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

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.

An Atlas of the Knee Joint Proteins and Their Role in Osteoarthritis Defined by Literature Mining

“…By comparison, LRRC32 is a key regulator of transforming growth factor beta activation ( 19 ). Although some transcriptomic and genome-wide association studies have implicated the LRRC32 gene as playing a role in OA ( 20 , 21 ), to our knowledge, no studies have evaluated this potential association at the protein level or its putative functional significance.…”

Computational Exploration of Single-Nucleotide Polymorphisms in the Human hRAS Gene: Implications and Insights

“…SNPs are differences in a single nucleotide that exist in the genome at a frequency greater than 1%. SNPs are found in roughly every 3,000 base pairs of the human genome and the frequency at which the various alleles arise varies throughout populations [ 11 , 12 ]. Genes that regulate cell proliferation may become downregulated as a result of nsSNPs, leading to uncontrollably dividing cells, the growth of cancer cells, and a variety of other human diseases.…”