Statistical Analysis of Gene Variants for Homologous Recombination Pathways of DNA Repair leading to Cancer Susceptibility

Usha  Adiga; B.  Jyoti; P.  Reddemma; Alfred J.  Augustine; Sampara  Vasishta

doi:10.6000/1929-6029.2024.13.36

Authors

Usha Adiga Department of Biochemistry, Apollo Institute of Medical Sciences and Research, Murukambattu - 517127, Chittoor, Andhra Pradesh, India https://orcid.org/0000-0001-7832-3991
B. Jyoti Department of Pathology, Apollo Institute of Medical Sciences and Research, Murukambattu - 517127, Chittoor, Andhra Pradesh, India
P. Reddemma Department of Biochemistry, Apollo Institute of Medical Sciences and Research, Murukambattu - 517127, Chittoor, Andhra Pradesh, India
Alfred J. Augustine Department of Surgery, Apollo Institute of Medical Sciences and Research, Murukambattu - 517127, Chittoor, Andhra Pradesh, India
Sampara Vasishta Department of Biochemistry, Apollo Institute of Medical Sciences and Research, Murukambattu - 517127, Chittoor, Andhra Pradesh, India

DOI:

https://doi.org/10.6000/1929-6029.2024.13.36

Keywords:

RAD51C, homologous recombination, DNA repair, functional prediction, pathway enrichment

Abstract

Background: RAD51C, a critical member of the RAD51 paralog family, is essential for homologous recombination (HR)-mediated DNA repair, a pathway crucial for maintaining genomic stability. Mutations in RAD51C have been linked to cancer susceptibility, particularly in breast and ovarian cancers, where impaired DNA repair mechanisms contribute to genomic instability and tumor progression. Despite its clinical significance, the functional impact of specific RAD51C variants remains poorly understood, necessitating a comprehensive investigation into their biological implications.

Methods: This study classified RAD51C gene variants into damaging and tolerant categories using computational prediction tools, including SIFT, PolyPhen, CADD, MetaLR, and Mutation Assessor. Variants were prioritized based on consensus scores and classified as high-confidence damaging variants. Correlation and agreement among tools were analyzed to refine predictions. Principal Component Analysis (PCA) and clustering methods were employed to group variants based on prediction patterns. Protein-protein interaction (PPI) networks and pathway enrichment analyses were conducted to contextualize damaging variants within broader biological systems, with a focus on their roles in HR, DNA repair, and cellular processes.

Results: A total of 2526 variants were analyzed, with damaging variants showing consistent patterns across tools. Consensus scores highlighted 302 high-confidence damaging variants, which were associated with disrupted biological processes, including double-strand break repair via homologous recombination, telomere maintenance, and regulation of cell cycle checkpoints. PPI analysis revealed an interconnected network with 11 nodes and 54 edges, with a clustering coefficient of 0.982, indicating tightly coordinated interactions among DNA repair proteins. Pathway enrichment analyses identified significant associations with homologous recombination (FDR = 2.55E-17) and the Fanconi anemia pathway (FDR = 2.96E-06).

Conclusion: This study provides a comprehensive framework for assessing the functional impacts of RAD51C variants by integrating computational predictions with biological analyses. The findings underscore the importance of RAD51C in HR and DNA repair pathways, offering insights into its role in genomic stability and cancer progression. These results can inform the prioritization of variants for experimental validation and guide therapeutic strategies targeting DNA repair deficiencies.

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