Diagnostic and Prognostic Value of microRNAs in Patients with Laryngeal Cancer: A Systematic Review

医学 肿瘤科 小RNA 疾病 内科学 癌症 系统回顾 头颈部癌 阶段(地层学) 生物信息学 梅德林 生物 生物化学 基因 古生物学
作者
Elisabetta Broseghini,Daria Maria Filippini,Laura Fabbri,Roberta Leonardi,Andi Abeshi,Davide Dal Molin,Matteo Fermi,Manuela Ferracin,Ignacio Javier Fernandez
出处
期刊:Non-Coding RNA [Multidisciplinary Digital Publishing Institute]
卷期号:9 (1): 9-9 被引量:5
标识
DOI:10.3390/ncrna9010009
摘要

Laryngeal squamous cell cancer (LSCC) is one of the most common malignant tumors of the head and neck region, with a poor survival rate (5-year overall survival 50–80%) as a consequence of an advanced-stage diagnosis and high recurrence rate. Tobacco smoking and alcohol abuse are the main risk factors of LSCC development. An early diagnosis of LSCC, a prompt detection of recurrence and a more precise monitoring of the efficacy of different treatment modalities are currently needed to reduce the mortality. Therefore, the identification of effective diagnostic and prognostic biomarkers for LSCC is crucial to guide disease management and improve clinical outcomes. In the past years, a dysregulated expression of small non-coding RNAs, including microRNAs (miRNAs), has been reported in many human cancers, including LSCC, and many miRNAs have been explored for their diagnostic and prognostic potential and proposed as biomarkers. We searched electronic databases for original papers that were focused on miRNAs and LSCC, using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocol. According to the outcome, 566 articles were initially screened, of which 177 studies were selected and included in the analysis. In this systematic review, we provide an overview of the current literature on the function and the potential diagnostic and prognostic role of tissue and circulating miRNAs in LSCC.

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
大本完成签到,获得积分10
1秒前
1秒前
bioseraph发布了新的文献求助10
2秒前
SJ完成签到,获得积分10
3秒前
喵喵完成签到,获得积分10
3秒前
MM完成签到 ,获得积分10
3秒前
3秒前
Hypnos发布了新的文献求助10
4秒前
脑洞疼应助浮生采纳,获得10
4秒前
molihuakai应助momo采纳,获得10
5秒前
棉花完成签到 ,获得积分10
6秒前
6秒前
桐桐应助lhy采纳,获得10
6秒前
chenjh完成签到,获得积分20
6秒前
Aki完成签到,获得积分20
7秒前
彭于晏应助zhoumuyun采纳,获得10
7秒前
煎蛋完成签到,获得积分10
8秒前
哈哈发布了新的文献求助10
8秒前
皮皮团完成签到 ,获得积分10
8秒前
tjfwg发布了新的文献求助10
9秒前
YI_JIA_YI完成签到,获得积分10
9秒前
年轻孤萍完成签到,获得积分10
10秒前
10秒前
大鲁完成签到,获得积分10
10秒前
蚊子发布了新的文献求助20
11秒前
11秒前
guoxihan发布了新的文献求助10
11秒前
11秒前
现代大米完成签到,获得积分10
11秒前
12秒前
huai发布了新的文献求助10
12秒前
fjy完成签到,获得积分10
14秒前
圣人海完成签到,获得积分10
14秒前
WXY发布了新的文献求助10
14秒前
好运常在发布了新的文献求助10
15秒前
科研通AI6.2应助chenjh采纳,获得10
16秒前
打打应助内向的凝芙采纳,获得20
16秒前
17秒前
卢小白发布了新的文献求助10
17秒前
伊酒发布了新的文献求助10
17秒前
高分求助中
Overcoming Stigma and Bias in Obesity Management 800
Malcolm Fraser : a biography 700
Signals, Systems, and Signal Processing 610
Materials selection in mechanical design 500
Bounds for Statistical Estimation in Semiparametric Models 500
Climate change and sports: Statistics report on climate change and sports 500
Forced degradation and stability indicating LC method for Letrozole: A stress testing guide 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 物理 内科学 复合材料 催化作用 物理化学 光电子学 电极 细胞生物学 基因 无机化学
热门帖子
关注 科研通微信公众号,转发送积分 6476938
求助须知:如何正确求助?哪些是违规求助? 8279147
关于积分的说明 17656018
捐赠科研通 5558965
什么是DOI,文献DOI怎么找? 2910712
邀请新用户注册赠送积分活动 1887687
关于科研通互助平台的介绍 1741013