Myrou Athena, Barmpagiannos Konstantinos, Ioakimidou Aliki, Savopoulos Christos
Department of Internal Medicine, American Hellenic Educational Progressive Association (AHEPA) University Hospital, 54636 Thessaloniki, Greece.
Microbiology Laboratory, Department of Immunology, American Hellenic Educational Progressive Association (AHEPA) University Hospital, 54636 Thessaloniki, Greece.
Int J Mol Sci. 2025 Mar 1;26(5):2231. doi: 10.3390/ijms26052231.
Neurological diseases contribute significantly to disability and mortality, necessitating improved diagnostic and prognostic tools. Advances in molecular biomarkers at genomic, transcriptomic, epigenomic, and proteomic levels have facilitated early disease detection. Notably, neurofilament light chain (NfL) serves as a key biomarker of neurodegeneration, while liquid biopsy techniques enable non-invasive monitoring through exosomal tau, α-synuclein, and inflammatory markers. Artificial intelligence (AI) and multi-omics integration further enhance biomarker discovery, promoting precision medicine. A comprehensive literature review was conducted using PubMed, Scopus, and Web of Science to identify studies (2010-2024) on molecular biomarkers in neurodegenerative and neuroinflammatory disorders. Key findings on genomic mutations, transcriptomic signatures, epigenetic modifications, and protein-based biomarkers were analyzed. The findings highlight the potential of liquid biopsy and multi-omics approaches in improving diagnostic accuracy and therapeutic stratification. Genomic, transcriptomic, and proteomic markers demonstrate utility in early detection and disease monitoring. AI-driven analysis enhances biomarker discovery and clinical application. Despite advancements, challenges remain in biomarker validation, standardization, and clinical implementation. Large-scale longitudinal studies are essential to ensure reliability. AI-powered multi-omics analysis may accelerate biomarker application, ultimately improving patient outcomes in neurological diseases.
神经系统疾病对残疾和死亡率有重大影响，因此需要改进诊断和预后工具。基因组、转录组、表观基因组和蛋白质组水平上分子生物标志物的进展促进了疾病的早期检测。值得注意的是，神经丝轻链（NfL）是神经退行性变的关键生物标志物，而液体活检技术能够通过外泌体tau、α-突触核蛋白和炎症标志物进行非侵入性监测。人工智能（AI）和多组学整合进一步增强了生物标志物的发现，推动了精准医学的发展。使用PubMed、Scopus和Web of Science进行了全面的文献综述，以确定（2010 - 2024年）关于神经退行性和神经炎症性疾病中分子生物标志物的研究。分析了基因组突变、转录组特征、表观遗传修饰和基于蛋白质的生物标志物的关键发现。这些发现突出了液体活检和多组学方法在提高诊断准确性和治疗分层方面的潜力。基因组、转录组和蛋白质组标志物在早期检测和疾病监测中显示出实用性。人工智能驱动的分析增强了生物标志物的发现和临床应用。尽管取得了进展，但在生物标志物的验证、标准化和临床实施方面仍然存在挑战。大规模纵向研究对于确保可靠性至关重要。人工智能驱动的多组学分析可能会加速生物标志物的应用，最终改善神经系统疾病患者的预后。
