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In silico assessment of DNA damage response gene variants associated with head and neck cancer.

Journal of biomolecular structure & dynamics(2023)

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Abstract
Head and neck cancer (HNC), the sixth most common cancer globally, stands first in India, especially Northeast India, where tobacco usage is predominant, which introduces various carcinogens leading to malignancies by accumulating DNA damages. Consequently, the present work aimed to predict the impact of significant germline variants in DNA repair and Tumour Suppressor genes on HNC development. WES in Ion Proton platform on 'discovery set' ( = 15), followed by recurrence assessment of the observed variants on 'confirmation set' ( = 40) using Sanger Sequencing was performed on the HNC-prevalent NE Indian populations. Initially, 53 variants were identified, of which seven HNC-linked DNA damage response gene variants were frequent in the studied populations. Different tools ascertained the biological consequences of these variants, of which the non-coding variants viz. _rs4150018, _rs6413436, _rs2746066, _rs6918700 showed risk, while _rs2491227 and _rs7074064 conferred protection against HNC by affecting transcriptional regulation and splicing mechanism. Molecular Dynamics Simulation of the full-length p53 model predicted that the observed coding _rs1042522 variant conferred HNC-risk by altering the structural dynamics of the protein, which displayed difficulty in the transition between active and inactive conformations due to high-energy barrier. Subsequent pathway and gene ontology analysis revealed that , and variants affected the Double-Strand Break Repair pathway, whereas and variants inactivated DNA repair cascade, facilitating uncontrolled cell proliferation, impaired apoptosis and malignant transformation. Conversely, and variants protected against HNC by controlling tumorigenesis, which requires experimental validation. These findings may serve as prognostic markers for developing preventive measures against HNC.
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Key words
DNA damage response,HNC,cancer-causing genes,full-length p53 model,germline variants,in silico assessment,molecular dynamics simulation,transcriptional regulation
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