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代谢组学在口腔鳞状细胞癌中的应用。

Application of metabolomics in oral squamous cell carcinoma.

机构信息

Department of Oral and Maxillofacial Surgery, Affiliated Hospital of Jiangsu University, Zhenjiang, China.

Department of Burn and Plastic Surgery, Affiliated Hospital of Jiangsu University, Zhenjiang, China.

出版信息

Oral Dis. 2024 Sep;30(6):3719-3731. doi: 10.1111/odi.14895. Epub 2024 Feb 20.

Abstract

BACKGROUND

Oral squamous cell carcinoma (OSCC) is a prevalent malignancy affecting the head and neck region. The prognosis for OSCC patients remains unfavorable due to the absence of precise and efficient early diagnostic techniques. Metabolomics offers a promising approach for identifying distinct metabolites, thereby facilitating early detection and treatment of OSCC.

OBJECTIVE

This review aims to provide a comprehensive overview of recent advancements in metabolic marker identification for early OSCC diagnosis. Additionally, the clinical significance and potential applications of metabolic markers for the management of OSCC are discussed.

RESULTS

This review summarizes metabolic changes during the occurrence and development of oral squamous cell carcinoma and reviews prospects for the clinical application of characteristic, differential metabolites in saliva, serum, and OSCC tissue. In this review, the application of metabolomic technology in OSCC research was summarized, and future research directions were proposed.

CONCLUSION

Metabolomics, detection technology that is the closest to phenotype, can efficiently identify differential metabolites. Combined with statistical data analyses and artificial intelligence technology, it can rapidly screen characteristic biomarkers for early diagnosis, treatment, and prognosis evaluations.

摘要

背景

口腔鳞状细胞癌(OSCC)是一种常见的头颈部恶性肿瘤。由于缺乏精确和有效的早期诊断技术,OSCC 患者的预后仍然不佳。代谢组学为识别独特代谢物提供了一种很有前途的方法,从而有助于 OSCC 的早期检测和治疗。

目的

本综述旨在全面概述用于早期 OSCC 诊断的代谢标志物鉴定方面的最新进展。此外,还讨论了代谢标志物在 OSCC 管理中的临床意义和潜在应用。

结果

本综述总结了口腔鳞状细胞癌发生和发展过程中的代谢变化,并综述了唾液、血清和 OSCC 组织中特征性、差异代谢物的临床应用前景。在本综述中,总结了代谢组学技术在 OSCC 研究中的应用,并提出了未来的研究方向。

结论

代谢组学是最接近表型的检测技术,能够有效地识别差异代谢物。结合统计数据分析和人工智能技术,能够快速筛选出用于早期诊断、治疗和预后评估的特征性生物标志物。

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