Identification of mitochondrial-related genes as potential biomarkers for the subtyping and prediction of Alzheimer’s disease

Abstract

Introduction: Alzheimer’s disease (AD) is a progressive and debilitating neurodegenerative disorder prevalent among older adults. Although AD symptoms can be managed through certain treatments, advancing the understanding of underlying disease mechanisms and developing effective therapies is critical. Methods: In this study, we systematically analyzed transcriptome data from temporal lobes of healthy individuals and patients with AD to investigate the relationship between AD and mitochondrial autophagy. Machine learning algorithms were used to identify six genes—FUNDC1, MAP1LC3A, CSNK2A1, VDAC1, CSNK2B, and ATG5—for the construction of an AD prediction model. Furthermore, AD was categorized into three subtypes through consensus clustering analysis. Results: The identified genes are closely linked to the onset and progression of AD and can serve as reliable biomarkers. The differences in gene expression, clinical features, immune infiltration, and pathway enrichment were examined among the three AD subtypes. Potential drugs for the treatment of each subtype were also identified. Discussion: The findings observed in the present study can help to deepen the understanding of the underlying disease mechanisms of AD and enable the development of precision medicine and personalized treatment approaches.