Discovery of biomarkers for gastric cancer: A proteomics approach

生物标志物发现 癌症 蛋白质组学 生物标志物 蛋白质组 癌症生物标志物 生物 幽门螺杆菌 计算生物学 医学 生物信息学 病理 肿瘤科 内科学 基因 生物化学
作者
Li‐Ling Lin,Hsuan‐Cheng Huang,Hsueh‐Fen Juan
出处
期刊:Journal of Proteomics [Elsevier BV]
卷期号:75 (11): 3081-3097 被引量:96
标识
DOI:10.1016/j.jprot.2012.03.046
摘要

Gastric cancer is the second leading cause of cancer-related deaths worldwide. Although many treatment options exist for patients with gastric tumors, the incidence and mortality rate of gastric cancer are on the rise. The early stages of gastric cancer are non-symptomatic, and the treatment response is unpredictable. This situation is further aggravated by a lack of diagnostic biomarkers that can aid in the early detection and prognosis of gastric cancer and in the prediction of chemoresistance. Moreover, clinical surgical specimens are rarely obtained, and traditional biomarkers of gastric cancer are not very effective. Many studies in the field of proteomics have contributed to the discovery and establishment of powerful diagnostic tools (e.g., ProteinChip array) in the management of cancer. The evolution in proteomic technologies has not only enabled the screening of a large number of samples but also enabled the identification of pathologically significant proteins, such as phosphoproteins, and the quantitation of difference in protein expression under different conditions. Multiplexed assays are used widely to accurately fractionate various complex samples such as blood, tissue, cells, and Helicobacter pylori-infected specimens to identify differentially expressed proteins. Biomarker detection studies have substantially contributed to the areas of secretome, metabolome, and phosphoproteome. Here, we review the development of potential biomarkers in the natural history of gastric cancer, with specific emphasis on the characteristics of target protein convergence.
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
1秒前
2秒前
2秒前
充电宝应助结实绮琴采纳,获得10
3秒前
4秒前
4秒前
天天快乐应助林lin采纳,获得10
4秒前
5秒前
HUAWEI发布了新的文献求助10
5秒前
炒面发布了新的文献求助10
5秒前
fruchtjelly发布了新的文献求助30
7秒前
8秒前
李健应助初景采纳,获得10
8秒前
9秒前
LiLiLiLi发布了新的文献求助10
13秒前
13秒前
好好学习完成签到,获得积分20
13秒前
orixero应助科研通管家采纳,获得10
14秒前
无极微光应助科研通管家采纳,获得20
14秒前
慕青应助HUAWEI采纳,获得10
14秒前
molihuakai应助科研通管家采纳,获得10
14秒前
14秒前
默默寄柔发布了新的文献求助10
14秒前
充电宝应助科研通管家采纳,获得10
14秒前
科研通AI2S应助科研通管家采纳,获得10
14秒前
烟花应助科研通管家采纳,获得10
15秒前
寒冷悟空完成签到,获得积分10
15秒前
lizishu应助科研通管家采纳,获得10
15秒前
CipherSage应助科研通管家采纳,获得10
15秒前
英俊的铭应助科研通管家采纳,获得10
15秒前
英姑应助科研通管家采纳,获得30
15秒前
Coral完成签到,获得积分10
15秒前
科研通AI2S应助科研通管家采纳,获得10
16秒前
星辰大海应助科研通管家采纳,获得10
16秒前
深情安青应助科研通管家采纳,获得10
16秒前
16秒前
Niobium完成签到,获得积分10
16秒前
16秒前
liao应助科研通管家采纳,获得10
16秒前
16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
Principles of town planning: translating concepts to applications 1000
Management and the Arts 510
Matrix Methods in Data Mining and Pattern Recognition Second Edition 510
核安全综合知识2024版 500
Photothermal Science and Techniques 500
Digital Displacement Hydrostatic Transmission for Rotorcraft and Distributed Propulsion 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 7710767
求助须知:如何正确求助?哪些是违规求助? 9267383
关于积分的说明 20065339
捐赠科研通 7287008
什么是DOI,文献DOI怎么找? 3297036
关于科研通互助平台的介绍 2451529
邀请新用户注册赠送积分活动 2304089