Metabolomics and its Application to the Development of Clinical Laboratory Tests for Prostate Cancer.

前列腺癌 代谢组学 医学 疾病 生物标志物发现 癌症生物标志物 癌症 前列腺 代谢组 恶性肿瘤 生物信息学 计算生物学 肿瘤科 蛋白质组学 内科学 生物 基因 生物化学
作者
Jonathan E. McDunn,Steven M. Stirdivant,Lisa A. Ford,Robert L. Wolfert
出处
期刊:PubMed 卷期号:26 (2): 92-104 被引量:11
链接
标识
摘要

There is a critical need to develop clinical laboratory assays that provide risk assessment for men at elevated risk for prostate cancer, and once diagnosed, could further identify those men with clinically significant disease.Recent advancements in analytical instrumentation have enabled mass spectrometry-based metabolomics methodologies. Further advancements in chromatographic techniques have facilitated high throughput, quantitative assays for a broad spectrum of biochemicals.Screening metabolomics techniques have been applied to biospecimens from large cohorts of men comparing those individuals with prostate cancer to those with no evidence of malignancy. Work beginning in tissues has identified biochemical profiles that correlate with disease and disease severity, including tumor grade and stage. Some of these metabolic abnormalities, such as dramatic elevations in sarcosine, have been found to translate into biological fluids, especially blood and urine, which can be sampled in a minimally invasive manner.The differential abundances of these tumor-associated metabolites have been found to improve the performance of clinical prognostic/diagnostic tools.The outlook is bright for metabolomic technology to address clinical diagnostic needs for prostate cancer patient management. Early validation of specific clinical tests provides a preview of further successes in this area. Metabolomics has shown its utility to complement and augment traditional clinical approaches as well as emerging genomic, transcriptomic and proteomic methodologies, and is expected to play a key role in the precision medicine-based management of the prostate cancer patient.

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
北74完成签到,获得积分20
1秒前
2秒前
2秒前
yang完成签到,获得积分10
2秒前
CC发布了新的文献求助10
2秒前
3秒前
柏特瑞发布了新的文献求助10
3秒前
feather完成签到,获得积分10
4秒前
科研通AI5应助zzzzz采纳,获得10
4秒前
5秒前
wangn发布了新的文献求助10
5秒前
fei2009xue应助小羊羊采纳,获得10
5秒前
白方明发布了新的文献求助10
5秒前
klio发布了新的文献求助10
6秒前
SYLH应助友好的储采纳,获得10
7秒前
科研通AI5应助color采纳,获得30
8秒前
8秒前
CC完成签到,获得积分10
8秒前
英姑应助敏感的曼香采纳,获得10
9秒前
曾经的采波完成签到 ,获得积分10
9秒前
10秒前
Yii完成签到,获得积分10
10秒前
12秒前
12秒前
甜筒超好吃完成签到,获得积分10
12秒前
白方明发布了新的文献求助10
13秒前
NexusExplorer应助苏木采纳,获得10
13秒前
14秒前
14秒前
桐桐应助幼柚采纳,获得10
14秒前
天天发布了新的文献求助10
15秒前
kyrie完成签到 ,获得积分10
15秒前
阔达的惠完成签到,获得积分10
16秒前
16秒前
媛小媛啊发布了新的文献求助10
16秒前
16秒前
孟龙威完成签到,获得积分10
16秒前
大模型应助潇洒莞采纳,获得10
16秒前
高分求助中
Continuum thermodynamics and material modelling 3000
Production Logging: Theoretical and Interpretive Elements 2500
Healthcare Finance: Modern Financial Analysis for Accelerating Biomedical Innovation 2000
Applications of Emerging Nanomaterials and Nanotechnology 1111
Covalent Organic Frameworks 1000
Les Mantodea de Guyane Insecta, Polyneoptera 1000
Theory of Block Polymer Self-Assembly 750
热门求助领域 (近24小时)
化学 医学 材料科学 生物 工程类 有机化学 生物化学 纳米技术 内科学 物理 化学工程 计算机科学 复合材料 基因 遗传学 物理化学 催化作用 细胞生物学 免疫学 电极
热门帖子
关注 科研通微信公众号,转发送积分 3483178
求助须知:如何正确求助?哪些是违规求助? 3072587
关于积分的说明 9127119
捐赠科研通 2764162
什么是DOI,文献DOI怎么找? 1516962
邀请新用户注册赠送积分活动 701873
科研通“疑难数据库(出版商)”最低求助积分说明 700737