Dataset Information

Identification of Transcriptional Metabolic Dysregulation in Subtypes of Pituitary Adenoma by Integrated Bioinformatics Analysis.

ABSTRACT: Background:Pituitary adenoma (PA) is a prevalent intracranial tumor. Metabolites differ between pituitary tumor and healthy tissues or among different tumor subtypes. However, the transcriptional changes in metabolic enzymes, which are usually seemed as targets for metabolic therapy, remain unidentified. Methods:Using microarray data for 160 samples from the Gene Expression Omnibus database, across the four most common tumor subtypes, we present the integrated identification of differentially expressed genes (DEGs) between tumors and controls. Results:Subtype-specific DEGs revealed 1081 prolactin tumor-specific DEGs, 437 nonfunctioning tumor-specific DEGs, and 217 common DEGs among the four subtypes. Functional enrichment showed that a lot of biological functions related to metabolism had changed. Twenty-one prolactin and twenty-three nonfunctioning tumor-specific metabolic-related DEGs are mainly involved in fatty acid and nucleotide metabolism, redox reaction, and gluconeogenesis. Eighteen metabolic-related DEGs enriched in the metabolism of xenobiotics by the cytochrome P450 pathway, sulfur metabolism, retinoid metabolism, and glucose homeostasis were abnormal in all subtypes of PA. Conclusion:Based on a comprehensive bioinformatics analysis of the available PA-related transcriptomics data, we identified specific DEGs related to metabolism, and some of them might be new attractive therapeutic targets. Especially, PDK4 and PCK1 might be new attractive biomarkers and therapeutic targets.

SUBMITTER: Hu J

PROVIDER: S-EPMC6885545 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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Identification of Transcriptional Metabolic Dysregulation in Subtypes of Pituitary Adenoma by Integrated Bioinformatics Analysis.

Hu Jintao J Yin Huachun H Li Bo B Yang Hui H

Diabetes, metabolic syndrome and obesity : targets and therapy 20191127

<h4>Background</h4>Pituitary adenoma (PA) is a prevalent intracranial tumor. Metabolites differ between pituitary tumor and healthy tissues or among different tumor subtypes. However, the transcriptional changes in metabolic enzymes, which are usually seemed as targets for metabolic therapy, remain unidentified.<h4>Methods</h4>Using microarray data for 160 samples from the Gene Expression Omnibus database, across the four most common tumor subtypes, we present the integrated identification of di ...[more]

PMID: 31819570

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Project description:Pituitary prolactinoma is one of the most complicated and fatally pathogenic pituitary adenomas. Therefore, there is an urgent need to improve our understanding of the underlying molecular mechanism that drives the initiation, progression, and metastasis of pituitary prolactinoma. The aim of the present study was to identify the key genes and signaling pathways associated with pituitary prolactinoma using bioinformatics analysis. Transcriptome microarray dataset GSE119063 was downloaded from Gene Expression Omnibus (GEO) database. Limma package in R software was used to screen DEGs. Pathway and Gene ontology (GO) enrichment analysis were conducted to identify the biological role of DEGs. A protein-protein interaction (PPI) network was constructed and analyzed by using HIPPIE database and Cytoscape software. Module analyses was performed. In addition, a target gene-miRNA regulatory network and target gene-TF regulatory network were constructed by using NetworkAnalyst and Cytoscape software. Finally, validation of hub genes by receiver operating characteristic (ROC) curve analysis. A total of 989 DEGs were identified, including 461 up regulated genes and 528 down regulated genes. Pathway enrichment analysis showed that the DEGs were significantly enriched in the retinoate biosynthesis II, signaling pathways regulating pluripotency of stem cells, ALK2 signaling events, vitamin D3 biosynthesis, cell cycle and aurora B signaling. Gene Ontology (GO) enrichment analysis showed that the DEGs were significantly enriched in the sensory organ morphogenesis, extracellular matrix, hormone activity, nuclear division, condensed chromosome and microtubule binding. In the PPI network and modules, SOX2, PRSS45, CLTC, PLK1, B4GALT6, RUNX1 and GTSE1 were considered as hub genes. In the target gene-miRNA regulatory network and target gene-TF regulatory network, LINC00598, SOX4, IRX1 and UNC13A were considered as hub genes. Using integrated bioinformatics analysis, we identified candidate genes in pituitary prolactinoma, which might improve our understanding of the molecular mechanisms of pituitary prolactinoma.

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Identification of Transcriptional Metabolic Dysregulation in Subtypes of Pituitary Adenoma by Integrated Bioinformatics Analysis.

Publications

Identification of Transcriptional Metabolic Dysregulation in Subtypes of Pituitary Adenoma by Integrated Bioinformatics Analysis.

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