Introduction

Oral cancer is a sixth commonly occurring cancer globally. The use of tobacco and alcohol consumption are being considered as the major risk factors for oral cancer. The metabolic profiling of tissue specimens for developing carcinogenic perturbations will allow better prognosis.

Objectives

To profile and generate precise ¹H HRMAS NMR spectral and quantitative statistical models of oral squamous cell carcinoma (OSCC) in tissue specimens including tumor, bed, margin and facial muscles. To apply the model in blinded prediction of malignancy among oral and neck tissues in an unknown set of patients suffering from OSCC along with neck invasion.

Methods

Statistical models of ¹H HRMAS NMR spectral data on 180 tissues comprising tumor, margin and bed from 43 OSCC patients were performed. The combined metabolites, lipids spectral intensity and concentration-based malignancy prediction models were proposed. Further, 64 tissue specimens from twelve patients, including neck invasions, were tested for malignancy in a blinded manner.

Results

Forty-eight metabolites including lipids have been quantified in tumor and adjacent tissues. All metabolites other than lipids were found to be upregulated in malignant tissues except for ambiguous glucose. All of three prediction models have successfully identified malignancy status among blinded set of 64 tissues from 12 OSCC patients with an accuracy of above 90%.

Conclusion

The efficiency of the models in malignancy prediction based on tumor induced metabolic perturbations supported by histopathological validation may revolutionize the OSCC assessment. Further, the results may enable machine learning to trace tumor induced altered metabolic pathways for better pattern recognition. Thus, it complements the newly developed REIMS-MS iKnife real time precession during surgery.

中文翻译：

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口腔SCC患者组织中的恶性预测，包括颈部侵犯：基于1 H HRMAS NMR的代谢组学研究

介绍

口腔癌是全球第六大常见癌症。吸烟和饮酒被认为是口腔癌的主要危险因素。进行致癌性扰动的组织标本的代谢谱分析可提供更好的预后。

目标

为了剖析并生成口腔鳞状细胞癌（OSCC）的组织样本（包括肿瘤，床，边缘和面部肌肉）中的精确¹ H HRMAS NMR光谱和定量统计模型。将该模型应用于未知患者中，OSCC伴有颈部浸润的口腔和颈部组织中的恶性肿瘤的盲预测。

方法

对43例OSCC患者的包括肿瘤，边缘和床的180个组织进行了¹ H HRMAS NMR光谱数据的统计模型。提出了组合代谢物，脂质谱强度和基于浓度的恶性肿瘤预测模型。此外，以盲法测试了来自十二个患者的64个组织标本，包括颈部侵犯，以检查其恶性程度。

结果

包括脂质在内的48种代谢物已在肿瘤和邻近组织中定量。发现除了脂质之外，在脂质组织中除脂质以外的所有代谢物均上调。这三个预测模型均已成功地从12位OSCC患者的64个组织的盲组中识别出恶性状态，其准确率超过90％。

结论

基于肿瘤诱发的代谢扰动的组织病理学验证支持的恶性肿瘤预测模型的效率可能会改变OSCC评估。此外，结果可能使机器学习能够追踪肿瘤诱导的改变的代谢途径，从而更好地识别模式。因此，它补充了新开发的REIMS-MS iKnife手术期间的实时进动。