 upregulated and 138 downregulated). Secondly, a cluster analysis of the DEGs was performed based on functions of the DEGs and signaling pathways were analyzed using the enrichment analysis tool on DAVID. Thirdly, 245 DEGs were identified using PPI network analysis. Among them, two co-expression modules comprising of 30 and 27 genes, respectively, and 35 hub genes were identified using Cytoscape MCODE. Finally, Kaplan-Meier analysis of the hub genes revealed that the increased expression of calcium-binding protein 1 (CABP1) was negatively associated with relapse-free survival. To summarize, all enriched Gene Ontology terms and Kyoto Encyclopedia of Genes and Genomes pathways may participate in mechanisms underlying GBM occurrence and progression, however further studies are required. CABP1 may be a key gene associated with the biological process of GBM development and may be involved in a crucial mechanism of GBM progression.

SUBMITTER: Li L

PROVIDER: S-EPMC6777220 | biostudies-literature | 2019 Nov

REPOSITORIES: biostudies-literature

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Identification of key candidate genes and pathways in glioblastoma by integrated bioinformatical analysis.

Li Lei L Liu Xiaohui X Ma Xiaoye X Deng Xianyu X Ji Tao T Hu Pingping P Wan Ronghao R Qiu Huijia H Cui Daming D Gao Liang L

Experimental and therapeutic medicine 20190905 5

Glioblastoma (GBM), characterized by high morbidity and mortality, is one of the most common lethal diseases worldwide. To identify the molecular mechanisms that contribute to the development of GBM, three cohort profile datasets (GSE50161, GSE90598 and GSE104291) were integrated and thoroughly analyzed; these datasets included 57 GBM cases and 22 cases of normal brain tissue. The current study identified differentially expressed genes (DEGs), and analyzed potential candidate genes and pathways. ...[more]

PMID: 31602219

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Project description:BackgroundAlzheimer's disease (AD) is known to be caused by multiple factors, meanwhile the pathogenic mechanism and development of AD associate closely with genetic factors. Existing understanding of the molecular mechanisms underlying AD remains incomplete.MethodsGene expression data (GSE48350) derived from post-modern brain was extracted from the Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) were derived from hippocampus and entorhinal cortex regions between AD patients and healthy controls and detected via Morpheus. Functional enrichment analyses, including Gene Ontology (GO) and pathway analyses of DEGs, were performed via Cytoscape and followed by the construction of protein-protein interaction (PPI) network. Hub proteins were screened using the criteria: nodes degree?10 (for hippocampus tissues) and???8 (for entorhinal cortex tissues). Molecular Complex Detection (MCODE) was used to filtrate the important clusters. University of California Santa Cruz (UCSC) and the database of RNA-binding protein specificities (RBPDB) were employed to identify the RNA-binding proteins of the long non-coding RNA (lncRNA).Results251 & 74 genes were identified as DEGs, which consisted of 56 & 16 up-regulated genes and 195 & 58 down-regulated genes in hippocampus and entorhinal cortex, respectively. Biological analyses demonstrated that the biological processes and pathways related to memory, transmembrane transport, synaptic transmission, neuron survival, drug metabolism, ion homeostasis and signal transduction were enriched in these genes. 11 genes were identified as hub genes in hippocampus and entorhinal cortex, and 3 hub genes were identified as the novel candidates involved in the pathology of AD. Furthermore, 3 lncRNAs were filtrated, whose binding proteins were closely associated with AD.ConclusionsThrough GO enrichment analyses, pathway analyses and PPI analyses, we showed a comprehensive interpretation of the pathogenesis of AD at a systematic biology level, and 3 novel candidate genes and 3 lncRNAs were identified as novel and potential candidates participating in the pathology of AD. The results of this study could supply integrated insights for understanding the pathogenic mechanism underlying AD and potential novel therapeutic targets.

Project description:: Preeclampsia is a pregnancy-specific disorder characterized by the presence of hypertension with the onset of either proteinuria, maternal organ or uteroplacental dysfunction. Preeclampsia is one of the leading causes of maternal and fetal mortality and morbidity worldwide. However, the etiopathologies of preeclampsia are not fully understood. Many studies have indicated that genes are differentially expressed between normal and in the disease state. Hence, this study systematically searched the literature on human gene expression that was differentially expressed in preeclampsia. An electronic search was performed through 2019 through PubMed, Scopus, Ovid-Medline, and Gene Expression Omnibus where the following MeSH (Medical Subject Heading) terms were used and they had been specified as the primary focus of the articles: Gene, placenta, preeclampsia, and pregnancy in the title or abstract. We also found additional MeSH terms through Cochrane Library: Transcript, sequencing, and profiling. From 687 studies retrieved from the search, only original publications that had performed high throughput sequencing of human placental tissues that reported on differentially expressed genes in pregnancies with preeclampsia were included. Two reviewers independently scrutinized the titles and abstracts before examining the eligibility of studies that met the inclusion criteria. For each study, study design, sample size, sampling type, and method for gene analysis and gene were identified. The genes listed were further analyzed with the DAVID, STRING and Cytoscape MCODE. Three original research articles involving preeclampsia comprising the datasets in gene expression were included. By combining three studies together, 250 differentially expressed genes were produced at a significance setting of p < 0.05. We identified candidate genes: LEP, NRIP1, SASH1, and ZADHHC8P1. Through GO analysis, we found extracellular matrix organization as the highly significant enriched ontology in a group of upregulated genes and immune process in downregulated genes. Studies on a genetic level have the potential to provide new insights into the regulation and to widen the basis for identification of changes in the mechanism of preeclampsia. Integrated bioinformatics could identify differentially expressed genes which could be candidate genes and potential pathways in preeclampsia that may improve our understanding of the cause and underlying molecular mechanisms that could be used as potential biomarkers for risk stratification and treatment.

Project description:PurposesCervical cancer (CC) is one of the highest frequently occurred malignant gynecological tumors with high rates of morbidity and mortality. Here, we aimed to identify significant genes associated with poor outcome. Materials and methods. Differentially expressed genes (DEGs) between CC tissues and normal cervical tissues were picked out by GEO2R tool and Venn diagram software. Database for Annotation, Visualization and Integrated Discovery (DAVID) was performed to analyze gene ontology (GO) and Kyoto Encyclopedia of Gene and Genome (KEGG) pathway. The protein-protein interactions (PPIs) of these DEGs were visualized by Cytoscape with Search Tool for the Retrieval of Interacting Genes (STRING). Afterwards, Kaplan-Meier analysis was applied to analyze the overall survival among these genes. The Gene Expression Profiling Interactive Analysis (GEPIA) was applied for further validation of the expression level of these genes.ResultsThe mRNA expression profile datasets of GSE63514, GSE27678, and GSE6791 were downloaded from the Gene Expression Omnibus database (GEO). In total, 76 CC tissues and 35 normal tissues were collected in the three profile datasets. There were totally 73 consistently expressed genes in the three datasets, including 65 up-regulated genes and 8 down-regulated genes. Of PPI network analyzed by Molecular Complex Detection (MCODE) plug-in, all 65 up-regulated genes and 4 down-regulated genes were selected. The results of the Kaplan-Meier survival analysis showed that 3 of the 65 up-regulated genes had a significantly worse prognosis, while 3 of the 4 down-regulated genes had a significantly better outcome. For validation in GEPIA, 4 of 6 genes (PLOD2, ANLN, AURKA, and AR) were confirmed to be significantly deregulated in CC tissues compared to normal tissues.ConclusionWe have identified three up-regulated (PLOD2, ANLN, and AURKA) and a down-regulated DEGs (AR) with poor prognosis in CC on the basis of integrated bioinformatical methods, which could be regarded as potential therapeutic targets for CC patients.

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Identification of key candidate genes and pathways in glioblastoma by integrated bioinformatical analysis.

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Identification of key candidate genes and pathways in glioblastoma by integrated bioinformatical analysis.

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