Shi Hua, Li Zhouying, Zou Quan, Yang Hui
School of Opto-electronic and Communication Engineering, Xiamen University of Technology, Ligong Road, Jimei District, Xiamen, Fujian 361024, China.
Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Chengdian Road, Kecheng District, Quzhou, Zhejiang 324000, China.
Brief Bioinform. 2025 Jul 2;26(4). doi: 10.1093/bib/bbaf361.
RNA modification, as a crucial post-transcriptional regulatory mechanism, plays a pivotal role in normal physiological processes and is closely associated with the onset and progression of various human diseases. Recent studies have highlighted significant alterations in the level of RNA modifications, including m6A, m6Am, m1A, m5C, m7G, ac4C, Ψ, and A-to-I editing, across multiple diseases. These findings suggest the potential of RNA modifications and their regulatory factors as biomarkers for early disease diagnosis and prognosis. This review provides an overview of statistical methods, machine learning techniques employed in identifying disease diagnostic and prognostic biomarkers, along with relevant evaluation metrics and bioinformatics tools. We further explore the types of common RNA modifications, the modifying proteins involved, and the underlying mechanisms of modification. The focus of this paper is on the application of machine learning algorithms in discovering RNA modification-related biomarkers, particularly for disease diagnosis and prognosis. By reviewing recent advancements in the identification of disease biomarkers, and analyzing the prospects and challenges of their clinical application, we aim to offer insights into the mining methods of RNA modifications and their associated factors as disease diagnostic or prognostic biomarkers, providing a valuable reference for future research and clinical practice.
RNA修饰作为一种关键的转录后调控机制，在正常生理过程中发挥着核心作用，并且与多种人类疾病的发生和发展密切相关。最近的研究强调了在多种疾病中RNA修饰水平的显著变化，包括m6A、m6Am、m1A、m5C、m7G、ac4C、Ψ和A-to-I编辑。这些发现表明RNA修饰及其调控因子作为早期疾病诊断和预后生物标志物的潜力。本综述概述了用于识别疾病诊断和预后生物标志物的统计方法、机器学习技术，以及相关的评估指标和生物信息学工具。我们进一步探讨了常见RNA修饰的类型、涉及的修饰蛋白以及修饰的潜在机制。本文的重点是机器学习算法在发现RNA修饰相关生物标志物方面的应用，特别是用于疾病诊断和预后。通过回顾疾病生物标志物识别的最新进展，并分析其临床应用的前景和挑战，我们旨在深入了解RNA修饰及其相关因子作为疾病诊断或预后生物标志物的挖掘方法，为未来的研究和临床实践提供有价值的参考。
