ArticleJournal of Alzheimer's disease reports
Exploring BSCL2 and associated genes in Alzheimer's disease by integrative analysis of bioinformatics, sn-RNAseq and machine learning approach.
Article in Journal of Alzheimer's disease reports. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Background: Alzheimer's disease (AD) is a progressive neurodegenerative disorder characterized by significant cognitive decline and memory impairment. This condition imposes a considerable economic burden on healthcare systems worldwide. Objective: Current diagnostic approaches often lack specificity and sensitivity, necessitating innovative methods to identify potential biomarkers that could facilitate earlier intervention and improved patient outcomes. Methods: In this study, we aimed to elucidate the role of the BSCL2 gene in the pathogenesis of AD by employing various bioinformatics techniques, including transcriptomic analysis and single-nucleus RNA sequencing (sn-RNAseq). We also integrated machine learning algorithms to identify potential biomarkers associated with AD. A weighted gene co-expression network was constructed to uncover co-expression modules linked to BSCL2, alongside AGPAT1 and EHD2, which demonstrated promising diagnostic potential with area under the curve (AUC) values exceeding 0.7. Results: Our analyses revealed significant alterations in immune cell profiles across different AD subtypes, providing insights into personalized immunotherapy approaches. Furthermore, pathway enrichment analyses highlighted key biological processes involved in AD, including oxidative phosphorylation, neuroactive ligand-receptor interactions, and Notch signaling pathways. Notably, sn-RNAseq data indicated that BSCL2-related gene activity exhibited significant changes in neural lineages, suggesting its influence on neurodegenerative mechanisms. Conclusions: This study underscores the potential of BSCL2, AGPAT1, and EHD2 as novel biomarkers for early detection of Alzheimer's disease. The insights gained from the co-expression analyses and immune profiling pave the way for personalized therapeutic strategies aimed at modulating the immune response in AD. Future research should focus on the clinical validation of these biomarkers and their role in the development of targeted interventions, ultimately enhancing our understanding of AD pathophysiology and improving patient management.
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