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的填写文献信息,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!

Pan-cancer single-cell landscape of drug-metabolizing enzyme genes

基因 生物 肿瘤微环境 药品 癌症 药物遗传学 细胞 癌细胞 癌症研究 药物反应 药物代谢 药理学 遗传学 计算生物学 生物化学 基因型
作者
Wei Mao,Tao Zhou,Feng Zhang,Maoxiang Qian,Xie Jianqiang,Zhengyan Li,Yang Shu,Yuan Li,Heng Xu
出处
期刊:Pharmacogenetics and Genomics [Lippincott Williams & Wilkins]
被引量:1
标识
DOI:10.1097/fpc.0000000000000538
摘要

Objective Varied expression of drug-metabolizing enzymes (DME) genes dictates the intensity and duration of drug response in cancer treatment. This study aimed to investigate the transcriptional profile of DMEs in tumor microenvironment (TME) at single-cell level and their impact on individual responses to anticancer therapy. Methods Over 1.3 million cells from 481 normal/tumor samples across 9 solid cancer types were integrated to profile changes in the expression of DME genes. A ridge regression model based on the PRISM database was constructed to predict the influence of DME gene expression on drug sensitivity. Results Distinct expression patterns of DME genes were revealed at single-cell resolution across different cancer types. Several DME genes were highly enriched in epithelial cells (e.g. GPX2, TST and CYP3A5 ) or different TME components (e.g. CYP4F3 in monocytes). Particularly, GPX2 and TST were differentially expressed in epithelial cells from tumor samples compared to those from normal samples. Utilizing the PRISM database, we found that elevated expression of GPX2, CYP3A5 and reduced expression of TST was linked to enhanced sensitivity of particular chemo-drugs (e.g. gemcitabine, daunorubicin, dasatinib, vincristine, paclitaxel and oxaliplatin). Conclusion Our findings underscore the varied expression pattern of DME genes in cancer cells and TME components, highlighting their potential as biomarkers for selecting appropriate chemotherapy agents.
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
刚刚
asir_xw发布了新的文献求助10
3秒前
MagicTerran完成签到,获得积分10
3秒前
xiaowuge完成签到 ,获得积分10
5秒前
菲菲发布了新的文献求助10
5秒前
lk发布了新的文献求助10
5秒前
核桃应助白茶采纳,获得10
8秒前
孙燕应助小文采纳,获得10
8秒前
wanci应助小城故事和冰雨采纳,获得10
9秒前
11秒前
11秒前
Orange应助Tiam采纳,获得10
12秒前
demmeretock发布了新的文献求助10
15秒前
CodeCraft应助科研通管家采纳,获得10
16秒前
16秒前
科研通AI5应助科研通管家采纳,获得10
16秒前
情怀应助科研通管家采纳,获得10
16秒前
16秒前
华仔应助科研通管家采纳,获得10
16秒前
17秒前
17秒前
17秒前
17秒前
机灵柚子应助苏比努尔采纳,获得10
17秒前
19秒前
CipherSage应助拉长的傲菡采纳,获得10
19秒前
烟花应助拉长的傲菡采纳,获得10
19秒前
烟花应助拉长的傲菡采纳,获得10
19秒前
SYLH应助拉长的傲菡采纳,获得10
20秒前
专注青槐应助拉长的傲菡采纳,获得10
20秒前
传奇3应助拉长的傲菡采纳,获得10
20秒前
半柚应助拉长的傲菡采纳,获得10
20秒前
冰魂应助拉长的傲菡采纳,获得10
20秒前
科研通AI5应助的订单采纳,获得10
21秒前
21秒前
酷炫思天完成签到,获得积分20
21秒前
武大帝77完成签到 ,获得积分10
22秒前
852应助demmeretock采纳,获得10
23秒前
24秒前
田様应助Okayoooooo采纳,获得10
25秒前
高分求助中
Thinking Small and Large 500
Algorithmic Mathematics in Machine Learning 500
Advances in Underwater Acoustics, Structural Acoustics, and Computational Methodologies 400
Genome Editing and Engineering: From TALENs, ZFNs and CRISPRs to Molecular Surgery 300
Getting Published in SSCI Journals: 200+ Questions and Answers for Absolute Beginners 300
The Monocyte-to-HDL ratio (MHR) as a prognostic and diagnostic biomarker in Acute Ischemic Stroke: A systematic review with meta-analysis (P9-14.010) 240
幼儿游戏与指导(第二版) 200
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 物理 生物化学 纳米技术 计算机科学 化学工程 内科学 复合材料 物理化学 电极 遗传学 量子力学 基因 冶金 催化作用
热门帖子
关注 科研通微信公众号,转发送积分 3833509
求助须知:如何正确求助?哪些是违规求助? 3375984
关于积分的说明 10491339
捐赠科研通 3095536
什么是DOI,文献DOI怎么找? 1704424
邀请新用户注册赠送积分活动 820037
科研通“疑难数据库(出版商)”最低求助积分说明 771721