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CC BY 4.0 · Glob Med Genet 2022; 09(02): 159-165
DOI: 10.1055/s-0042-1743571
Original Article

A Computational Data Mining Strategy to Identify the Common Genetic Markers of Temporomandibular Joint Disorders and Osteoarthritis

Vijayashree Priyadharsini Jayaseelan 
1   Clinical Genetics Lab, Cellular and Molecular Research Centre, Saveetha Dental College and Hospital, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Poonamallee High Road, Chennai, Tamil Nadu, India
,
Paramasivam Arumugam 
2   Molecular Biology Lab, Cellular and Molecular Research Centre, Saveetha Dental College and Hospital, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Poonamallee High Road, Chennai, Tamil Nadu, India
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Abstract

Statement of Problem Prosthodontic planning in patients with temporomandibular joint disorders (TMDs) is a challenge for the clinicians.

Purpose A differential biomarker identification could aid in developing methods for early detection and confirmation of TMD from other related conditions.

Materials and Methods The present study identified candidate genes with possible association with TMDs. The observational study delineates genes from three datasets retrieved from DisGeNET database. The convergence of datasets identifies potential genes related to TMDs with associated complication such as osteoarthritis. Gene ontology analysis was also performed to identify the potential pathways associated with the genes belonging to each of the datasets.

Results The preliminary analysis revealed vascular endothelial growth factor A (VEGFA), interleukin 1 β (IL1B, and estrogen receptor 1 (ESR1) as the common genes associated with all three phenotypes assessed. The gene ontology analysis revealed functional pathways in which the genes of each dataset were clustered. The chemokine and cytokine signaling pathway, gonadotropin-releasing hormone receptor pathway, cholecystokinin receptors (CCKR) signaling, and tumor growth factor (TGF)-β signaling pathway were the pathways most commonly associated with the phenotypes. The genes CCL2, IL6, and IL1B were found to be the common genes across temporomandibular joint (TMJ) and TMJ + osteoarthritis (TMJ-OA) datasets.

Conclusion Analysis through computational approach has revealed IL1B as the crucial candidate gene which could have a strong association with bone disorders. Nevertheless, several immunological pathways have also identified numerous genes showing putative association with TMJ and other related diseases. These genes have to be further validated using experimental approaches to acquire clarity on the mechanisms related to the pathogenesis.

Keywords

computational approach - osteoarthritis - temporomandibular joint disorders - inflammation - genetic markers

Authors' Contributions

V.P.J.: conceptualization, data analysis, interpretation, and manuscript draft preparation. P.A.: manuscript preparation and editing. All authors read and approved their manuscript.




Publication History

Received: 27 December 2021

Accepted: 27 January 2022

Article published online:
09 March 2022

© 2022. The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution License, permitting unrestricted use, distribution, and reproduction so long as the original work is properly cited. (https://creativecommons.org/licenses/by/4.0/)

Georg Thieme Verlag KG
Rüdigerstraße 14, 70469 Stuttgart, Germany

 

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