材料科学
石墨烯
胰腺导管腺癌
介孔材料
胰腺癌
癌症研究
纳米技术
内科学
医学
癌症
生物
生物化学
催化作用
作者
Jia Qi,Caiyun Fang,Chunhui Deng,Fangying Shi,Qunyan Yao
标识
DOI:10.1021/acsami.5c03176
摘要
Pancreatic ductal adenocarcinoma (PDAC) is a highly aggressive and lethal cancer, typically diagnosed at advanced stages due to its asymptomatic onset and challenges in early detection. To address the critical need for the early diagnosis of PDAC, we developed a laser desorption/ionization mass spectrometry (LDI-MS) platform based on mesoporous silica-modified magnetic graphene (MG@mSiO2). MG@mSiO2 exhibited exceptional ultraviolet (UV) absorption, efficient ionization, and minimal background interference, enabling high-resolution profiling of serum metabolic fingerprints (SMFs). Based on the extracted SMFs, we constructed a Random Forest (RF) model to classify PDAC patients, high-risk (HR) individuals, and healthy controls (HC), achieving an accuracy of 97.5% in the independent test set. Additionally, a six-metabolite biomarker panel was identified, showing strong diagnostic potential with sensitivity and accuracy exceeding 89.1% for distinguishing HC from PDAC. When coupled with the serological marker carbohydrate antigen 19-9 (CA19-9), the integrated strategy delivered significantly improved diagnostic performance, achieving high accuracy ranging from 95.3% to 100% in distinguishing HR and PDAC patients from HC. Furthermore, metabolic pathway analysis revealed key pathways associated with PDAC progression, providing mechanistic insights into the disease. This work provides a powerful diagnostic tool for PDAC screening, establishing a foundation for early detection and precision medicine in clinical practice.
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