Thymidylate Synthase Expression and Prognosis in Colorectal Cancer: A Meta-Analysis of Colorectal Cancer Survival Data

作者
Yao Chen,Cuihua Yi,Lian Liu,Bei Li,Yawei Wang,Xiuwen Wang
出处
期刊:International Journal of Biological Markers [SAGE Publishing]
卷期号:27 (3): 203-211 被引量:10
标识
DOI:10.5301/jbm.2012.9584
摘要

BACKGROUND: Although many studies have investigated the prognostic effect of thymidylate synthase (TS) in colorectal cancer, no consensus has been reached. The aim of this meta-analysis was to obtain a more precise estimate of the prognostic significance of TS expression in localized cancers treated by curative resection and adjuvant chemotherapy. MATERIALS AND METHOD: Seventeen eligible studies reporting survival in 2,893 patients stratified by TS expression were pooled using a fixed- or random-effects model. The main outcome measure was hazard ratio (HR). RESULTS: The overall HR for overall survival was 1.01 (95% CI 0.74-1.39, p=0.947), with an I2 of 64.4%. The total HR for disease-free survival was 1.36 (95% CI 0.97-1.89, p=0.072), with an I2 of 75.8%. In the TS protein-tested subgroup, the total HR for disease-free survival was 1.72 (95% CI 1.02-2.89, p=0.042), with an I2 of 81.3%. CONCLUSION: Our meta-analysis showed that, in the adjuvant setting, TS expression does not predict a poorer disease-free survival or a worse overall survival. Therefore, we believe that it is inappropriate to regard TS expression as a prognostic factor for patients with stage II and stage III colorectal cancer treated by surgery and adjuvant chemotherapy.

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
微笑的忆枫完成签到 ,获得积分10
1秒前
我要的飞翔完成签到,获得积分10
4秒前
4秒前
幽默跳跳糖完成签到 ,获得积分10
5秒前
minkeyantong完成签到 ,获得积分10
5秒前
背后的幻巧完成签到,获得积分10
5秒前
starry发布了新的文献求助10
5秒前
Nole应助乐乐ovo采纳,获得10
5秒前
飞龙在天完成签到 ,获得积分10
6秒前
西瓜完成签到,获得积分10
6秒前
7秒前
完美世界应助食量大如牛采纳,获得10
8秒前
Cassie完成签到,获得积分0
8秒前
8秒前
8秒前
9秒前
10秒前
考拉发布了新的文献求助10
12秒前
12秒前
氧气瑞发布了新的文献求助10
13秒前
JinLy发布了新的文献求助10
14秒前
南瓜气气完成签到,获得积分10
14秒前
15秒前
刘qqqqq发布了新的文献求助10
15秒前
ymh完成签到,获得积分10
16秒前
wlq发布了新的文献求助10
17秒前
外向烤鸡发布了新的文献求助10
17秒前
17秒前
无糖果粒橙应助端庄亿先采纳,获得10
18秒前
张欢馨应助HDY采纳,获得10
19秒前
bbihk完成签到,获得积分10
19秒前
19秒前
liubowen发布了新的文献求助20
19秒前
徐若楠发布了新的文献求助10
20秒前
21秒前
开心小兔子完成签到 ,获得积分10
21秒前
21秒前
俊逸白萱完成签到,获得积分20
21秒前
Itazu发布了新的文献求助10
22秒前
白石人家应助幸福水儿采纳,获得10
23秒前
高分求助中
Markov Chain Monte Carlo 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
Common Foundations of American and East Asian Modernisation: From Alexander Hamilton to Junichero Koizumi 5000
How to Use Machine Learning in Chemistry: An Introduction 1000
Matrix Methods in Data Mining and Pattern Recognition Second Edition 510
Discerning Saints: Moralization of Intrinsic Motivation and Selective Prosociality at Work 500
Handbuch Trainingswissenschaft – Trainingslehre 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 7580965
求助须知:如何正确求助?哪些是违规求助? 9160358
关于积分的说明 19598825
捐赠科研通 7163470
什么是DOI,文献DOI怎么找? 3265939
关于科研通互助平台的介绍 2430880
邀请新用户注册赠送积分活动 2257015