Identification and prediction of m7G-related Alzheimer’s disease subtypes: insights from immune infiltration and machine learning models

作者：Chao Ma, Jian Li, Yuhua Chi, Xuan Sun, Maoquan Yang, Xueqin Sui · 发表于：Frontiers in Aging Neuroscience · 年份：2023 · DOI：10.3389/fnagi.2023.1161068 · 被引用次数：13 · 研究领域：RNA modifications and cancer、Ferroptosis and cancer prognosis、MicroRNA in disease regulation

Introduction: Alzheimer's disease (AD) is a complex and progressive neurodegenerative disorder that primarily affects older individuals. N7-methylguanosine (m7G) is a common RNA chemical modification that impacts the development of numerous diseases. Thus, our work investigated m7G-related AD subtypes and established a predictive model. Methods: The datasets for AD patients, including GSE33000 and GSE44770, were obtained from the Gene Expression Omnibus (GEO) database, which were derived from the prefrontal cortex of the brain. We performed differential analysis of m7G regulators and examined the immune signatures differences between AD and matched-normal samples. Consensus clustering was employed to identify AD subtypes based on m7G-related differentially expressed genes (DEGs), and immune signatures were explored among different clusters. Furthermore, we developed four machine learning models based on the expression profiles of m7G-related DEGs and identified five important genes from the optimal model. We evaluated the predictive power of the 5-gene-based model using an external AD dataset (GSE44770). Results: A total of 15 genes related to m7G were found to be dysregulated in patients with AD compared to non-AD patients. This finding suggests that there are differences in immune characteristics between these two groups. Based on the differentially expressed m7G regulators, we categorized AD patients into two clusters and calculated the ESTIMATE score for each cluster. Clu...