N6-Methyladenosine RNA modification in cerebrospinal fluid as a novel potential diagnostic biomarker for progressive multiple sclerosis

• Published in: Journal of translational medicine Vol. 19; no. 1; pp. 1 - 316
• Main Authors: Ye, Fei, Wang, Tianzhu, Wu, Xiaoxin, Liang, Jie, Li, Jiaoxing, Sheng, Wenli
• Format: Journal Article
• Language: English
• Published: London BioMed Central Ltd 22.07.2021; BioMed Central; BMC
• Subjects: Algorithms; Alzheimer's disease; Biomarkers; Cerebrospinal fluid; Cerebrospinal fluid (CSF); Classification; Clustering; Datasets; Development and progression; Diagnosis; Diagnostic biomarker; DNA methylation; Gene expression; Genetic aspects; Health aspects; Medical prognosis; Multiple sclerosis; N6-methyladenosine; N6-methyladenosine (m6A); Neurodegeneration; Neurological diseases; Parkinson's disease; Patients; Physiological aspects; Polymerase chain reaction; Post-translational modification; Principal components analysis; Progressive multiple sclerosis (PMS); Proteins; RNA; RNA modification; Support vector machines; Young adults; China
• ISSN: 1479-5876; 1479-5876
• DOI: 10.1186/s12967-021-02981-5

Abstract Background Progressive multiple sclerosis (PMS) is an uncommon and severe subtype of MS that worsens gradually and leads to irreversible disabilities in young adults. Currently, there are no applicable or reliable biomarkers to distinguish PMS from relapsing–remitting multiple sclerosis (RRMS). Previous studies have demonstrated that dysfunction of N 6-methyladenosine (m6A) RNA modification is relevant to many neurological disorders. Thus, the aim of this study was to explore the diagnostic biomarkers for PMS based on m6A regulatory genes in the cerebrospinal fluid (CSF). Methods Gene expression matrices were downloaded from the ArrayExpress database. Then, we identified differentially expressed m6A regulatory genes between MS and non-MS patients. MS clusters were identified by consensus clustering analysis. Next, we analyzed the correlation between clusters and clinical characteristics. The random forest (RF) algorithm was applied to select key m6A-related genes. The support vector machine (SVM) was then used to construct a diagnostic gene signature. Receiver operating characteristic (ROC) curves were plotted to evaluate the accuracy of the diagnostic model. In addition, CSF samples from MS and non-MS patients were collected and used for external validation, as evaluated by an m6A RNA Methylation Quantification Kit and by real-time quantitative polymerase chain reaction. Results The 13 central m6A RNA methylation regulators were all upregulated in MS patients when compared with non-MS patients. Consensus clustering analysis identified two clusters, both of which were significantly associated with MS subtypes. Next, we divided 61 MS patients into a training set (n = 41) and a test set (n = 20). The RF algorithm identified eight feature genes, and the SVM method was successfully applied to construct a diagnostic model. ROC curves revealed good performance. Finally, the analysis of 11 CSF samples demonstrated that RRMS samples exhibited significantly higher levels of m6A RNA methylation and higher gene expression levels of m6A-related genes than PMS samples. Conclusions The dynamic modification of m6A RNA methylation is involved in the progression of MS and could potentially represent a novel CSF biomarker for diagnosing MS and distinguishing PMS from RRMS in the early stages of the disease.