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 工程类 病毒 地质学 海洋学
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.
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