Department of Neurology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China.
Department of Nuclear Medicine, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China.
J Alzheimers Dis. 2024;100(1):261-277. doi: 10.3233/JAD-240022.
Early-onset Alzheimer's disease (EOAD) exhibits a notable degree of heterogeneity as compared to late-onset Alzheimer's disease (LOAD). The proteins and pathways contributing to the pathophysiology of EOAD still need to be completed and elucidated.
Using correlation network analysis and machine learning to analyze cerebrospinal fluid (CSF) proteomics data to identify potential biomarkers and pathways associated with EOAD.
We employed mass spectrometry to conduct CSF proteomic analysis using the data-independent acquisition method in a Chinese cohort of 139 CSF samples, including 40 individuals with normal cognition (CN), 61 patients with EOAD, and 38 patients with LOAD. Correlation network analysis of differentially expressed proteins was performed to identify EOAD-associated pathways. Machine learning assisted in identifying crucial proteins differentiating EOAD. We validated the results in an Western cohort and examined the proteins expression by enzyme-linked immunosorbent assay (ELISA) in additional 9 EOAD, 9 LOAD, and 9 CN samples from our cohort.
We quantified 2,168 CSF proteins. Following adjustment for age and sex, EOAD exhibited a significantly greater number of differentially expressed proteins than LOAD compared to CN. Additionally, our data indicates that EOAD may exhibit more pronounced synaptic dysfunction than LOAD. Three potential biomarkers for EOAD were identified: SH3BGRL3, LRP8, and LY6 H, of which SH3BGRL3 also accurately classified EOAD in the Western cohort. LY6 H reduction was confirmed via ELISA, which was consistent with our proteomic results.
This study provides a comprehensive profile of the CSF proteome in EOAD and identifies three potential EOAD biomarker proteins.
早发性阿尔茨海默病（EOAD）与晚发性阿尔茨海默病（LOAD）相比表现出显著的异质性。导致 EOAD 病理生理学的蛋白质和途径仍需要进一步阐明。
使用相关网络分析和机器学习分析脑脊液（CSF）蛋白质组学数据，以确定与 EOAD 相关的潜在生物标志物和途径。
我们采用质谱法，使用数据非依赖性采集方法对来自中国的 139 例 CSF 样本进行 CSF 蛋白质组学分析，其中包括 40 例认知正常（CN）个体、61 例 EOAD 患者和 38 例 LOAD 患者。对差异表达蛋白进行相关网络分析，以鉴定与 EOAD 相关的途径。机器学习辅助鉴定区分 EOAD 的关键蛋白。我们在西方队列中验证了这些结果，并在我们的队列中另外 9 例 EOAD、9 例 LOAD 和 9 例 CN 样本中通过酶联免疫吸附试验（ELISA）检测了这些蛋白质的表达。
我们定量了 2168 种 CSF 蛋白。在调整年龄和性别后，与 CN 相比，EOAD 表现出明显更多的差异表达蛋白，与 LOAD 相比差异更显著。此外，我们的数据表明 EOAD 可能比 LOAD 表现出更明显的突触功能障碍。我们鉴定出三个潜在的 EOAD 生物标志物：SH3BGRL3、LRP8 和 LY6H，其中 SH3BGRL3 还能准确地在西方队列中分类 EOAD。通过 ELISA 验证了 LY6H 的降低，这与我们的蛋白质组学结果一致。
本研究提供了 EOAD 脑脊液蛋白质组的全面概况，并鉴定出三个潜在的 EOAD 生物标志物蛋白。
