ArticleMolecular neurobiology2024
Identification of Autophagy-Related Candidate Genes in the Early Diagnosis of Alzheimer's Disease and Exploration of Potential Molecular Mechanisms.
Article in Molecular neurobiology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Role of Pentacyclic Triterpenes in the Management of Neurological Disorders: An Insight into Molecular Mechanisms and Therapeutic Approaches.Molecular neurobiology · 2026Review
- Autophagy and Cellular Senescence in Alzheimer's Disease: Key Drivers of Neurodegeneration.CNS neuroscience & therapeutics · 2025Review
- Programmed cell death signatures-driven microglial transformation in Alzheimer's disease: single-cell transcriptomics and functional validation.Frontiers in immunology · 2025Article
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Authors and funding
7 authors.
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Abstract
This study aimed to identify autophagy-related candidate genes for the early diagnosis of Alzheimer's disease (AD) and elucidate their potential molecular mechanisms. Differentially expressed genes (DEGs) and phenotype-associated significant module genes were obtained using the "limma" package and weighted gene co-expression network analysis (WGCNA) based on hippocampal tissue datasets from AD patients and control samples. The intersection between the list of autophagy-related genes (ATGs), DEGs, and module genes was further investigated to obtain AD-autophagy-related differential expression genes (ATDEGs). Subsequently, the least absolute shrinkage and selection operator (LASSO) algorithm was utilized to identify hub genes, and a second intersection was performed with important module genes from the protein-protein interaction (PPI) network to obtain co-hub genes. Finally, a diagnostic model was constructed by receiver operating characteristic (ROC) analysis to determine the candidate genes with high diagnostic efficacy in the external validation set. Moreover, immune infiltration analysis was performed on AD patient brain tissues and explore the correlation between candidate genes and immune cells. We further analyzed the expression level of candidate genes in the SH-SY5Y cells with Aβ
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