Correlation between Cancerous Exosomes and Protein Markers Based on Surface-Enhanced Raman Spectroscopy (SERS) and Principal Component Analysis (PCA)

微泡 外体 拉曼光谱 主成分分析 化学 拉曼散射 肺癌 癌症研究 生物 病理 小RNA 医学 生物化学 基因 物理 人工智能 计算机科学 光学
作者
Hyunku Shin,Hyesun Jeong,Jaena Park,Sunghoi Hong,Yeonho Choi
出处
期刊:ACS Sensors [American Chemical Society]
卷期号:3 (12): 2637-2643 被引量:185
标识
DOI:10.1021/acssensors.8b01047
摘要

Exosomes, which are nanovesicles secreted by cells, are promising biomarkers for cancer diagnosis and prognosis, based on their specific surface protein compositions. Here, we demonstrate the correlation of nonsmall cell lung cancer (NSCLC) cell-derived exosomes and potential protein markers by unique Raman scattering profiles and principal component analysis (PCA) for cancer diagnosis. On the basis of surface enhanced Raman scattering (SERS) signals of exosomes from normal and NSCLC cells, we extracted Raman patterns of cancerous exosomes by PCA and clarified specific patterns as unique peaks through quantitative analysis with ratiometric mixtures of cancerous and normal exosomes. The unique peaks correlated well with cancerous exosome ratio ( R2 > 90%) as the unique Raman band of NSCLC exosome. To examine the origin of the unique peaks, we compared these unique peaks with characteristic Raman bands of several exosomal protein markers (CD9, CD81, EpCAM, and EGFR). EGFR had 1.97-fold similarity in Raman profiles than other markers, and it showed dominant expression against the cancerous exosomes in an immunoblotting result. We expect that these results will contribute to studies on exosomal surface protein markers for diagnosis of cancers.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
PDF的下载单位、IP信息已删除 (2025-6-4)

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
WW完成签到,获得积分10
1秒前
111发布了新的文献求助10
3秒前
深情安青应助dawda采纳,获得10
3秒前
今后应助alice采纳,获得10
3秒前
搜集达人应助TGGXS采纳,获得10
3秒前
Six_seven发布了新的文献求助10
4秒前
4秒前
大模型应助zhaoyunhang采纳,获得10
4秒前
sdniuidifod发布了新的文献求助10
4秒前
orangevv发布了新的文献求助10
5秒前
ccc发布了新的文献求助10
5秒前
不想起名字完成签到,获得积分10
6秒前
3080完成签到 ,获得积分10
6秒前
哈哈给哈哈的求助进行了留言
6秒前
浮游应助111采纳,获得10
9秒前
9秒前
酷波er应助lvzhechen采纳,获得10
10秒前
Elliot发布了新的文献求助10
10秒前
generaliu发布了新的文献求助10
10秒前
10秒前
orangevv完成签到,获得积分10
11秒前
11秒前
12秒前
bless发布了新的文献求助10
13秒前
13秒前
13秒前
今后应助zxs采纳,获得10
13秒前
BioPolaris发布了新的文献求助10
14秒前
15秒前
浮游应助ky一下采纳,获得10
16秒前
dawda发布了新的文献求助10
16秒前
17秒前
qw完成签到,获得积分20
17秒前
科研通AI6应助sdniuidifod采纳,获得10
17秒前
18秒前
18秒前
量子星尘发布了新的文献求助10
19秒前
19秒前
卧推120完成签到,获得积分10
19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
Iron toxicity and hematopoietic cell transplantation: do we understand why iron affects transplant outcome? 1500
List of 1,091 Public Pension Profiles by Region 1001
EEG in Childhood Epilepsy: Initial Presentation & Long-Term Follow-Up 500
Latent Class and Latent Transition Analysis: With Applications in the Social, Behavioral, and Health Sciences 500
On the application of advanced modeling tools to the SLB analysis in NuScale. Part I: TRACE/PARCS, TRACE/PANTHER and ATHLET/DYN3D 500
L-Arginine Encapsulated Mesoporous MCM-41 Nanoparticles: A Study on In Vitro Release as Well as Kinetics 500
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 生物化学 物理 纳米技术 计算机科学 内科学 化学工程 复合材料 物理化学 基因 遗传学 催化作用 冶金 量子力学 光电子学
热门帖子
关注 科研通微信公众号,转发送积分 5472385
求助须知:如何正确求助?哪些是违规求助? 4574678
关于积分的说明 14347789
捐赠科研通 4502046
什么是DOI,文献DOI怎么找? 2466815
邀请新用户注册赠送积分活动 1454881
关于科研通互助平台的介绍 1429206