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

作     者：Jiang, Xinrong Tao, Liye Cao, Shuo Xu, Zhengao Zheng, Shuang Zhang, Huafang Xu, Xinran Qu, Xuetong Liu, Xingyue Yu, Jiekai Chen, Xiaoming Wu, Jianmin Liang, Xiao 

作者机构：Zhejiang Univ Sir Run Run Shaw Hosp Sch Med Hangzhou 310016 Zhejiang Peoples R China Zhejiang Univ Dept Gen Surg Zhejiang Key Lab Multi Precis Diag & Treatment Liv Sir Run Run Shaw HospSch Med Hangzhou 310016 Peoples R China Zhejiang Univ Inst Analyt Chem Dept Chem Hangzhou 310058 Zhejiang Peoples R China Well Healthcare Technol Co Hangzhou 310051 Peoples R China Zhejiang Univ Biomed Res Ctr Sir Run Run Shaw Hosp Sch Med Hangzhou 310016 Zhejiang Peoples R China Taizhou First Peoples Hosp Taizhou 318020 Zhejiang Peoples R China Wuyi First Peoples Hosp Jinhua 321200 Zhejiang Peoples R China Zhejiang Univ Sch Med Key Lab Canc Prevent & Intervent Canc InstChina Natl Minist EducAffiliated Hosp 2 Hangzhou 310000 Zhejiang Peoples R China Shaoxing Univ Sch Med Shaoxing 312000 Zhejiang Peoples R China Hangzhou Med Coll Sch Basic Med Sci & Forens Med Hangzhou 310000 Zhejiang Peoples R China Zhejiang Univ Childrens Hosp Sch Med Hangzhou 310052 Peoples R China 

出 版 物：《ACS APPLIED MATERIALS & INTERFACES》 (ACS Appl. Mater. Interfaces)

年 卷 期：2025年第17卷第4期

页      面：5893-5908页

核心收录：

学科分类：0817[工学-化学工程与技术] 08[工学] 0805[工学-材料科学与工程（可授工学、理学学位）] 

基　　金：Leading Goose Project of the Science and Technology Department of Zhejiang Province [2024C03049] Major project of Health Science and Technology Program of Zhejiang Province [WKJ-ZJ-2407] National Natural Science Foundation of China [22274138, 21874118, 21575127, 82472718, 82072625] Key Project in the Agricultural and Social Development Sector of the Science and Technology Bureau of Hangzhou [20231203A09] 

主　　题：porous silicon laser desorption ionization serum metabolic fingerprinting liver disease diagnosis machine learning algorithm 

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 similar to 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.