Serum peptide profiling: identifying novel cancer biomarkers for early disease detection.

癌症 生物标志物 计算生物学 医学 生物信息学 生物 内科学 生物化学
作者
Andrew J. Martorella,Richard J. Robbins
出处
期刊:PubMed 卷期号:78 Suppl 1: 123-8 被引量:2
链接
标识
摘要

Recent advances in mass spectrometry have enabled the identification of hundreds of low molecular weight (LMW) peptides that have previously been difficult to detect in human serum. Serum peptide patterns can now be analyzed using commercially available statistical programs to identify potential peptide patterns that may correlate with the presence or absence of specific diseases. A serum peptide profile (SPP), which is unique to each patient, can be created and compared to a known SPP from a specific disease. The SPP thus serves as a potential early stage biomarker prior to the clinical manifestation of disease. A unique and automated technology platform has been developed by members of the Protein Center at Memorial Sloan-Kettering Cancer Center (MSKCC). It involves a magnetic bead-based approach to extract LMW peptides from serum, placing them by robotic automation on a stainless steel MALDI-TOF target plate, subjecting them to mass spectrometric analysis, and using GeneSpring software to analyze the peptide patterns. Human serum from a cohort of 27 patients with metastatic thyroid cancer and 32 controls were analyzed on the MSKCC platform. 549 individual LMW peptides were identified. A SPP composed of 98 discriminatory LMW peptides was able to distinguish between the two groups of serum samples with high statistical accuracy. We believe that our automated system will serve as a model for future biotechnology laboratories in the quest for hidden diagnostic clues that may be detected by simply analyzing a drop of blood.

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
滴滴滴完成签到,获得积分10
刚刚
怕黑的樱完成签到 ,获得积分10
刚刚
张i鹅完成签到,获得积分10
刚刚
檀靓发布了新的文献求助10
1秒前
1秒前
寻找布冯发布了新的文献求助10
1秒前
陈小青完成签到 ,获得积分10
1秒前
幸福中心完成签到,获得积分10
2秒前
瀚泛完成签到,获得积分10
2秒前
2秒前
3秒前
Robin完成签到,获得积分20
3秒前
zhou完成签到,获得积分20
3秒前
4秒前
4秒前
4秒前
杨志坚完成签到 ,获得积分0
5秒前
5秒前
斯文败类应助iiii采纳,获得10
5秒前
跳跃的凌文完成签到 ,获得积分10
5秒前
6秒前
Davic发布了新的文献求助10
6秒前
zhuwg完成签到,获得积分20
6秒前
刺猬完成签到,获得积分10
7秒前
li发布了新的文献求助10
7秒前
7秒前
7秒前
岳岳岳发布了新的文献求助10
7秒前
jouholly完成签到,获得积分10
8秒前
8秒前
章山蝶发布了新的文献求助10
9秒前
LaLaC发布了新的文献求助10
9秒前
可靠夜绿发布了新的文献求助10
9秒前
11秒前
了0完成签到 ,获得积分10
11秒前
香蕉觅云应助Robin采纳,获得10
11秒前
发一篇sci完成签到 ,获得积分10
11秒前
12秒前
聂难敌发布了新的文献求助10
12秒前
12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
Zeolites: From Fundamentals to Emerging Applications 1500
Architectural Corrosion and Critical Infrastructure 1000
Early Devonian echinoderms from Victoria (Rhombifera, Blastoidea and Ophiocistioidea) 1000
Hidden Generalizations Phonological Opacity in Optimality Theory 1000
Comprehensive Computational Chemistry 2023 800
2026国自然单细胞多组学大红书申报宝典 800
热门求助领域 (近24小时)
化学 医学 生物 材料科学 工程类 有机化学 内科学 生物化学 物理 计算机科学 纳米技术 遗传学 基因 复合材料 化学工程 物理化学 病理 催化作用 免疫学 量子力学
热门帖子
关注 科研通微信公众号,转发送积分 4911831
求助须知:如何正确求助?哪些是违规求助? 4187185
关于积分的说明 13003332
捐赠科研通 3955152
什么是DOI,文献DOI怎么找? 2168569
邀请新用户注册赠送积分活动 1187064
关于科研通互助平台的介绍 1094301