Chen Rouan, Wu Yuxuan, Fang Yiming, Lan Tian, Shi Wei
Medical College of Ophthalmology and Optometry, Shandong University of Traditional Chinese Medicine, 250355 Jinan, Shandong, China.
The Second Clinical Medical School of Nanjing Medical University, 211166 Nanjing, Jiangsu, China.
Discov Med. 2024 Dec;36(191):2356-2364. doi: 10.24976/Discov.Med.202436191.217.
Age-related macular degeneration (AMD) is a significant factor causing blindness in adults. However, the clinical diagnosis of AMD is relatively challenging, due to the shortcomings of the existing clinical examination methods and the latent period of retinal damage before macular degeneration becomes apparent. This study aims to explore the potential of extracellular vesicles (EVs) protein chips for early diagnosis of AMD using patients' plasma samples.
To achieve early diagnosis of AMD, this study utilized a high-throughput platform for liquid biopsy based on EVs protein chips. Forty AMD patients and 41 normal individuals were recruited. Through machine learning methods, we identified that ATP-binding cassette transporter A1 (ABCA1) is an EVs protein marker for diagnosing AMD. Additionally, a validation set was constructed using the random forest method for verification.
The results of the study indicated that ABCA1 is a reliable biomarker for diagnosing AMD. The validation using the random forest method confirmed the robustness and reliability of ABCA1 as a diagnostic marker. This finding suggested that ABCA1 can serve as a new promising liquid biopsy-based marker for diagnosing macular degeneration.
The utilization of EVs protein chips, combined with machine learning methods, can effectively identify ABCA1 as a biomarker for the early diagnosis of AMD. This approach offers a promising new method for liquid biopsy diagnostics, potentially improving the clinical diagnosis and management of macular degeneration.
年龄相关性黄斑变性(AMD)是导致成年人失明的一个重要因素。然而,由于现有临床检查方法的不足以及黄斑变性明显之前视网膜损伤的潜伏期,AMD的临床诊断相对具有挑战性。本研究旨在利用患者血浆样本探索细胞外囊泡(EVs)蛋白芯片在AMD早期诊断中的潜力。
为实现AMD的早期诊断,本研究利用了基于EVs蛋白芯片的液体活检高通量平台。招募了40例AMD患者和41例正常个体。通过机器学习方法,我们确定ATP结合盒转运蛋白A1(ABCA1)是诊断AMD的一种EVs蛋白标志物。此外,使用随机森林方法构建了一个验证集进行验证。
研究结果表明ABCA1是诊断AMD的可靠生物标志物。使用随机森林方法进行的验证证实了ABCA1作为诊断标志物的稳健性和可靠性。这一发现表明ABCA1可作为一种基于液体活检的有前景的新型黄斑变性诊断标志物。
EVs蛋白芯片与机器学习方法相结合,能够有效识别ABCA1作为AMD早期诊断的生物标志物。这种方法为液体活检诊断提供了一种有前景的新方法,有可能改善黄斑变性的临床诊断和管理。