An 8-ferroptosis-related genes signature from Bronchoalveolar Lavage Fluid for prognosis in patients with idiopathic pulmonary fibrosis

医学 支气管肺泡灌洗 特发性肺纤维化 肺纤维化 基因签名 纤维化 签名(拓扑) 病理 基因 免疫学 内科学 基因表达 遗传学 几何学 生物 数学
作者
Yaowu He,Yu Shang,Yupeng Li,Menghan Wang,Dihua Yu,Yi Yang,Shangwei Ning,Hong Chen
出处
期刊:BMC Pulmonary Medicine [Springer Nature]
卷期号:22 (1) 被引量:14
标识
DOI:10.1186/s12890-021-01799-7
摘要

With the rapid advances of genetic and genomic technologies, the pathophysiological mechanisms of idiopathic pulmonary fibrosis (IPF) were gradually becoming clear, however, the prognosis of IPF was still poor. This study aimed to systematically explore the ferroptosis-related genes model associated with prognosis in IPF patients.Datasets were collected from the Gene Expression Omnibus (GEO). The least absolute shrinkage and selection operator (LASSO) Cox regression analysis was applied to create a multi-gene predicted model from patients with IPF in the Freiburg cohort of the GSE70866 dataset. The Siena cohort and the Leuven cohort were used for validation.Nineteen differentially expressed genes (DEGs) between the patients with IPF and control were associated with poor prognosis based on the univariate Cox regression analysis (all P < 0.05). According to the median value of the risk score derived from an 8-ferroptosis-related genes signature, the three cohorts' patients were stratified into two risk groups. Prognosis of high-risk group (high risk score) was significantly poorer compared with low-risk group in the three cohorts. According to multivariate Cox regression analyses, the risk score was an independently predictor for poor prognosis in the three cohorts. Receiver operating characteristic (ROC) curve analysis and decision curve analysis (DCA) confirmed the signature's predictive value in the three cohorts. According to functional analysis, inflammation- and immune-related pathways and biological process could participate in the progression of IPF.These results imply that the 8-ferroptosis-related genes signature in the bronchoalveolar lavage samples might be an effective model to predict the poor prognosis of IPF.

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
NexusExplorer应助搞怪不言采纳,获得10
刚刚
科研通AI5应助一天八杯水采纳,获得10
1秒前
1秒前
1秒前
2秒前
大模型应助琪琪扬扬采纳,获得10
3秒前
丘比特应助琪琪扬扬采纳,获得10
3秒前
共享精神应助琪琪扬扬采纳,获得10
3秒前
JamesPei应助dafwfwaf采纳,获得10
3秒前
叶子完成签到,获得积分10
3秒前
xuyun完成签到,获得积分10
3秒前
脑洞疼应助木棉采纳,获得10
3秒前
GGG发布了新的文献求助10
3秒前
zena92完成签到,获得积分10
4秒前
4秒前
听风发布了新的文献求助10
5秒前
一一发布了新的文献求助10
5秒前
CC完成签到,获得积分20
6秒前
7秒前
时生111完成签到 ,获得积分10
7秒前
kb发布了新的文献求助10
8秒前
dafwfwaf完成签到,获得积分20
8秒前
Snow完成签到 ,获得积分10
9秒前
9秒前
CC发布了新的文献求助10
9秒前
小苏打完成签到,获得积分10
10秒前
Xiaoxiao应助程琳采纳,获得10
10秒前
ycc完成签到 ,获得积分10
10秒前
畏寒的北完成签到,获得积分10
11秒前
爆米花应助单纯的雅香采纳,获得10
11秒前
俭朴的玉兰完成签到 ,获得积分10
11秒前
12秒前
12秒前
13秒前
13秒前
13秒前
adazbd发布了新的文献求助10
13秒前
Jenny应助木头人采纳,获得10
13秒前
ATAYA完成签到,获得积分10
14秒前
14秒前
高分求助中
Continuum Thermodynamics and Material Modelling 3000
Production Logging: Theoretical and Interpretive Elements 2700
Social media impact on athlete mental health: #RealityCheck 1020
Ensartinib (Ensacove) for Non-Small Cell Lung Cancer 1000
Unseen Mendieta: The Unpublished Works of Ana Mendieta 1000
Bacterial collagenases and their clinical applications 800
El viaje de una vida: Memorias de María Lecea 800
热门求助领域 (近24小时)
化学 材料科学 生物 医学 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 基因 遗传学 物理化学 催化作用 量子力学 光电子学 冶金
热门帖子
关注 科研通微信公众号,转发送积分 3527961
求助须知:如何正确求助?哪些是违规求助? 3108159
关于积分的说明 9287825
捐赠科研通 2805882
什么是DOI,文献DOI怎么找? 1540070
邀请新用户注册赠送积分活动 716926
科研通“疑难数据库(出版商)”最低求助积分说明 709808