Identification of key regulators in parathyroid adenoma using an integrative gene network analysis

Parathyroid adenoma (PA) is marked by a certain benign outgrowth in the surface of parathyroid glands. The transcriptome analysis of parathyroid adenomas can provide a deep insight into actively expressed genes and transcripts. Hence, we analyzed and compared the gene expression profiles of parathyr...

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Bibliographic Details
Published in:Bioinformation 2020-11, Vol.16 (11), p.910-922
Main Authors: Imam, Nikhat, Alam, Aftab, Siddiqui, Mohd Faizan, Ahmed, Mohd Murshad, Malik, Md Zubbair, Ikbal Khan, Md Jawed, Ishrat, Romana
Format: Article
Language:English
Subjects:
Adenoma
Biological activity
Calcium channels (voltage-gated)
Crosstalk
Gene expression
Genes
Molecular modelling
Network analysis
Parathyroid
Parathyroid gland
Tumors
Wnt protein
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Summary:Parathyroid adenoma (PA) is marked by a certain benign outgrowth in the surface of parathyroid glands. The transcriptome analysis of parathyroid adenomas can provide a deep insight into actively expressed genes and transcripts. Hence, we analyzed and compared the gene expression profiles of parathyroid adenomas and healthy parathyroid gland tissues from Gene Expression Omnibus (GEO) database. We identified a total of 280 differentially expressed genes (196 up-regulated, 84 down-regulated), which are involved in a wide array of biological processes. We further constructed a gene interaction network and analyzed its topological properties to know the network structure and its hidden mechanism. This will help to understand the molecular mechanisms underlying parathyroid adenoma development. We thus identified 13 key regulators (PRPF19, SMC3, POSTN, SNIP1, EBF1, MEIS2, PAX9, SCUBE2, WNT4, ARHGAP10, DOCK5, CAV1 and VSIR), which are deep-rooted from top to bottom in the gene interaction network forming a backbone for the network. The structural features of the network are probably maintained by crosstalk between important genes within the network along with associated functional modules.Thus, gene-expression profiling and network approach could be used to provide an independent platform to glen insights from available clinical data.
ISSN:0973-2063
0973-8894
0973-2063
DOI:10.6026/97320630016910