Department of Dentistry, Oral and Maxillofacial Plastic and Reconstructive Surgery, Faculty of Medicine, Yamagata University, Yamagata, Japan;
Department of Neurology, Hematology, Metabolism, Endocrinology and Diabetology, Faculty of Medicine, Yamagata University, Yamagata, Japan.
In Vivo. 2021 Jan-Feb;35(1):541-547. doi: 10.21873/invivo.12289.
BACKGROUND/AIM: The current study aimed to identify biomarkers for differentiating between patients with oral cancer (OC) and healthy controls (HCs) on the basis of the comprehensive proteomic analyses of saliva samples by using liquid chromatography-mass spectrometry (LC-MS/MS).
Unstimulated saliva samples were collected from 39 patients with OC and from 31 HCs. Proteins in the saliva were comprehensively analyzed using LC-MS/MS. To differentiate between patients with OC and HCs, a multiple logistic regression model was developed for evaluating the discriminatory ability of a combination of multiple markers.
A total of 23 proteins were significantly differentially expressed between the patients with OC and the HCs. Six out of the 23 proteins, namely α-2-macroglobulin-like protein 1, cornulin, hemoglobin subunit β, Ig k chain V-II region Vk167, kininogen-1 and transmembrane protease serine 11D, were selected using the forward-selection method and applied to the multiple logistic regression model. The area under the curve for discriminating between patients with OC and HCs was 0.957 when the combination of the six metabolites was used (95% confidence interval=0.915-0.998; p<0.001). Furthermore, these candidate proteins did not show a stage-specific difference.
The results of the current study showed that six salivary proteins are potential non-invasive biomarkers for OC screening.
背景/目的：本研究旨在通过液相色谱-质谱联用（LC-MS/MS）对唾液样本进行全面蛋白质组学分析，以鉴定出区分口腔癌（OC）患者和健康对照（HC）的生物标志物。
收集了 39 名 OC 患者和 31 名 HCs 的非刺激性唾液样本。使用 LC-MS/MS 对唾液中的蛋白质进行了全面分析。为了区分 OC 患者和 HCs，建立了一个多变量逻辑回归模型，用于评估多个标志物组合的鉴别能力。
OC 患者和 HCs 之间共有 23 种蛋白质表达水平存在显著差异。使用前向选择法选择了其中的 6 种蛋白质，即α-2-巨球蛋白样蛋白 1、角蛋白 Cornin、血红蛋白亚基β、免疫球蛋白 k 链 V-II 区 Vk167、激肽原-1 和跨膜蛋白酶丝氨酸 11D，并将其应用于多变量逻辑回归模型。当组合使用这 6 种代谢物时，区分 OC 患者和 HCs 的曲线下面积为 0.957（95%置信区间为 0.915-0.998；p<0.001）。此外，这些候选蛋白没有表现出特定阶段的差异。
本研究结果表明，6 种唾液蛋白可能是 OC 筛查的潜在非侵入性生物标志物。
