2026-10-11T21:46:04Z https://api.figshare.com/v2/oai

oai:figshare.com:article/33904811 2026-09-17T17:38:02Z category_7 category_13 category_14 category_873 category_734 category_931 category_64 category_132 portal_5 item_type_3 month_year_09_2026

<p>Atomic content of wild-type and mutant-type.</p> Faria Ferdouse Mim (25038563) Taslima Akter Sumiya (25038566) Roksana Khanam (25038569) Jannati Akter (25038572) Farhana Arzu (25038575) Samia Haque (25038578) Md. Roman Miah (25038581) K.M. Tanjida Islam (23142024) Shahin Mahmud (13993958) Medicine Genetics Molecular Biology Chemical Sciences not elsewhere classified Biological Sciences not elsewhere classified Information Systems not elsewhere classified Cancer Infectious Diseases transcription factor ezh2 prominent hub gene precise therapeutic decisions patient survival data identify genes exhibiting identify critical genes critically affect post conformational change detection biophysical energetics assessment altered patient survival 886 genes exhibiting 60 nonsense snps 36 missense snps pathogenic variations disrupting unsupervised machine learning molecular framework demonstrating depth computational analyses three tumor suppressors multiple common oncogenes common cancer globally oncogenic genetic variations mutations trigger dysregulation level analysis provides high mutation propensity tumor suppressor pathways 8 %), cdh1 mutation propensity level analysis genetic variations molecular components deep learning computational screening translational modification suppressive capacity study aimed significantly upregulated potential biomarkers often driven mirna mir individual studies including ccne1 genetic variants gc pathways expression dysregulation dysregulated expression differential expression cbioportal database cbioportal confirmed 2 %), 05 ). <div><p>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.</p></div> 2026-09-17T17:37:42Z Dataset Dataset 10.1371/journal.pone.0358440.s014 https://figshare.com/articles/dataset/_p_Atomic_content_of_wild-type_and_mutant-type_p_/33904811 CC BY 4.0