Computational screening of oncogenic genetic variations in tumor suppressor proteins driving gastric cancer pathogenesis
- 1Department of Biotechnology and Genetic Engineering, Mawlana Bhashani Science and Technology University, Santosh, Tangail-1902, Bangladesh
- 2Department of Clinical Pharmacy and Pharmacology, University of Dhaka, Dhaka, Bangladesh
Abstract
Gastric cancer (GC) is currently the fifth most common cancer globally, often driven by dysregulation of tumor suppressor pathways. While individual studies on genetic variations of proteins are common, a comprehensive systems-level analysis of proteins regulating GC pathways showing both expression dysregulation and high mutation frequency remains unexplored. Therefore, our study aimed to identify critical genes and their pathogenic variations disrupting the tumor-suppressive capacity of the GC pathway. We employed a deep learning-based graph neural network model to identify genes exhibiting both dysregulated expression and high mutation propensity in GC. Key tumor suppressor proteins (TP53, CDH1, and APC), their genetic variants, and molecular components were subjected to in-depth computational analyses, including evolutionary conservation profiling, biophysical energetics assessment, and unsupervised machine learning for conformational change detection. Variants were validated from the cBioPortal database and patient survival data. Our deep learning model demonstrated exceptional performance (MSE: 0.00482 ± 0.00023 to 0.07108 ± 0.00437; R²: 0.85098 ± 0.01903 to 0.85899 ± 0.01987; AUC-ROC: 0.93095 ± 0.01758 to 0.93309 ± 0.00725) and identified 1,886 genes exhibiting both differential expression and mutation propensity. Graph neural network analysis revealed TP53 as the most prominent hub gene (47.6% mutation frequency), followed by ERBB2 (8.8%), CDH1 (8.2%), and APC (6.8%). Variants validation from cBioPortal confirmed the association of GC with 36 missense SNPs that critically affect post-translational modification (methylation and phosphorylation) sites and 60 nonsense SNPs. Furthermore, TP53, CDH1, and APC were significantly upregulated in GC tissues and associated with altered patient survival (p < 0.05). The transcription factor EZH2 and miRNA miR-129-5p were identified as key regulatory elements affecting all three tumor suppressors. Additionally, mutations trigger dysregulation of multiple common oncogenes, including CCNE1/2 and FGFR2. This systems-level analysis provides a molecular framework demonstrating how pathogenic variants fundamentally compromise tumor suppressor proteins in GC pathways, leading to the identification of potential biomarkers and precise therapeutic decisions for GC intervention.
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BibTeX
@article{mim2026computational,
title = {{Computational screening of oncogenic genetic variations in tumor suppressor proteins driving gastric cancer pathogenesis}},
author = {Faria Ferdouse Mim and Taslima Akter Sumiya and Roksana Khanam and Jannati Akter and Farhana Arzu and Samia Haque and Md. Roman Miah and K.M. Tanjida Islam and Shahin Mahmud},
journal = {PLOS One},
year = {2026},
month = sep,
doi = {10.1371/journal.pone.0358440},
url = {https://doi.org/10.1371/journal.pone.0358440},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Computational screening of oncogenic genetic variations in tumor suppressor proteins driving gastric cancer pathogenesis
AU - Faria Ferdouse Mim
AU - Taslima Akter Sumiya
AU - Roksana Khanam
AU - Jannati Akter
AU - Farhana Arzu
AU - Samia Haque
AU - Md. Roman Miah
AU - K.M. Tanjida Islam
AU - Shahin Mahmud
PY - 2026
DA - 2026/09/17
JO - PLOS One
DO - 10.1371/journal.pone.0358440
UR - https://doi.org/10.1371/journal.pone.0358440
AB - Gastric cancer (GC) is currently the fifth most common cancer globally, often driven by dysregulation of tumor suppressor pathways. While individual studies on genetic variations of proteins are common, a comprehensive systems-level analysis of proteins regulating GC pathways showing both expression dysregulation and high mutation frequency remains unexplored. Therefore, our study aimed to identify critical genes and their pathogenic variations disrupting the tumor-suppressive capacity of the GC pathway. We employed a deep learning-based graph neural network model to identify genes exhibiting both dysregulated expression and high mutation propensity in GC. Key tumor suppressor proteins (TP53, CDH1, and APC), their genetic variants, and molecular components were subjected to in-depth computational analyses, including evolutionary conservation profiling, biophysical energetics assessment, and unsupervised machine learning for conformational change detection. Variants were validated from the cBioPortal database and patient survival data. Our deep learning model demonstrated exceptional performance (MSE: 0.00482 ± 0.00023 to 0.07108 ± 0.00437; R²: 0.85098 ± 0.01903 to 0.85899 ± 0.01987; AUC-ROC: 0.93095 ± 0.01758 to 0.93309 ± 0.00725) and identified 1,886 genes exhibiting both differential expression and mutation propensity. Graph neural network analysis revealed TP53 as the most prominent hub gene (47.6% mutation frequency), followed by ERBB2 (8.8%), CDH1 (8.2%), and APC (6.8%). Variants validation from cBioPortal confirmed the association of GC with 36 missense SNPs that critically affect post-translational modification (methylation and phosphorylation) sites and 60 nonsense SNPs. Furthermore, TP53, CDH1, and APC were significantly upregulated in GC tissues and associated with altered patient survival (p < 0.05). The transcription factor EZH2 and miRNA miR-129-5p were identified as key regulatory elements affecting all three tumor suppressors. Additionally, mutations trigger dysregulation of multiple common oncogenes, including CCNE1/2 and FGFR2. This systems-level analysis provides a molecular framework demonstrating how pathogenic variants fundamentally compromise tumor suppressor proteins in GC pathways, leading to the identification of potential biomarkers and precise therapeutic decisions for GC intervention.
SN - 1932-6203
ER -