Dataset Information

Identification of potential therapeutic targets for gliomas by bioinformatics analysis.

ABSTRACT: Gliomas are primary tumors that originate in the brain or spinal cord and develop from supportive glial cells. The present study aimed to identify potential candidate molecular markers for the treatment of gliomas, and to explore the underlying mechanisms of this disease. The gene expression profile data GSE50021, which consisted of 10 specimens of normal brain tissues and 35 specimens of glioma tissues, was downloaded from Gene Expression Omnibus (GEO). The methylation microarray data GSE50022, consisting of 28 glioma specimens, was also downloaded from GEO. Differentially expressed genes (DEGs) between patients with glioma and normal individuals were identified, and key methylation sites were screened. Transcriptional regulatory networks were constructed, and target genes were selected. Survival analysis of key methylation sites and risk analysis of sub-pathways were performed, from which key genes and pathways were selected. A total of 79 DEGs and 179 key methylation sites were identified, of which 20 target genes and 36 transcription factors were included in the transcriptional regulatory network. Glutamate metabotropic receptor 2 (GRM2) was regulated by 8 transcription factors. Inositol-trisphosphate 3-kinase A (ITPKA) was a significantly enriched DEG, associated with the inositol phosphate metabolism pathway, Survival analysis revealed that the survival time of patients with lower methylation levels in cg00157228 was longer than patients with higher methylation levels. ITPKA was the closest located gene to cg00157228. In conclusion, GRM2 and enriched ITPKA, associated with the inositol phosphate metabolism pathway, may be key mechanisms in the development and progression of gliomas. Furthermore, the present study provided evidence for an additional mechanism of methylation-induced gliomas, in which methylation results in the dysregulation of specific transcripts. The results of the present study may provide a research direction for studying the mechanisms underlying the development and progression of gliomas.

SUBMITTER: Ma K

PROVIDER: S-EPMC5652254 | biostudies-literature | 2017 Nov

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Identification of potential therapeutic targets for gliomas by bioinformatics analysis.

Ma Ke K Cheng Zhihua Z Sun Liqun L Li Haibo H

Oncology letters 20170828 5

Gliomas are primary tumors that originate in the brain or spinal cord and develop from supportive glial cells. The present study aimed to identify potential candidate molecular markers for the treatment of gliomas, and to explore the underlying mechanisms of this disease. The gene expression profile data GSE50021, which consisted of 10 specimens of normal brain tissues and 35 specimens of glioma tissues, was downloaded from Gene Expression Omnibus (GEO). The methylation microarray data GSE50022, ...[more]

PMID: 29113156

Similar Datasets

Project description:The aim of the present study was to identify potential therapeutic targets for colorectal cancer (CRC). The gene expression profile GSE32323, containing 34 samples, including 17 specimens of CRC tissues and 17 of paired normal tissues from CRC patients, was downloaded from the Gene Expression Omnibus database. Following data preprocessing using the Affy and preprocessCore packages, the differentially-expressed genes (DEGs) between the two types of samples were identified with the Linear Models for Microarray Analysis package. Next, functional and pathway enrichment analysis of the DEGs was performed using the Database for Annotation Visualization and Integrated Discovery. The protein-protein interaction (PPI) network was established using the Search Tool for the Retrieval of Interacting Genes database. Utilizing WebGestalt, the potential microRNAs (miRNAs/miRs) of the DEGs were screened and the integrated miRNA-target network was built. A cohort of 1,347 DEGs was identified, the majority of which were mainly enriched in cell cycle-related biological processes and pathways. Cyclin-dependent kinase 1 (CDK1), cyclin B1 (CCNB1), MAD2 mitotic arrest deficient-like 1 (MAD2L1) and BUB1 mitotic checkpoint serine/threonine kinase B (BUB1B) were prominent in the PPI network, while the over-represented genes in the integrated miRNA-target network were SRY (sex determining region Y)-box 4 (SOX4; targeted by hsa-mir-129), v-myc avian myelocytomatosis viral oncogene homolog (MYC; targeted by hsa-let-7c and hsa-mir-145) and cyclin D1 (CCND1; targeted by hsa-let-7b). CDK1, CCNB1 and CCND1 were also associated with the p53 signaling pathway. Overall, several genes associated with the cell cycle and p53 pathway were identified as biomarkers for CRC. CDK1, CCNB1, MAD2L1, BUB1B, SOX4, collagen type I ?2 chain and MYC may play significant roles in CRC progression by affecting the cell cycle-related pathways, while CDK1, CCNB1 and CCND1 may serve as crucial regulators in the p53 signaling pathway. Furthermore, SOX4, MYC and CCND1 may be targets of miR-129, hsa-mir-145 and hsa-let-7c, respectively. However, further validation of these data is required.

Project description:BackgroundHeart failure (HF) is the most common cardiovascular diseases and the leading cause of cardiovascular diseases related deaths. Increasing molecular targets have been discovered for HF prognosis and therapy. However, there is still an urgent need to identify novel biomarkers. Therefore, we evaluated biomarkers that might aid the diagnosis and treatment of HF.MethodsWe searched next-generation sequencing (NGS) dataset (GSE161472) and identified differentially expressed genes (DEGs) by comparing 47 HF samples and 37 normal control samples using limma in R package. Gene ontology (GO) and pathway enrichment analyses of the DEGs were performed using the g: Profiler database. The protein-protein interaction (PPI) network was plotted with Human Integrated Protein-Protein Interaction rEference (HiPPIE) and visualized using Cytoscape. Module analysis of the PPI network was done using PEWCC1. Then, miRNA-hub gene regulatory network and TF-hub gene regulatory network were constructed by Cytoscape software. Finally, we performed receiver operating characteristic (ROC) curve analysis to predict the diagnostic effectiveness of the hub genes.ResultsA total of 930 DEGs, 464 upregulated genes and 466 downregulated genes, were identified in HF. GO and REACTOME pathway enrichment results showed that DEGs mainly enriched in localization, small molecule metabolic process, SARS-CoV infections, and the citric acid tricarboxylic acid (TCA) cycle and respiratory electron transport. After combining the results of the PPI network miRNA-hub gene regulatory network and TF-hub gene regulatory network, 10 hub genes were selected, including heat shock protein 90 alpha family class A member 1 (HSP90AA1), arrestin beta 2 (ARRB2), myosin heavy chain 9 (MYH9), heat shock protein 90 alpha family class B member 1 (HSP90AB1), filamin A (FLNA), epidermal growth factor receptor (EGFR), phosphoinositide-3-kinase regulatory subunit 1 (PIK3R1), cullin 4A (CUL4A), YEATS domain containing 4 (YEATS4), and lysine acetyltransferase 2B (KAT2B).ConclusionsThis discovery-driven study might be useful to provide a novel insight into the diagnosis and treatment of HF. However, more experiments are needed in the future to investigate the functional roles of these genes in HF.

Dataset Information

Identification of potential therapeutic targets for gliomas by bioinformatics analysis.

Publications

Identification of potential therapeutic targets for gliomas by bioinformatics analysis.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets