Porous Silicon Particle-Assisted Mass Spectrometry Technology Unlocks Serum Metabolic Fingerprints in the Progression From Chronic Hepatitis B to Hepatocellular Carcinoma

材料科学 肝细胞癌 质谱法 多孔硅 粒子(生态学) 多孔性 慢性肝炎 化学工程 纳米技术 色谱法 癌症研究 冶金 医学 化学 复合材料 病毒学 海洋学 病毒 工程类 地质学
作者
Xinrong Jiang,Liye Tao,Shuo Cao,Zhengao Xu,Shuang Zheng,Huafang Zhang,Xinran Xu,Xuetong Qu,Xingyue Liu,Jiekai Yu,Xiaoming Chen,Jianmin Wu,Xiao Liang
出处
期刊:ACS Applied Materials & Interfaces [American Chemical Society]
标识
DOI:10.1021/acsami.4c17563
摘要

Hepatocellular carcinoma (HCC) is a common malignancy and generally develops from liver cirrhosis (LC), which is primarily caused by the chronic hepatitis B (CHB) virus. Reliable liquid biopsy methods for HCC screening in high-risk populations are urgently needed. Here, we establish a porous silicon-assisted laser desorption ionization mass spectrometry (PSALDI-MS) technology to profile metabolite information hidden in human serum in a high throughput manner. Serum metabolites can be captured in the pore channel of APTES-modified porous silicon (pSi) particles and well-preserved during storage or transportation. Furthermore, serum metabolites captured in the APTES-pSi particles can be directly detected on the LDI-MS without the addition of an organic matrix, thus greatly accelerating the acquisition of metabolic fingerprints of serum samples. The PSALDI-MS displays the capability of high throughput (5 min per 96 samples), high reproducibility (coefficient of variation <15%), high sensitivity (LOD ∼ 1 pmol), and high tolerance to background salt and proteins. In a multicenter cohort study, 1433 subjects including healthy controls (HC), CHB, LC, and HCC volunteers were enrolled and nontargeted serum metabolomic analysis was performed on the PSALDI-MS platform. After the selection of feature metabolites, a stepwise diagnostic model for the classification of different liver disease stages was constructed by the machine learning algorithm. In external testing, the accuracy of 91.2% for HC, 71.4% for CHB, 70.0% for LC, and 95.3% for HCC was achieved by chemometrics. Preliminary studies indicated that the diagnostic model constructed from serum metabolic fingerprint also displays good predictive performance in a prospective observation. We believe that the combination of PSALDI-MS technology and machine learning may serve as an efficient tool in clinical practice.
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
大模型应助土豆淀粉采纳,获得10
刚刚
hhllhh啊完成签到,获得积分10
刚刚
1秒前
1秒前
1秒前
3秒前
积极小全完成签到,获得积分20
5秒前
111完成签到,获得积分10
6秒前
花无缺发布了新的文献求助10
6秒前
linjiandefeng发布了新的文献求助10
6秒前
6秒前
时舒发布了新的文献求助30
7秒前
WYY完成签到,获得积分10
7秒前
7秒前
奋斗初南完成签到,获得积分10
8秒前
8秒前
9秒前
11秒前
知夏完成签到,获得积分20
12秒前
宁学者发布了新的文献求助10
12秒前
拼搏的代玉完成签到,获得积分10
13秒前
14秒前
sharon发布了新的文献求助10
14秒前
跳跃凡桃发布了新的文献求助10
14秒前
知夏发布了新的文献求助10
15秒前
夏尔发布了新的文献求助10
16秒前
17秒前
土豆淀粉发布了新的文献求助10
19秒前
zhui发布了新的文献求助10
19秒前
完美世界应助YLS采纳,获得10
20秒前
hsbuuwqbdubeq发布了新的文献求助10
20秒前
香蕉孤风完成签到,获得积分10
20秒前
moonlimb完成签到 ,获得积分10
22秒前
香蕉觅云应助香蕉孤风采纳,获得30
24秒前
善养完成签到,获得积分10
24秒前
Xethan发布了新的文献求助20
24秒前
yy发布了新的文献求助10
25秒前
可怜的游戏完成签到,获得积分10
26秒前
乐观德地完成签到,获得积分10
26秒前
28秒前
高分求助中
Continuum Thermodynamics and Material Modelling 3000
Production Logging: Theoretical and Interpretive Elements 2700
Mechanistic Modeling of Gas-Liquid Two-Phase Flow in Pipes 2500
Structural Load Modelling and Combination for Performance and Safety Evaluation 1000
Conference Record, IAS Annual Meeting 1977 720
電気学会論文誌D(産業応用部門誌), 141 巻, 11 号 510
Typology of Conditional Constructions 500
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 3565440
求助须知:如何正确求助?哪些是违规求助? 3138424
关于积分的说明 9426703
捐赠科研通 2838813
什么是DOI,文献DOI怎么找? 1560572
邀请新用户注册赠送积分活动 729695
科研通“疑难数据库(出版商)”最低求助积分说明 717589