ORIGINAL RESEARCH article
Front. Bioinform.
Sec. Network Bioinformatics
Volume 5 - 2025 | doi: 10.3389/fbinf.2025.1664576
Integrative Machine Learning and Transcriptomic Analysis Identifies Key Molecular Targets in MNPN-Associated Oral Squamous Cell Carcinoma Pathogenesis
Provisionally accepted
- Department of Stomatology, Department of Clinical Laboratory, The Third People’s Hospital of Henan Province, Zhengzhou, China
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Background: Oral squamous cell carcinoma (OSCC) represents a significant global health challenge, with betel nut consumption being a major risk factor. 3-(methylnitrosamino)propionitrile (MNPN), a betel nut-derived nitrosamine, has been identified as a potential carcinogen, but its molecular targets in OSCC pathogenesis remain poorly understood. Methods: We employed a comprehensive computational framework integrating target prediction, transcriptomic analysis, weighted gene co-expression network analysis (WGCNA), and machine learning approaches. Four OSCC datasets from Gene Expression Omnibus (GEO) were analyzed, and MNPN targets were predicted using ChEMBL, PharmMapper, and SwissTargetPrediction databases. Machine learning algorithms (n=127 combinations) were evaluated for optimal biomarker identification, with model interpretability assessed using SHAP (SHapley Additive exPlanations) analysis. Results: Target prediction identified 881 potential MNPN targets across three databases. WGCNA revealed 534 OSCC-associated differentially expressed genes, with 38 overlapping MNPN targets. Machine learning optimization identified 13 hub genes, with PLAU demonstrating the highest predictive performance (AUC=0.944). SHAP analysis confirmed PLAU and PLOD3 as the most influential contributors to disease prediction. Functional enrichment analysis revealed MNPN targets' involvement in xenobiotic response, hypoxic conditions, and aberrant tissue remodeling. Conclusions: This study provides the first comprehensive molecular characterization of MNPN-associated OSCC pathogenesis, identifying PLAU as a critical therapeutic target with exceptional diagnostic potential. Our findings establish a foundation for developing targeted interventions for betel nut nitrosamine-associated oral cancers and demonstrate the power of integrative computational approaches in environmental carcinogen research.
Keywords: Oral squamous cell carcinoma (OSCC), Betel nut nitrosamine, 3-(methylnitrosamino)propionitrile (MNPN), Transcriptomic Analysis, machine learning
Received: 12 Jul 2025; Accepted: 01 Sep 2025.
Copyright: © 2025 Wang, Jin, Xu, Li and Ji. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Xiangjun Wang, Department of Stomatology, Department of Clinical Laboratory, The Third People’s Hospital of Henan Province, Zhengzhou, China
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