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Identification of Autophagy-Related Candidate Genes in the Early Diagnosis of Alzheimer’s Disease and Exploration of Potential Molecular Mechanisms

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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 Aβ25–35 (25 µM). Among the 17 identified AD-ATDEGs, ATP6V1E1 stood out with area under the curve (AUC) values of 0.869, 0.817, and 0.714 in the external validation set, underscoring its high diagnostic efficacy in both hippocampal and peripheral blood contexts for AD patients. Meanwhile, ATP6V1E1 expression was positively correlated with effector memory CD4 + T cells, while negatively correlated with natural killer T cells and activated CD4 + T cells. Results from quantitative PCR (qPCR) and immunofluorescence assays indicated a reduction in ATP6V1E1 expression, aligning with our database analysis findings. In summary, ATP6V1E1 as a candidate gene provides a new perspective for the early identification and pathogenesis of AD.

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The original contributions presented in the study are included in the article; further inquiries can be directed to the corresponding author.

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Acknowledgements

We acknowledge all GEO data builders and data contributors. We thank the Xiangya Hospital provides SH-SY5Y cells.

Funding

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

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  1. Jian Wang and Xinhua Huo contributed equally to this work.

Authors and Affiliations

  1. The Institute of Reproduction and Stem Cell Engineering, School of Basic Medical Sciences, Central South University, Changsha, China

    Jian Wang & Xuan Sun

  2. Hunan Guangxiu Hospital, Hunan Normal University, Changsha, China

    Jian Wang, Xinhua Huo, Huiqin Zhou & Xiaofeng Li

  3. Department of Radiology, Central South University, The Third Xiangya Hospital, Changsha, China

    Huasheng Liu

  4. Reproductive and Genetic Hospital of CITIC Xiangya, Changsha, China

    Na Lu

  5. Hunan Guangxiu Medical Imaging Diagnosis Center, Changsha, China

    Jian Wang

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  1. Jian Wang
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Contributions

All authors contributed to the study’s conception and design, and Jian Wang and Xinhua Huo contributed equally to this work. Material preparation, data collection, and analysis were performed by Xinhua Huo and Huiqin Zhou. The first draft of the manuscript was written by Jian Wang and Xinhua Huo. Huasheng Liu and Na Lu participated in the revision of the manuscript. Xinhua Huo, Xiaofeng Li, and Xuan Sun performed the cell culture, data correction, and image rendering. All authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Correspondence to Jian Wang.

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Wang, J., Huo, X., Zhou, H. et al. Identification of Autophagy-Related Candidate Genes in the Early Diagnosis of Alzheimer’s Disease and Exploration of Potential Molecular Mechanisms. Mol Neurobiol (2024). https://doi.org/10.1007/s12035-024-04011-z

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