Recent developments in mass-spectrometry-based targeted proteomics of clinical cancer biomarkers

蛋白质组学 生物标志物发现 生物标志物 癌症生物标志物 定量蛋白质组学 癌症 医学 计算生物学 工作流程 生物信息学 计算机科学 生物 内科学 生物化学 数据库 基因
作者
Deborah Wenk,Charlotte Zuo,Thomas Kislinger,Lusia Sepiashvili
出处
期刊:Clinical Proteomics [BioMed Central]
卷期号:21 (1): 6-6 被引量:50
标识
DOI:10.1186/s12014-024-09452-1
摘要

Abstract Routine measurement of cancer biomarkers is performed for early detection, risk classification, and treatment monitoring, among other applications, and has substantially contributed to better clinical outcomes for patients. However, there remains an unmet need for clinically validated assays of cancer protein biomarkers. Protein tumor markers are of particular interest since proteins carry out the majority of biological processes and thus dynamically reflect changes in cancer pathophysiology. Mass spectrometry-based targeted proteomics is a powerful tool for absolute peptide and protein quantification in biological matrices with numerous advantages that make it attractive for clinical applications in oncology. The use of liquid chromatography-tandem mass spectrometry (LC–MS/MS) based methodologies has allowed laboratories to overcome challenges associated with immunoassays that are more widely used for tumor marker measurements. Yet, clinical implementation of targeted proteomics methodologies has so far been limited to a few cancer markers. This is due to numerous challenges associated with paucity of robust validation studies of new biomarkers and the labor-intensive and operationally complex nature of LC–MS/MS workflows. The purpose of this review is to provide an overview of targeted proteomics applications in cancer, workflows used in targeted proteomics, and requirements for clinical validation and implementation of targeted proteomics assays. We will also discuss advantages and challenges of targeted MS-based proteomics assays for clinical cancer biomarker analysis and highlight some recent developments that will positively contribute to the implementation of this technique into clinical laboratories.
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
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
刚刚
科目三应助ly采纳,获得10
1秒前
1秒前
2秒前
啦啦啦完成签到,获得积分10
2秒前
Zhengkeke发布了新的文献求助10
2秒前
3秒前
淡淡的如曼完成签到,获得积分10
3秒前
长情冷菱完成签到,获得积分10
3秒前
朱春枝发布了新的文献求助10
3秒前
5秒前
冰冰发布了新的文献求助10
6秒前
xxxx发布了新的文献求助10
6秒前
白衣卿相完成签到,获得积分10
7秒前
林白发布了新的文献求助10
8秒前
8秒前
Ruby发布了新的文献求助10
9秒前
田様应助犹豫路灯采纳,获得10
9秒前
白衣卿相发布了新的文献求助10
10秒前
haha发布了新的文献求助10
10秒前
小芭乐完成签到 ,获得积分10
10秒前
11秒前
11秒前
hhhh发布了新的文献求助10
11秒前
CBLASER完成签到 ,获得积分10
12秒前
薛定谔的猫完成签到,获得积分10
12秒前
12秒前
13秒前
遇见发布了新的文献求助10
13秒前
13秒前
所所应助依旧采纳,获得30
14秒前
XaSevak完成签到,获得积分20
14秒前
阿玖完成签到 ,获得积分10
15秒前
斯文败类应助无二三采纳,获得30
15秒前
酷波er应助熙熙攘攘采纳,获得10
16秒前
风清扬发布了新的文献求助30
16秒前
16秒前
17秒前
壮观灭绝发布了新的文献求助10
17秒前
高分求助中
Principles of Economics, 11th Edition 10000
University Physics with Modern Physics, 16th edition 10000
(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
Environmental Leverage in Times of Climate Crisis: Product Standards, Carbon Border Measures and Preferential Trade Agreements 1000
Matrix Methods in Data Mining and Pattern Recognition 510
Social Skills Improvement System-Rating Scales--Chinese Version 500
Dynamische Polarisation von H-1 und B-11 in (CH-3)-3NBH-3 500
热门求助领域 (近24小时)
化学 材料科学 医学 生物 纳米技术 工程类 有机化学 化学工程 生物化学 计算机科学 内科学 物理 复合材料 催化作用 细胞生物学 无机化学 光电子学 物理化学 电极 基因
热门帖子
关注 科研通微信公众号,转发送积分 7243408
求助须知:如何正确求助?哪些是违规求助? 8867663
关于积分的说明 18706012
捐赠科研通 6917719
什么是DOI,文献DOI怎么找? 3196581
关于科研通互助平台的介绍 2370231
邀请新用户注册赠送积分活动 2171207