Differential diagnosis of lung cancer and benign lung lesion using salivary metabolites: A preliminary study

医学 唾液 生物标志物 肺癌 代谢物 内科学 曲线下面积 置信区间 胃肠病学 病理 肿瘤科 生物化学 化学
作者
Satoshi Takamori,Shinya Ishikawa,Jun Suzuki,Hiroyuki Oizumi,Tetsuro Uchida,Shohei Ueda,Kaoru Edamatsu,Mitsuyoshi Iino,Masahiro Sugimoto
出处
期刊:Thoracic Cancer [Wiley]
卷期号:13 (3): 460-465 被引量:13
标识
DOI:10.1111/1759-7714.14282
摘要

Saliva is often used as a biomarker for the diagnosis of some oral and systematic diseases, owing to the non-invasive attribute of the fluid. In this study, we aimed to identify salivary biomarkers for distinguishing lung cancer (LC) from benign lung lesion (BLL).Unstimulated saliva samples were collected from 41 patients with LC and 21 with BLL. Salivary metabolites were comprehensively analyzed using capillary electrophoresis mass spectrometry. To differentiate between patients with LCs and BLLs, the discriminatory ability of each biomarker was assessed. Furthermore, a multiple logistic regression (MLR) model was developed for evaluating discriminatory ability of each salivary metabolite.The profiles of 10 salivary metabolites were remarkably different between the LC and BLL samples. Among them, the concentration of salivary tryptophan was significantly lower in the samples from patients with LC than in those from patients with BLL, and the area under the curve (AUC) for discriminating patients with LC from those with BLL was 0.663 (95% confidence interval [CI] = 0.516-0.810, p = 0.036). Furthermore, from the MLR model developed using these metabolites, diethanolamine, cytosine, lysine, and tyrosine, were selected using the back-selection regression method. The MLR model based on these four metabolites had a high discriminatory ability for patients with LC and those with BLL (AUC = 0.729, 95% CI = 0.598-0.861, p = 0.003).The four salivary metabolites can serve as potential non-invasive biomarkers for distinguishing LC from BLL.

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
量子星尘发布了新的文献求助10
1秒前
我是老大应助小汁儿采纳,获得10
1秒前
1秒前
2秒前
大卷发布了新的文献求助10
2秒前
2秒前
3秒前
璎丸子完成签到,获得积分10
3秒前
3秒前
orixero应助Xuexi采纳,获得10
4秒前
菠萝发布了新的文献求助10
4秒前
张贵虎完成签到 ,获得积分10
4秒前
111发布了新的文献求助10
5秒前
逃亡的小狗完成签到,获得积分10
5秒前
5秒前
5秒前
雪白煜城完成签到,获得积分10
5秒前
6秒前
ccccl完成签到,获得积分10
6秒前
嘿嘿发布了新的文献求助10
6秒前
宝安发布了新的文献求助10
7秒前
wu发布了新的文献求助10
7秒前
7秒前
量子星尘发布了新的文献求助10
8秒前
黄豆酱发布了新的文献求助10
8秒前
8秒前
蓝岚关注了科研通微信公众号
9秒前
酷波er应助123w123采纳,获得10
9秒前
双子苦糖完成签到,获得积分10
9秒前
非洲大象完成签到,获得积分10
9秒前
10秒前
不田完成签到,获得积分10
10秒前
10秒前
薛妖怪发布了新的文献求助10
11秒前
可爱的函函应助123采纳,获得10
11秒前
11秒前
13秒前
HJJHJH发布了新的文献求助20
13秒前
量子星尘发布了新的文献求助10
14秒前
ZZZ完成签到,获得积分10
14秒前
高分求助中
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
Clinical Microbiology Procedures Handbook, Multi-Volume, 5th Edition 2000
The Cambridge History of China: Volume 4, Sui and T'ang China, 589–906 AD, Part Two 1000
The Composition and Relative Chronology of Dynasties 16 and 17 in Egypt 1000
Russian Foreign Policy: Change and Continuity 800
Real World Research, 5th Edition 800
Qualitative Data Analysis with NVivo By Jenine Beekhuyzen, Pat Bazeley · 2024 800
热门求助领域 (近24小时)
化学 材料科学 生物 医学 工程类 计算机科学 有机化学 物理 生物化学 纳米技术 复合材料 内科学 化学工程 人工智能 催化作用 遗传学 数学 基因 量子力学 物理化学
热门帖子
关注 科研通微信公众号,转发送积分 5712999
求助须知:如何正确求助?哪些是违规求助? 5213045
关于积分的说明 15269140
捐赠科研通 4864791
什么是DOI,文献DOI怎么找? 2611645
邀请新用户注册赠送积分活动 1561939
关于科研通互助平台的介绍 1519153