Xiangjun Wang, Panpan Jin, Juan Xu, Junyi Li, Mengzhen Ji
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Machine learning algorithms (n = 127 combinations) were evaluated for optimal biomarker identification, with model interpretability assessed using SHAP (SHapley Additive exPlanations) analysis.</p><p><strong>Results: </strong>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.</p><p><strong>Conclusion: </strong>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.</p>","PeriodicalId":73066,"journal":{"name":"Frontiers in bioinformatics","volume":"5 ","pages":"1664576"},"PeriodicalIF":3.9000,"publicationDate":"2025-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12508658/pdf/","citationCount":"0","resultStr":"{\"title\":\"Integrative machine learning and transcriptomic analysis identifies key molecular targets in MNPN-associated oral squamous cell carcinoma pathogenesis.\",\"authors\":\"Xiangjun Wang, Panpan Jin, Juan Xu, Junyi Li, Mengzhen Ji\",\"doi\":\"10.3389/fbinf.2025.1664576\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>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.</p><p><strong>Methods: </strong>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.</p><p><strong>Results: </strong>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. 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引用次数: 0
摘要
背景：口腔鳞状细胞癌（OSCC）是一个重大的全球健康挑战，槟榔是一个主要的危险因素。3-（甲基亚硝胺）丙腈（MNPN）是一种源自槟榔的亚硝胺，已被确定为一种潜在的致癌物，但其在OSCC发病机制中的分子靶点尚不清楚。方法：我们采用了一个综合的计算框架，整合了目标预测、转录组学分析、加权基因共表达网络分析（WGCNA）和机器学习方法。分析来自Gene Expression Omnibus （GEO）的4个OSCC数据集，并使用ChEMBL、PharmMapper和SwissTargetPrediction数据库预测MNPN靶点。评估机器学习算法（n = 127个组合）以确定最佳生物标志物，并使用SHapley加性解释（SHapley Additive explanation）分析评估模型的可解释性。结果：目标预测在三个数据库中确定了881个潜在的MNPN目标。WGCNA共发现534个oscc相关差异表达基因，其中38个MNPN靶点重叠。机器学习优化识别出13个轮毂基因，其中PLAU的预测性能最高（AUC = 0.944）。SHAP分析证实PLAU和PLOD3是预测疾病最具影响力的因子。功能富集分析显示MNPN靶点参与异种生物反应、缺氧条件和异常组织重塑。结论：本研究首次提供了mnpn相关OSCC发病机制的全面分子特征，确定了PLAU是具有特殊诊断潜力的关键治疗靶点。我们的研究结果为开发针对槟榔亚硝胺相关口腔癌的靶向干预奠定了基础，并展示了综合计算方法在环境致癌物研究中的力量。
Integrative machine learning and transcriptomic analysis identifies key molecular targets in MNPN-associated oral squamous cell carcinoma pathogenesis.
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.
Conclusion: 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.
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