NDLI: Co-expression network analysis of differentially expressed genes associated with metastasis in prolactin pituitary tumors.

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Co-expression network analysis of differentially expressed genes associated with metastasis in prolactin pituitary tumors.

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Co-expression network analysis of differentially expressed genes associated with metastasis in prolactin pituitary tumors.

Content Provider	World Health Organization (WHO)-Global Index Medicus
Author	Yin, Qing Song, Yechun Zhang, Wei Zang, Zhenle Yang, Hui
Description	Author Affiliation: Zhang W ( Department of Neurosurgery, Xinqiao Hospital, Third Military Medical University, Chongqing 400037, P.R. China.); Zang Z ( Department of Neurosurgery, Xinqiao Hospital, Third Military Medical University, Chongqing 400037, P.R. China.); Song Y ( Department of Neurosurgery, Xinqiao Hospital, Third Military Medical University, Chongqing 400037, P.R. China.); Yang H ( Department of Neurosurgery, Xinqiao Hospital, Third Military Medical University, Chongqing 400037, P.R. China.); Yin Q ( Department of Rehabilitation and Physical Therapy, Southwest Hospital, Third Military Medical University, Chongqing 400038, P.R. China.)
Abstract	The aim of the present study was to construct a coexpression network of differently expressed genes (DEGs) in prolactin pituitary (PRL) tumor metastasis. The gene expression profile, GSE22812 was downloaded from the Gene Expression Omnibus database, and including five noninvasive, two invasive and six aggressiveinvasive PRL tumor samples. Compared with noninvasive samples, DEGs were identified in invasive and aggressiveinvasive samples using a limma package in R language. The expression values of DEGs were hierarchically clustered. Next, Gene Ontology (GO) function enrichment and Kyoto Encyclopedia of Genes and Genomes pathway analysis of DEGs were performed via The Database for Annotation, Visualization and Integrated Discovery. Finally, gene pairs of DEGs between noninvasive and aggressiveinvasive samples were identified using the Spearman cor( ) function in R language. Compared with the noninvasive samples, 61 and 89 DEGs were obtained from invasive and aggressiveinvasive samples, respectively. Cluster analysis showed that four genes were shared by the two samples, including upregulated solute carrier family 2, facilitated glucose transporter member 11 (SLC2A11) and teneurin transmembrane protein 1 (TENM1) and downregulated importin 7 (IPO7) and chromogranin B (CHGB). In the invasive samples, the most significant GO terms responded to cyclic adenosine monophosphate and a glucocorticoid stimulus. However, this occurred in the cell cycle, and was in response to hormone stimulation in aggressiveinvasive samples. The coexpression network of DEGs showed different gene pairs and modules, and SLC2A11 and CHGB occurred in two coexpression networks within different coexpressed pairs. In the present study, the coexpression network was constructed using bioinformatics methods. SLC2A11, TENM1, IPO7 and CHGB are hypothesized to be closely associated with metastasis of PRL. Furthermore, CHGB and SLC2A11 may be significant in PRL tumor progression and serve as molecular biomarkers for PRL tumors. However, further investigation is required to confirm the current results.
ISSN	17912997
e-ISSN	17913004
Journal	Molecular Medicine Reports
Issue Number	1
Volume Number	10
Language	English
Publisher	Spandidos Publications
Publisher Date	2014-07-01
Publisher Place	Greece
Access Restriction	Open
Subject Keyword	Gene Regulatory Networks Prolactinoma Metabolism Chromogranin B Genetics Cluster Analysis Databases, Genetic Gene Expression Profiling Gene Expression Regulation, Neoplastic Glucose Transport Proteins, Facilitative Neoplasm Metastasis Oligonucleotide Array Sequence Analysis Pathology Research Support, Non-u.s. Gov't Discipline Molecular Biology
Content Type	Text
Resource Type	Article
Subject	Genetics Biochemistry Molecular Biology Cancer Research Molecular Medicine Oncology

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