Dataset Information

Identification of key modules and hub genes in glioblastoma multiforme based on co-expression network analysis.

ABSTRACT: Glioblastoma multiforme (GBM) is the most malignant primary tumour in the central nervous system, but the molecular mechanisms underlying its pathogenesis remain unclear. In this study, data set GSE50161 was used to construct a co-expression network for weighted gene co-expression network analysis. Two modules (dubbed brown and turquoise) were found to have the strongest correlation with GBM. Functional enrichment analysis indicated that the brown module was involved in the cell cycle, DNA replication, and pyrimidine metabolism. The turquoise module was primarily related to circadian rhythm entrainment, glutamatergic synapses, and axonal guidance. Hub genes were screened by survival analysis using The Cancer Genome Atlas and Human Protein Atlas databases and further tested using the GSE4290 and Gene Expression Profiling Interactive Analysis databases. The eight hub genes (NUSAP1, SHCBP1, KNL1, SULT4A1, SLC12A5, NUF2, NAPB, and GARNL3) were verified at both the transcriptional and translational levels, and these gene expression levels were significant based on the World Health Organization classification system. These hub genes may be potential biomarkers and therapeutic targets for the accurate diagnosis and management of GBM.

SUBMITTER: Li C

PROVIDER: S-EPMC7931238 | biostudies-literature | 2021 Mar

REPOSITORIES: biostudies-literature

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Identification of key modules and hub genes in glioblastoma multiforme based on co-expression network analysis.

Li Chun C Pu Bangming B Gu Long L Zhang Mingwei M Shen Hongping H Yuan Yuan Y Liao Lishang L

FEBS open bio 20210209 3

Glioblastoma multiforme (GBM) is the most malignant primary tumour in the central nervous system, but the molecular mechanisms underlying its pathogenesis remain unclear. In this study, data set GSE50161 was used to construct a co-expression network for weighted gene co-expression network analysis. Two modules (dubbed brown and turquoise) were found to have the strongest correlation with GBM. Functional enrichment analysis indicated that the brown module was involved in the cell cycle, DNA repli ...[more]

PMID: 33423377

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Project description:Atrial fibrillation (AF) is the most common form of cardiac arrhythmia and significantly increases the risks of morbidity, mortality and health care expenditure; however, treatment for AF remains unsatisfactory due to the complicated and incompletely understood underlying mechanisms. In the present study, weighted gene co‑expression network analysis (WGCNA) was conducted to identify key modules and hub genes to determine their potential associations with AF. WGCNA was performed in an AF dataset GSE79768 obtained from the Gene Expression Omnibus, which contained data from paired left and right atria in cardiac patients with persistent AF or sinus rhythm. Differentially expressed gene (DEG) analysis was used to supplement and validate the results of WGCNA. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses were also performed. Green and magenta modules were identified as the most critical modules associated with AF, from which 6 hub genes, acetyl‑CoA Acetyltransferase 1, death domain‑containing protein CRADD, gypsy retrotransposon integrase 1, FTX transcript, XIST regulator, transcription elongation factor A like 2 and minichromosome maintenance complex component 3 associated protein, were hypothesized to serve key roles in the pathophysiology of AF due to their increased intramodular connectivity. Functional enrichment analysis results demonstrated that the green module was associated with energy metabolism, and the magenta module may be associated with the Hippo pathway and contain multiple interactive pathways associated with apoptosis and inflammation. In addition, the blue module was identified to be an important regulatory module in AF with a higher specificity for the left atria, the genes of which were primarily correlated with complement, coagulation and extracellular matrix formation. These results suggest that may improve understanding of the underlying mechanisms of AF, and assist in identifying biomarkers and potential therapeutic targets for treating patients with AF.

Dataset Information

Identification of key modules and hub genes in glioblastoma multiforme based on co-expression network analysis.

Publications

Identification of key modules and hub genes in glioblastoma multiforme based on co-expression network analysis.

Similar Datasets

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