Computational identification of rare pathogenic genomic variants in esophageal cancer markers: Transcript-level
Muhammad Bilal Azmi1, Sajida Qureshi2, Rafia Wasi1
1Computational Biochemistry Research Laboratory, Department of Biochemistry, Dow Medical College, Dow University of Health Sciences, Karachi, 74200, Pakistan.
Background And Aims:
Esophageal cancer (EC) is a rapidly progressing malignancy that significantly contributes to cancer-related mortality. The genetic causes of EC, particularly rare coding pathogenic variants, remain incompletely defined. This study focuses on non-synonymous single nucleotide polymorphisms (nsSNPs) because they can impact the functions of critical proteins implicated in carcinogenesis.
Methodology:
We employed an extended computational framework for the identification and analysis of rare-coding nsSNPs within 28 EC-associated genes and identified 126 protein isoforms from public databases such as NCBI and ENSEMBL, focusing on those with MAF <1%. Functional predictions were evaluated using different bioinformatics tools to ascertain pathogenicity. Furthermore, ten rare-coding nsSNPs were mapped to 23 transcript-level variants within genes such as GRB7, SLCO1A2, HIF1AN, KCNQ3, and DLL1.
Results:
Computational analysis showed that these variants have a prominent impact on protein stability and function because substitutions occurred at conserved residues, thereby perturbing the stability and important regulatory attributes of the proteins. Structural modeling supported our findings: some variants may compromise domain integrity and influence the signal transduction pathways relevant to the progression of EC. The oncogenic potential of the conserved variants was also computationally validated using the Cscape tool, with candidates including rs1591837395 (G68R) in SLCO1A2 and rs758624092 (D201H) in HIF1AN. Collectively, this study identified rare and potentially pathogenic coding variants in EC-related genes and elaborated on their effects at the transcript, sequence, and structural levels.
Conclusion:
Integrating these insights into EC provides an extended view of its molecular mechanisms and may be used in the near future as a basis for precision medicine for early diagnosis and targeted therapy.
