Suppr超能文献

口腔舌鳞状细胞癌的组织代谢特征分析诊断。

Oral tongue squamous cell carcinoma diagnosis from tissue metabolic profiling.

机构信息

Central Laboratory of Stomatology, Nanjing Stomatological Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, Jiangsu, China.

Department of Oral and Maxillofacial Surgery, Nanjing Stomatological Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, Jiangsu, China.

出版信息

Oral Dis. 2024 May;30(4):2158-2165. doi: 10.1111/odi.14696. Epub 2023 Jul 24.

Abstract

OBJECTIVE

Disease metabolomes have been studied for identifying diagnostic and predictive biomarkers of pathology. Oral tongue squamous cell carcinoma (OTSCC) is one of the most prevalent subtypes of head and neck squamous cell carcinoma, yet the profile and diagnostic value of its tissue metabolite are unclear.

SUBJECTS AND METHODS

Tumor tissue samples and matched normal mucosal tissue samples were collected from 40 OTSCC patients. Untargeted metabolic analysis by liquid chromatography-mass spectrometry/mass spectrometry, in positive and negative ion modes, was used to identify dysregulated metabolites in OTSCC. Further, utilizing LASSO regression and receiver operating characteristic analyses, biomarker metabolites were selected and validated, and a diagnostic model was established.

RESULTS

One hundred and ninety metabolites were detected. The OTSCC had a total of 89 dysregulated metabolites, of which 73 were elevated. A diagnostic panel of nine metabolites was subsequently created that could accurately identify OTSCC with 100% sensitivity of 100%, 100% specificity and an AUC of 1.00.

CONCLUSIONS

This study identified distinct metabolic characteristics of OTSCC and established a diagnostic model. Our research also contributes to the investigation of the pathogenesis of OTSCC.

摘要

目的

疾病代谢组学已被用于鉴定病理学的诊断和预测生物标志物。口腔舌鳞状细胞癌(OTSCC)是头颈部鳞状细胞癌中最常见的亚型之一,但它的组织代谢物特征和诊断价值尚不清楚。

受试者和方法

从 40 例 OTSCC 患者中收集肿瘤组织样本和匹配的正常黏膜组织样本。采用液相色谱-质谱/质谱法(正离子和负离子模式)进行非靶向代谢分析,以鉴定 OTSCC 中失调的代谢物。进一步利用 LASSO 回归和接收者操作特征分析,选择和验证生物标志物代谢物,并建立诊断模型。

结果

共检测到 190 种代谢物。OTSCC 共有 89 种失调代谢物,其中 73 种升高。随后创建了一个由 9 种代谢物组成的诊断面板,可准确识别 OTSCC,灵敏度为 100%,特异性为 100%,AUC 为 1.00。

结论

本研究确定了 OTSCC 的独特代谢特征,并建立了诊断模型。我们的研究还有助于探讨 OTSCC 的发病机制。

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验