Research on RNA modification in disease diagnosis and prognostic biomarkers: current status and challenges

Abstract

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.

Keywords: RNA modifications; biomarkers; disease diagnosis; machine learning; prognostic models.

Figure 1

Relationships between AI, machine learning, deep learning, and statistical methods. AI algorithms include machine learning and deep learning algorithms. Machine learning algorithms commonly used to identify RNA modification-related disease biomarkers mainly include supervised learning and unsupervised learning.

Figure 2

Regulatory mechanisms of RNA modifications in disease occurrence and progression. ‘Writers’ catalyze the addition of modifications to RNA, ‘erasers’ remove these modifications, and ‘readers’ recognize the modified RNA and recruit the appropriate molecular machinery during translation. These modification regulators change the modification state of the target gene RNA, affecting RNA metabolism, thereby altering signaling pathways and cell phenotypes, and ultimately affecting disease onset and progression.

Figure 3

Workflow for using machine learning to identify RNA modification related genes as diagnostic or prognostic biomarkers for diseases.