Document Type : Research Articles
Authors
1
Department of Animal Science, Faculty of Agriculture, Ferdowsi University of Mashhad, Mashhad, Iran
2
Department of Animal Science, Faculty of Agriculture, Ferdowsi University of Mashhad
10.22067/jcmr.2026.98223.1134
Abstract
Background: Pan-cancer genomic studies have indicated that diverse tumor types share recurrent molecular alterations converging on common oncogenic pathways, yet the network architecture linking these shared drivers remains incompletely characterized. Identifying core hub genes and cross-tumor biomarkers within the protein–protein interaction (PPI) network could reveal fundamental principles of oncogenesis and nominate novel therapeutic targets.
Methods: In this study, the authors systematically analyzed 1,400 recurrently top 50 most frequently mutated genes across 28 cancer types from The Cancer Genome Atlas (TCGA) via the Genomic Data Commons, curating 335 unique candidates after duplicate removal. A protein–protein interaction (PPI) network was constructed using STRING in Cytoscape (v3.10.3), and hub genes were prioritized through Network Analyzer degree scoring, cytoHubba Maximal Clique Centrality, and Molecular Complex Detection clustering integration, a triple-method consensus. Functional enrichment was performed using ClueGO/CluePedia with Bonferroni-corrected significance (p < 0.05).
Results: The resulting network comprised 335 nodes and 6114 edges with high centralization (0.527). MUC16 was consistently represented in the top-50 somatic mutation-frequency lists for all 28 analyzed cancer types, indicating broad cross-tumor recurrence at the level of mutation-list inclusion. and was connected to highly ranked hubs, including TP53, CTNNB1, MYC, and KRAS. The seven core hub genes identified through the intersection of the top-10 NetworkAnalyzer degree ranking and the top-10 cytoHubba Maximal Clique Centrality ranking were TP53, PTEN, PIK3CA, NRAS, KRAS, CTNNB1, and CDKN2A. NOTCH1 was identified as an additional network-associated hub by cytoHubba and MCODE but was not included in the final seven-gene intersection. Enriched pathways included negative regulation of proliferation, stem cell maintenance, protein stability, chromatin remodeling, and microRNA-mediated gene silencing.
Conclusion: These findings indicate that recurrent pan-cancer drivers converge on interconnected hubs disrupting growth-suppressive and stemness circuits, nominating MUC16, CSMD3, FAT family genes, and KMT2C/KMT2D as exploratory cross-tumor biomarker candidates and potential therapeutic targets for further validation.
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