Identification of the immune-related biomarkers in Behcet’s disease by plasma proteomic analysis

免疫系统 接收机工作特性 医学 免疫学 计算生物学 肿瘤科 内科学 生物 生物信息学
作者
Huan Liu,Panpan Zhang,Fuzhen Li,Xiao Xiao,Yinan Zhang,Na Li,Liping Du,Peizeng Yang
出处
期刊:Arthritis Research & Therapy [Springer Nature]
卷期号:25 (1) 被引量:5
标识
DOI:10.1186/s13075-023-03074-y
摘要

Abstract Background This study aimed to investigate the expression profile of immune response-related proteins of Behcet’s disease (BD) patients and identify potential biomarkers for this disease. Methods Plasma was collected from BD patients and healthy controls (HC). Immune response-related proteins were measured using the Olink Immune Response Panel. Differentially expressed proteins (DEPs) were used to construct prediction models via five machine learning algorithms: naive Bayes, support vector machine, extreme gradient boosting, random forest, and neural network. The prediction performance of the five models was assessed using the area under the curve (AUC) value, recall (sensitivity), specificity, precision, accuracy, F1 score, and residual distribution. Subtype analysis of BD was performed using the consensus clustering method. Results Proteomics results showed 43 DEPs between BD patients and HC ( P < 0.05). These DEPs were mainly involved in the Toll-like receptor 9 and NF-κB signaling pathways. Five models were constructed using DEPs [interleukin 10 (IL10), Fc receptor like 3 (FCRL3), Mannan-binding lectin serine peptidase 1 (MASP1), NF2, moesin-ezrin-radixin like (MERLIN) tumor suppressor (NF2), FAM3 metabolism regulating signaling molecule B (FAM3B), and O-6-methylguanine-DNA methyltransferase (MGMT)]. Among these models, the neural network model showed the best performance (AUC = 0.856, recall: 0.692, specificity: 0.857, precision: 0.900, accuracy: 0.750, F1 score: 0.783). BD patients were divided into two subtypes according to the consensus clustering method: one with high disease activity in association with higher expression of tripartite motif-containing 5 (TRIM5), SH2 domain-containing 1A (SH2D1A), phosphoinositide-3-kinase adaptor protein 1 (PIK3AP1), hematopoietic cell-specific Lyn substrate 1 (HCLS1), and DNA fragmentation factor subunit alpha (DFFA) and the other with low disease activity in association with higher expression of C–C motif chemokine ligand 11 (CCL11). Conclusions Our study not only revealed a distinctive immune response-related protein profile for BD but also showed that IL10, FCRL3, MASP1, NF2, FAM3B, and MGMT could serve as potential immune biomarkers for this disease. Additionally, a novel molecular disease classification model was constructed to identify subsets of BD.
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
大幅提高文件上传限制,最高150M (2024-4-1)

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
斯文寄松完成签到 ,获得积分10
2秒前
..完成签到 ,获得积分10
3秒前
lichen发布了新的文献求助10
4秒前
Jun完成签到 ,获得积分10
7秒前
NXZNXZ完成签到 ,获得积分10
9秒前
fanconi完成签到 ,获得积分10
11秒前
大个应助Kevin采纳,获得10
12秒前
曾珍完成签到 ,获得积分10
12秒前
lichen完成签到,获得积分10
13秒前
14秒前
爱听歌寄云完成签到 ,获得积分10
14秒前
Kaives完成签到 ,获得积分10
16秒前
英俊的铭应助Bonnie采纳,获得10
19秒前
哆啦A梦完成签到,获得积分10
19秒前
风雨无阻发布了新的文献求助10
20秒前
21秒前
脑洞疼应助戈壁滩的鱼采纳,获得10
22秒前
谨慎纸飞机完成签到,获得积分10
23秒前
drift完成签到,获得积分10
23秒前
风趣的惜天完成签到 ,获得积分10
24秒前
性温雅完成签到 ,获得积分10
24秒前
顾矜应助勤恳依霜采纳,获得10
25秒前
Kevin发布了新的文献求助10
27秒前
28秒前
28秒前
风雨无阻完成签到,获得积分10
29秒前
Bonnie完成签到,获得积分20
31秒前
32秒前
菠萝炒蛋加饭完成签到 ,获得积分10
32秒前
研友_O8Wz4Z完成签到,获得积分10
33秒前
执着易形发布了新的文献求助10
33秒前
浅忆完成签到 ,获得积分10
33秒前
未設定发布了新的文献求助10
33秒前
打打应助研友_LMBAXn采纳,获得10
36秒前
lianliyou应助科研通管家采纳,获得10
37秒前
小二郎应助科研通管家采纳,获得10
37秒前
lianliyou应助科研通管家采纳,获得10
37秒前
wanci应助科研通管家采纳,获得10
37秒前
lianliyou应助科研通管家采纳,获得10
37秒前
meng完成签到,获得积分10
38秒前
高分求助中
Sustainability in Tides Chemistry 2000
Microlepidoptera Palaearctica, Volumes 1 and 3 - 13 (12-Volume Set) [German] 1122
Дружба 友好报 (1957-1958) 1000
The Data Economy: Tools and Applications 1000
Mantiden - Faszinierende Lauerjäger – Buch gebraucht kaufen 700
PraxisRatgeber Mantiden., faszinierende Lauerjäger. – Buch gebraucht kaufe 700
A Dissection Guide & Atlas to the Rabbit 600
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 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 基因 遗传学 催化作用 物理化学 免疫学 量子力学 细胞生物学
热门帖子
关注 科研通微信公众号,转发送积分 3099819
求助须知:如何正确求助?哪些是违规求助? 2751306
关于积分的说明 7612410
捐赠科研通 2403104
什么是DOI,文献DOI怎么找? 1275188
科研通“疑难数据库(出版商)”最低求助积分说明 616276
版权声明 599053