已入深夜,您辛苦了!由于当前在线用户较少,发布求助请尽量完整地填写文献信息,科研通机器人24小时在线,伴您度过漫漫科研夜!祝你早点完成任务,早点休息,好梦!

lncRNA profile study reveals the mRNAs and lncRNAs associated with docetaxel resistance in breast cancer cells

多西紫杉醇 紫杉醇 乳腺癌 紫杉烷 癌症研究 生物 癌症 肿瘤科 医学 遗传学
作者
Peide Huang,Fengyu Li,Lin Li,Yuling You,Shizhi Luo,Dong Zhou,Qiang Gao,Song Wu,Nils Brünner,Jan Stenvang
出处
期刊:Scientific Reports [Nature Portfolio]
卷期号:8 (1) 被引量:50
标识
DOI:10.1038/s41598-018-36231-4
摘要

Resistance to adjuvant systemic treatment, including taxanes (docetaxel and paclitaxel) is a major clinical problem for breast cancer patients. lncRNAs (long non-coding RNAs) are non-coding transcripts, which have recently emerged as important players in a variety of biological processes, including cancer development and chemotherapy resistance. However, the contribution of lncRNAs to docetaxel resistance in breast cancer and the relationship between lncRNAs and taxane-resistance genes are still unclear. Here, we performed comprehensive RNA sequencing and analyses on two docetaxel-resistant breast cancer cell lines (MCF7-RES and MDA-RES) and their docetaxel-sensitive parental cell lines. We identified protein coding genes and pathways that may contribute to docetaxel resistance. More importantly, we identified lncRNAs that were consistently up-regulated or down-regulated in both the MCF7-RES and MDA-RES cells. The co-expression network and location analyses pinpointed four overexpressed lncRNAs located within or near the ABCB1 (ATP-binding cassette subfamily B member 1) locus, which might up-regulate the expression of ABCB1. We also identified the lncRNA EPB41L4A-AS2 (EPB41L4A Antisense RNA 2) as a potential biomarker for docetaxel sensitivity. These findings have improved our understanding of the mechanisms underlying docetaxel resistance in breast cancer and have provided potential biomarkers to predict the response to docetaxel in breast cancer patients.

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
黄瓜双耳拌腐竹完成签到,获得积分10
1秒前
2秒前
YUAN发布了新的文献求助10
3秒前
YUAN发布了新的文献求助10
3秒前
我是老大应助loopy采纳,获得10
3秒前
hhhjy发布了新的文献求助30
4秒前
rave发布了新的文献求助30
4秒前
4秒前
4秒前
5秒前
5秒前
六六六完成签到 ,获得积分10
6秒前
bkagyin应助LQL采纳,获得30
7秒前
杨德帅发布了新的文献求助10
9秒前
YUAN发布了新的文献求助10
9秒前
YUAN发布了新的文献求助10
9秒前
YUAN发布了新的文献求助10
9秒前
xiaomaxia完成签到,获得积分10
11秒前
12秒前
科研通AI6.4应助老仙翁采纳,获得10
13秒前
科研通AI6.4应助老仙翁采纳,获得10
13秒前
科研通AI6.4应助老仙翁采纳,获得10
13秒前
科研通AI6.4应助老仙翁采纳,获得10
14秒前
科研通AI6.4应助老仙翁采纳,获得10
14秒前
NexusExplorer应助老仙翁采纳,获得10
14秒前
科研通AI6.2应助老仙翁采纳,获得10
14秒前
14秒前
星辰大海应助老仙翁采纳,获得10
14秒前
科研通AI6.2应助老仙翁采纳,获得10
14秒前
可爱的函函应助老仙翁采纳,获得10
14秒前
斯文的菲音完成签到,获得积分10
15秒前
文澜清禾完成签到 ,获得积分10
15秒前
15秒前
16秒前
17秒前
18秒前
19秒前
荷包蛋不吃猫完成签到 ,获得积分10
19秒前
LQL发布了新的文献求助30
19秒前
orange发布了新的文献求助10
22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
Rosenblum, Global Change Biology 800
Essentials of Carbohydrate Chemistry and Biochemistry, 4th Edition 800
Organizational Behavior 510
Management and the Arts 510
Matrix Methods in Data Mining and Pattern Recognition Second Edition 510
Physiologic specialization in Peronospora manshurica 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 计算机科学 化学工程 工程类 有机化学 物理 复合材料 生物化学 内科学 细胞生物学 基因 遗传学 免疫学 冶金 光电子学 癌症研究
热门帖子
关注 科研通微信公众号,转发送积分 7777672
求助须知:如何正确求助?哪些是违规求助? 9318513
关于积分的说明 20364691
捐赠科研通 7364587
什么是DOI,文献DOI怎么找? 3318990
关于科研通互助平台的介绍 2466628
邀请新用户注册赠送积分活动 2334211