Network-medicine approach for the identification of genetic association of parathyroid adenoma with cardiovascular disease and type-2 diabetes

Imam N., Alam A., Siddiqui M. F., Veg A., Bay S., Khan M. J. I., ...More

Briefings in functional genomics, vol.22, no.3, pp.250-262, 2023 (SCI-Expanded) identifier identifier identifier

  • Publication Type: Article / Article
  • Volume: 22 Issue: 3
  • Publication Date: 2023
  • Doi Number: 10.1093/bfgp/elac054
  • Journal Name: Briefings in functional genomics
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Applied Science & Technology Source, Biotechnology Research Abstracts, CAB Abstracts, EMBASE, MEDLINE
  • Page Numbers: pp.250-262
  • Keywords: parathyroid adenoma, PHPT, gene ontology, network biology, disease-gene relationship, network medicines
  • Istanbul Medipol University Affiliated: Yes


Primary hyperparathyroidism is caused by solitary parathyroid adenomas (PTAs) in most cases (⁓85%), and it has been previously reported that PTAs are associated with cardiovascular disease (CVD) and type-2 diabetes (T2D). To understand the molecular basis of PTAs, we have investigated the genetic association amongst PTAs, CVD and T2D through an integrative network-based approach and observed a remarkable resemblance. The current study proposed to compare the PTAs-associated proteins with the overlapping proteins of CVD and T2D to determine the disease relationship. We constructed the protein-protein interaction network by integrating curated and experimentally validated interactions in humans. We found the $11$ highly clustered modules in the network, which contain a total of $13$ hub proteins (TP53, ESR1, EGFR, POTEF, MEN1, FLNA, CDKN2B, ACTB, CTNNB1, CAV1, MAPK1, G6PD and CCND1) that commonly co-exist in PTAs, CDV and T2D and reached to network's hierarchically modular organization. Additionally, we implemented a gene-set over-representation analysis over biological processes and pathways that helped to identify disease-associated pathways and prioritize target disease proteins. Moreover, we identified the respective drugs of these hub proteins. We built a bipartite network that helps decipher the drug-target interaction, highlighting the influential roles of these drugs on apparently unrelated targets and pathways. Targeting these hub proteins by using drug combinations or drug-repurposing approaches will improve the clinical conditions in comorbidity, enhance the potency of a few drugs and give a synergistic effect with better outcomes. This network-based analysis opens a new horizon for more personalized treatment and drug-repurposing opportunities to investigate new targets and multi-drug treatment and may be helpful in further analysis of the mechanisms underlying PTA and associated diseases.