Dataset Information

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

ABSTRACT: Hepatocellular carcinoma (HCC) is one of the most common malignant neoplasms worldwide, however the underlying mechanisms and gene signatures of HCC are unknown. In the present study the profile datasets of four cohorts were integrated to elucidate the pathways and candidate genes of HCC. The expression profiles GSE25097, GSE45267, GSE57957 and GSE62232 were downloaded from the Gene Expression Omnibus database, including 436 HCC and 94 normal liver tissues. A total of 185 differentially expressed genes (DEGs) were identified in HCC, including 92 upregulated genes and 92 downregulated genes. Gene ontology (GO) was performed, which revealed that the upregulated DEGs were primarily enriched in cell division, mitotic nuclear division, mitotic cytokinesis and G1/S transition of the mitotic cell cycle. Pathway enrichment was analyzed based on the Kyoto Encyclopedia of Genes and Genomes database to assess the functional relevance of DEGs. The most significant module was selected from protein-protein interactions and 15 important hub genes were identified. The sub-networks of hub genes were involved in cell division, p53 signaling, and T lymphotropic virus type I infection signaling pathways. In conclusion, the present study revealed that the identified DEG candidate genes may promote the understanding of the cause and molecular mechanisms underlying the development of HCC and that these candidates and signal pathways may be potential targets of clinical therapy for HCC.

SUBMITTER: Xing T

PROVIDER: S-EPMC5958738 | biostudies-literature | 2018 Jun

REPOSITORIES: biostudies-literature

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

Xing Tonghai T Yan Tingmang T Zhou Qiang Q

Experimental and therapeutic medicine 20180416 6

Hepatocellular carcinoma (HCC) is one of the most common malignant neoplasms worldwide, however the underlying mechanisms and gene signatures of HCC are unknown. In the present study the profile datasets of four cohorts were integrated to elucidate the pathways and candidate genes of HCC. The expression profiles GSE25097, GSE45267, GSE57957 and GSE62232 were downloaded from the Gene Expression Omnibus database, including 436 HCC and 94 normal liver tissues. A total of 185 differentially expresse ...[more]

PMID: 29805517

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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 hepatocellular carcinoma by integrated bioinformatical analysis.

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

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