唾液代谢组学在伴或不伴异型增生的口腔白斑病中的应用。

Salivary metabolomics for oral leukoplakia with and without dysplasia.

机构信息

Department of Dentistry, Oral and Maxillofacial - Plastic and Reconstructive Surgery Faculty of Medicine, Yamagata University, Yamagata 990-9585, Japan.

出版信息

J Stomatol Oral Maxillofac Surg. 2023 Dec;124(6S):101618. doi: 10.1016/j.jormas.2023.101618. Epub 2023 Sep 1.

DOI:10.1016/j.jormas.2023.101618

PMID:37659754

Abstract

PURPOSE

Oral leukoplakia (OL) is a common potentially malignant oral disorder. Therefore, there is a need for simple screening methods for OL before its transformation into oral cancer. Furthermore, because invasive open biopsy is the sole method to determine if an OL lesion is dysplastic, there is also a clinical need for non-invasive methods to differentiate dysplastic OL from non-dysplastic OL. This study aimed to identify salivary metabolites that can help differentiate patients with OL from healthy controls (HC) and also dysplastic OL from non-dysplastic OL.

MATERIAL & METHODS: Whole unstimulated saliva samples were collected from patients with OL (n = 30) and HCs (n = 29). The OL group included nine patients with dysplastic OL and 20 with non-dysplastic OL. Hydrophilic metabolites in the saliva samples were comprehensively analyzed through capillary electrophoresis mass spectrometry. To evaluate the discrimination ability of a combination of multiple markers, a multiple logistic regression (MLR) model was developed to differentiate patients with OL from HCs and dysplastic OL from non-dysplastic OL.

RESULTS

Twenty-eight metabolites were evidently different between patients with OL and HCs. Finally, three metabolites (guanine, carnitine, and N-acetylputrescine) were selected to develop the MLR model, which resulted in a high area under curve (AUC) of the receiver operating characteristic (ROC) to differentiate patients with OL from HCs (AUC = 0.946, p < 0.001, 95% confidential interval [CI] = 0.889- 1.000). Similarly, two metabolites were evidently different between patients with dysplastic and non-dysplastic OL. Finally, only one metabolite (7-methylguanine) was selected in the MLR model, which revealed a moderate discrimination ability for dysplastic and non-dysplastic OL (AUC = 0761, p = 0.027, 95% CI = 0.551-0.972).

CONCLUSION

Our candidate salivary metabolites showed potential not only to discriminate OL from HC, but also to discriminate dysplastic OL from non-dysplastic OL.

摘要

目的

口腔白斑（OL）是一种常见的潜在恶性口腔疾病。因此，在其发展为口腔癌之前，需要一种简单的 OL 筛查方法。此外，由于侵袭性开放式活检是唯一确定 OL 病变是否异型增生的方法，因此也需要一种非侵入性方法来区分异型增生 OL 和非异型增生 OL。本研究旨在确定唾液代谢物，以帮助区分 OL 患者与健康对照（HC），以及区分异型增生 OL 和非异型增生 OL。

材料与方法

收集 30 例 OL 患者和 29 例 HC 的全唾液样本。OL 组包括 9 例异型增生 OL 患者和 20 例非异型增生 OL 患者。通过毛细管电泳质谱法对唾液样本中的亲水性代谢物进行全面分析。为了评估多个标记物组合的区分能力，建立了多元逻辑回归（MLR）模型，以区分 OL 患者与 HC，以及异型增生 OL 与非异型增生 OL。

结果

OL 患者与 HC 之间有 28 种代谢物明显不同。最后，选择三种代谢物（鸟嘌呤、肉碱和 N-乙酰腐胺）建立 MLR 模型，ROC 的曲线下面积（AUC）对 OL 患者与 HC 进行区分（AUC=0.946，p<0.001，95%置信区间[CI]=0.889-1.000）。同样，异型增生 OL 与非异型增生 OL 之间有两种代谢物明显不同。最后，仅选择 MLR 模型中的一种代谢物（7-甲基鸟嘌呤），对异型增生和非异型增生 OL 具有中等的区分能力（AUC=0.761，p=0.027，95%CI=0.551-0.972）。