Survival prediction of gastric cancer by a seven-microRNA signature

内科学 医学 危险系数 肿瘤科 小RNA 癌症 多元分析 生存分析 生物 比例危险模型 置信区间 基因 生物化学
作者
Xiaofei Li,Y. Zhang,Y. Zhang,Jie Ding,Kaichun Wu,Dan Fan
出处
期刊:Gut [BMJ]
卷期号:59 (5): 579-585 被引量:328
标识
DOI:10.1136/gut.2008.175497
摘要

Aims

Several microarray studies have reported microRNA (miRNA) expression signatures that classify cancer patients into different prognostic groups. No study has evaluated the association between miRNA expression patterns and gastric cancer prognosis. In this study, we developed a seven-miRNA signature that is closely associated with survival of patients with gastric cancer.

Patients and methods

MiRNA expression profile was analysed by real-time RT-PCR in 100 gastric cancer patients, which were randomly assigned to either the training set or the testing set. Cox proportional hazard regression and risk-score analysis were used to identify a stage-independent set of seven-miRNA signature in the training set that could classify patients with significantly different prognosis. This miRNA signature was further validated by the testing set and an independent cohort 60 patients.

Results

We have identified a seven-miRNA signature (miR-10b, miR-21, miR-223, miR-338, let-7a, miR-30a-5p, miR-126) for overall survival (p=0.0009) and relapse-free survival (p=0.0005) of gastric cancer patients. Multivariate analysis shown that the risk signature was an independent predictor of overall survival (HR=3.046; 95% CI, 1.246 to 7.445, p=0.015) and relapse-free survival (HR=3.337; 95% CI, 1.298 to 8.580, p=0.012). Furthermore, the predictive value of this seven-miRNA signature was validated in the testing set of 50 patients and an independent set of 60 patients.

Conclusion

Our seven-miRNA signature is closely associated with relapse-free and overall survival among patients with gastric cancer. The prognostic signature could be applicable to future decisions concerning treatment.

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
刚刚
LiuChuannan完成签到 ,获得积分10
刚刚
嘿嘿发布了新的文献求助10
刚刚
蓦然回首完成签到,获得积分10
2秒前
浮游应助aquar1us采纳,获得10
4秒前
浮游应助aquar1us采纳,获得10
4秒前
满当当发布了新的文献求助10
4秒前
4秒前
甜甜的冰淇淋完成签到,获得积分10
5秒前
赘婿应助帅气念之采纳,获得10
5秒前
5秒前
老路完成签到,获得积分10
8秒前
8秒前
欣喜安蕾发布了新的文献求助10
8秒前
minkuuuuuuu给ZED的求助进行了留言
9秒前
9秒前
FashionBoy应助令狐凝阳采纳,获得10
9秒前
9秒前
13秒前
TEMPO发布了新的文献求助10
13秒前
mzf发布了新的文献求助10
14秒前
aquar1us完成签到,获得积分10
15秒前
爱学习的小张完成签到,获得积分10
15秒前
16秒前
Ganann完成签到 ,获得积分10
16秒前
llh发布了新的文献求助10
17秒前
长白雪茫茫完成签到,获得积分10
17秒前
酷酷蜗牛发布了新的文献求助100
18秒前
REBECCA完成签到 ,获得积分10
20秒前
小飞侠完成签到 ,获得积分10
21秒前
唯心如意完成签到,获得积分10
22秒前
Bob完成签到,获得积分10
22秒前
白智妍完成签到,获得积分10
22秒前
HOOW完成签到,获得积分10
23秒前
汉堡包应助卫青柏采纳,获得30
27秒前
30秒前
31秒前
YY土豆侠完成签到,获得积分20
31秒前
量子星尘发布了新的文献求助10
32秒前
Colin发布了新的文献求助10
32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
List of 1,091 Public Pension Profiles by Region 1581
以液相層析串聯質譜法分析糖漿產品中活性雙羰基化合物 / 吳瑋元[撰] = Analysis of reactive dicarbonyl species in syrup products by LC-MS/MS / Wei-Yuan Wu 1000
Biology of the Reptilia. Volume 21. Morphology I. The Skull and Appendicular Locomotor Apparatus of Lepidosauria 600
The Scope of Slavic Aspect 600
Foregrounding Marking Shift in Sundanese Written Narrative Segments 600
Rousseau, le chemin de ronde 500
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 生物化学 物理 纳米技术 计算机科学 内科学 化学工程 复合材料 物理化学 基因 遗传学 催化作用 冶金 量子力学 光电子学
热门帖子
关注 科研通微信公众号,转发送积分 5540269
求助须知:如何正确求助?哪些是违规求助? 4626796
关于积分的说明 14601195
捐赠科研通 4567835
什么是DOI,文献DOI怎么找? 2504244
邀请新用户注册赠送积分活动 1481913
关于科研通互助平台的介绍 1453562