Identification of volatile biomarkers for lung cancer from different histological sources: A comprehensive study

肺癌 乙醛 化学 癌变 癌症 生物化学 癌症研究 医学 内科学 乙醇
作者
Wei Lv,Wenmin Shi,Zhijuan Zhang,Lihua Ru,Weisheng Feng,Hanxiao Tang,Xiangqi Wang
出处
期刊:Analytical Biochemistry [Elsevier]
卷期号:690: 115527-115527 被引量:2
标识
DOI:10.1016/j.ab.2024.115527
摘要

The identification of noninvasive volatile biomarkers for lung cancer is a significant clinical challenge. Through in vitro studies, the recognition of altered metabolism in cell volatile organic compound (VOC) emitting profile, along with the occurrence of oncogenesis, provides insight into the biochemical pathways involved in the production and metabolism of lung cancer volatile biomarkers. In this research, for the first time, a comprehensive comparative analysis of the volatile metabolites in NSCLS cells (A549), SCLC cells (H446), lung normal cells (BEAS-2B), as well as metabolites in both the oxidative stress (OS) group and control group. Specifically, the combination of eleven VOCs, including n-dodecane, acetaldehyde, isopropylbenzene, p-ethyltoluene and cis-1,3-dichloropropene, exhibited potential as volatile biomarkers for lung cancer originating from two different histological sources. Furthermore, the screening process in A549 cell lines resulted in the identification of three exclusive biomarkers, isopropylbenzene, formaldehyde and bromoform. Similarly, the exclusive biomarkers 1,2,4-trimethylbenzene, p-ethyltoluene, and cis-1,3-dichloropropene were present in the H446 cell line. Additionally, significant changes in trans-2-pentene, acetaldehyde, 1,2,4-trimethylbenzene, and bromoform were observed, indicating a strong association with OS. These findings highlight the potential of volatile biomarkers profiling as a means of noninvasive identification for lung cancer diagnosis.

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
刚刚
celinewu完成签到,获得积分10
1秒前
Aippan完成签到,获得积分10
1秒前
量子星尘发布了新的文献求助10
2秒前
田様应助呆萌太清采纳,获得20
2秒前
上guanguan完成签到,获得积分10
3秒前
哭泣战斗机应助kawaiikid采纳,获得10
3秒前
香蕉君达完成签到,获得积分10
3秒前
3秒前
我行我素完成签到 ,获得积分10
3秒前
不会写论文的小蜜蜂完成签到 ,获得积分10
4秒前
4秒前
4秒前
读书明智发布了新的文献求助10
4秒前
脆脆鲨发布了新的文献求助10
4秒前
4秒前
4秒前
起床了吗发布了新的文献求助10
4秒前
lzj发布了新的文献求助10
4秒前
zsy完成签到,获得积分10
5秒前
5秒前
zyyy发布了新的文献求助10
5秒前
汉堡包应助heiye采纳,获得10
6秒前
6秒前
6秒前
高级丹药师发布了新的文献求助100
6秒前
123完成签到,获得积分10
7秒前
7秒前
英俊的铭应助drwzm采纳,获得10
7秒前
hodge完成签到,获得积分10
7秒前
8秒前
英姑应助nanno采纳,获得10
8秒前
8秒前
今后应助fantastic采纳,获得10
9秒前
wang完成签到,获得积分10
9秒前
qq完成签到,获得积分10
9秒前
ELEGENCE发布了新的文献求助10
9秒前
杨洋发布了新的文献求助10
9秒前
qinshimigyue发布了新的文献求助10
9秒前
研友_VZG7GZ应助晴天采纳,获得10
10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
Introduction to strong mixing conditions volume 1-3 5000
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
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 5719050
求助须知:如何正确求助?哪些是违规求助? 5254852
关于积分的说明 15287660
捐赠科研通 4869006
什么是DOI,文献DOI怎么找? 2614559
邀请新用户注册赠送积分活动 1564435
关于科研通互助平台的介绍 1521807