Quantitative proteomics revealed protein biomarkers to distinguish malignant pleural effusion from benign pleural effusion

胸腔积液 恶性胸腔积液 蛋白质组学 医学 病理 胸膜液 定量蛋白质组学 放射科 生物 生物化学 基因
作者
Tingyan Dong,Yueming Liang,Hui Chen,Yanling Li,Zhiping Li,Xinglin Gao
出处
期刊:Journal of Proteomics [Elsevier BV]
卷期号:302: 105201-105201 被引量:4
标识
DOI:10.1016/j.jprot.2024.105201
摘要

To identify protein biomarkers capable of early prediction regarding the distinguishing malignant pleural effusion (MPE) from benign pleural effusion (BPE) in patients with lung disease. A four-dimensional data independent acquisition (4D-DIA) proteomic was performed to determine the differentially expressed proteins in samples from 20 lung adenocarcinoma MPE and 30 BPE. The significantly differential expressed proteins were selected for Gene Ontology (GO) enrichment and Kyoto Encyclopaedia of Genes and Genomes (KEGG) pathway analysis. Protein biomarkers with high capability to discriminate MPE from BPE patients were identified by Random Forest (RF) algorithm prediction model, whose diagnostic and prognostic efficacy in primary tumors were further explored in public datasets, and were validated by ELISA experiment. 50 important proteins (30 up-regulated and 20 down-regulated) were selected out as potential markers to distinguish the MPE from BPE group. GO analysis revealed that those proteins involving the most important cell component is extracellular space. KEGG analysis identified the involvement of cellular adhesion molecules pathway. Furthermore, the Area Under Curve (AUC) of these proteins were ranged from 0.717 to 1.000,with excellent diagnostic properties to distinguish the MPE. Finally, significant survival and gene and protein expression analysis demonstrated BPIFB1, DPP4, HPRT1 and ABI3BP had high discriminating values. We performed a 4D-DIA proteomics to determine the differentially expressed proteins in pleural effusion samples from MPE and BPE. Some potential protein biomarkers were identified to distinguish the MPE from BPE patients., which may provide helpful diagnostic and therapeutic insights for lung cancer. This is significant because the median survival time of patients with MPE is usually 4–12 months, thus, it is particularly important to diagnose MPE early to start treatments promptly. The most common causes of MPE are lung cancers, while pneumonia and tuberculosis are the main causes of BPE. If more diagnostic markers could be identified periodically, there would be an important significance to clinical diagnose and treatment with drugs in lung cancer patients.
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
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
一丁雨完成签到,获得积分10
刚刚
logic完成签到,获得积分10
1秒前
小方发布了新的文献求助10
2秒前
2秒前
史迪奇大王完成签到,获得积分10
8秒前
浮游应助科研通管家采纳,获得10
8秒前
今后应助科研通管家采纳,获得10
8秒前
CipherSage应助科研通管家采纳,获得20
8秒前
李健应助科研通管家采纳,获得10
8秒前
科研通AI5应助科研通管家采纳,获得10
8秒前
ephore应助科研通管家采纳,获得150
8秒前
Solarenergy完成签到,获得积分0
8秒前
大模型应助科研通管家采纳,获得10
8秒前
ephore应助科研通管家采纳,获得150
8秒前
orixero应助科研通管家采纳,获得10
9秒前
浮游应助科研通管家采纳,获得10
9秒前
科研通AI2S应助科研通管家采纳,获得10
9秒前
ephore应助科研通管家采纳,获得150
9秒前
CipherSage应助科研通管家采纳,获得10
9秒前
科研通AI6应助科研通管家采纳,获得150
9秒前
若ruofeng应助科研通管家采纳,获得20
9秒前
若ruofeng应助科研通管家采纳,获得20
9秒前
领导范儿应助科研通管家采纳,获得10
9秒前
小蘑菇应助科研通管家采纳,获得10
9秒前
10秒前
研友_VZG7GZ应助科研通管家采纳,获得10
10秒前
ephore应助科研通管家采纳,获得150
10秒前
10秒前
科研通AI6应助科研通管家采纳,获得10
10秒前
子车茗应助科研通管家采纳,获得30
10秒前
哟梦完成签到,获得积分10
10秒前
清爽难敌发布了新的文献求助10
10秒前
张北海完成签到,获得积分10
10秒前
小白i完成签到,获得积分10
12秒前
心斋完成签到,获得积分10
12秒前
量子星尘发布了新的文献求助10
13秒前
byr完成签到 ,获得积分10
17秒前
18秒前
浩浩浩完成签到,获得积分10
18秒前
流英关注了科研通微信公众号
20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
Acute Mountain Sickness 2000
Cowries - A Guide to the Gastropod Family Cypraeidae 1200
Handbook of Milkfat Fractionation Technology and Application, by Kerry E. Kaylegian and Robert C. Lindsay, AOCS Press, 1995 1000
Why Neuroscience Matters in the Classroom 500
The Affinity Designer Manual - Version 2: A Step-by-Step Beginner's Guide 500
Affinity Designer Essentials: A Complete Guide to Vector Art: Your Ultimate Handbook for High-Quality Vector Graphics 500
热门求助领域 (近24小时)
化学 医学 生物 材料科学 工程类 有机化学 内科学 生物化学 物理 计算机科学 纳米技术 遗传学 基因 复合材料 化学工程 物理化学 病理 催化作用 免疫学 量子力学
热门帖子
关注 科研通微信公众号,转发送积分 5046342
求助须知:如何正确求助?哪些是违规求助? 4275513
关于积分的说明 13327433
捐赠科研通 4089550
什么是DOI,文献DOI怎么找? 2237798
邀请新用户注册赠送积分活动 1244906
关于科研通互助平台的介绍 1173084