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Copyright (c) 2025 Waqas Ahmad Abbasi, Sajida Qureshi , Muhammad Asif Qureshi , Mohammad Saeed Quraishy
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
The undersigned hereby assign all rights, included but not limited to copyright, for this manuscript to CMB Association upon its submission for consideration to publication on Cellular and Molecular Biology. The rights assigned include, but are not limited to, the sole and exclusive rights to license, sell, subsequently assign, derive, distribute, display and reproduce this manuscript, in whole or in part, in any format, electronic or otherwise, including those in existence at the time this agreement was signed. The authors hereby warrant that they have not granted or assigned, and shall not grant or assign, the aforementioned rights to any other person, firm, organization, or other entity. All rights are automatically restored to authors if this manuscript is not accepted for publication.Identification of novel variants with predicted pathogenicity as key targets in esophageal cancer
Corresponding Author(s) : Sajida Qureshi
Cellular and Molecular Biology,
Vol. 71 No. 9: Issue 9
Abstract
Esophageal cancer (EC) remains a major global health challenge due to its aggressive nature and poor prognosis. Genetic alterations play a crucial role in tumor progression; however, a deeper understanding of the genetic landscape of EC is essential for identifying novel and potent therapeutic targets. This study aims to identify key genes and their variants with potential pathogenicity driving EC progression. Whole-exome sequencing (WES) was performed on EC samples to identify missense variants. A comprehensive in-silico analysis was conducted using SIFT, FATHMM, PROVEAN, MutationTaster, and LRT to classify high-risk variants. Gene expression, mutation frequency, and prognostic relevance were analyzed using GEPIA and cBioPortal platforms. Protein stability was assessed with MuPro and I-Mutant to evaluate the impact of the identified variant, while protein-protein interaction (PPI) analysis via STRING and enrichment analysis through Metascape were performed to explore associated biological pathways. A total of 331 novel high-risk missense variants were identified across 274 genes and systematically refined, narrowing down to 23 prognostically significant variants in 11 genes (PSMC1, SCN8A, HNRNPA3, RPL23, COL5A2, TBL1XR1, TCP1, HNRNPD, CALM2, ABCC2, and HNRNPA1), which were also among the most differentially expressed in EC. Variants in these genes were predicted to destabilize their corresponding proteins, contributing to EC progression. In-silico survival analysis further indicated significantly worse outcomes for patients harboring alterations in these genes, including others. Protein stability analysis confirmed their destabilizing effects, while functional enrichment highlighted their involvement in key pathways driving tumorigenesis. This study identified 11 key DEGs harboring potentially pathogenic novel missense variants, highlighting vulnerabilities for precision-targeted therapies in EC.
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