Title
Identification of key regulators in parathyroid adenoma using an integrative gene network analysis
Authors
Nikhat Imam1,2, Aftab Alam2, Mohd Faizan Siddiqui3, Mohd Murshad Ahmed2, Md. Zubbair Malik4, Jawed Ikbal Khan*1 & Romana Ishrat*2
Affiliation
1Institute of Computer Science & Information Technology, Department of Mathematics, Magadh University, Bodh Gaya-824234 (Bihar, India); 2Centre for Interdisciplinary Research in Basic Sciences, Jamia Millia Islamia, New Delhi-110025 (India); 3International Medical Faculty, Osh State University, Osh City, 723500, Kyrgyz Republic (Kyrgyzstan); 4School of Computational & Integrative Sciences, Jawaharlal Nehru University, New Delhi, India.
Dr. Romana Ishrat - Email: rishrat@jmi.ac.in
& Prof. Jawed Ikbal Khan - Email: jawedikbalkhan@gmail.com;
*Corresponding Authors
Article Type
Research Article
Date
Received October 12, 2020; Revised October 23, 2020; Accepted October 23, 2020; Published November 30, 2021
Abstract
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 database name. 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.
Keywords
Microarray gene expression; parathyroid adenoma; primary hyperparathyroidism; DEGs; gene ontology; network analysis
Citation
Imam et al. Bioinformation 16(11): 910-922 (2020)
Edited by
P Kangueane
ISSN
0973-2063
Publisher
License
This is an Open Access article which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. This is distributed under the terms of the Creative Commons Attribution License.
