Construction and evaluation of Alzheimer’s disease diagnostic prediction model based on genes involved in mitophagy

Frontiers in Aging Neuroscience · 5 authors, 2 centres

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Using bioinformatics analysis of public GEO datasets combined with machine learning methods, the authors identified four mitophagy-related genes (OPTN, PTGS2, TOMM20, VDAC1) and built a diagnostic prediction model for Alzheimer's disease. The model showed good predictive performance and was verified across multiple independent datasets, AD cell models, and clinical peripheral blood samples.

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This study used bioinformatics approaches to identify mitophagy-related genes with diagnostic potential for Alzheimer's disease. Gene expression profiles were downloaded from GEO, and limma, PPI, functional enrichment analysis, and WGCNA were used to screen 72 differentially expressed mitophagy-related genes. A logistic regression diagnostic model and a nomogram combining the four genes with age were constructed. ROC curve analysis of GSE122063 and GSE63061 confirmed model reliability. Validation across multiple independent datasets, AD cell models, and clinical peripheral blood samples showed consistent expression trends: OPTN and VDAC1 were upregulated, while PTGS2 and TOMM20 were downregulated in AD. The models may provide a supplementary role in AD diagnosis.

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other

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neurology

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AI pipeline

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ORIGINAL PAPER

PMC10077494

Construction and evaluation of Alzheimer’s disease diagnostic prediction model based on genes involved in mitophagy

Frontiers in Aging Neuroscience

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ABSTRACT

Abstract Introduction Alzheimer’s disease (AD) is a common neurodegenerative disease. The concealment of the disease is the difficulty of its prevention and treatment. Previous studies have shown that mitophagy is crucial to the development of AD. However, there is a lack of research on the identification and clinical significance of mitophagy-related genes in AD. Therefore, the purpose of this study was to identify the mitophagy-related genes with the diagnostic potential for AD and establish a diagnostic model for AD. Methods Firstly, we download the AD gene expression profile from Gene Expression Omnibus (GEO). Limma, PPI, functional enrichment analysis and WGCNA were used to screen the differential expression of mitophagy-related AD gene. Then, machine learning methods (random forest, univariate analysis, support vector machine, LASSO regression and support vector machine classification) were used to identify diagnostic markers. Finally, the diagnostic model was established and evaluated by ROC, multiple regression analysis, nomogram, calibration curve and other methods. Moreover, multiple independent datasets, AD cell models and AD clinical samples were used to verify the expression level of characteristic genes in the diagnostic model. Results In total, 72 differentially expressed mitophagy-related related genes were identified, which were mainly involved in biological functions such as autophagy, apoptosis and neurological diseases. Four mitophagy-related genes (OPTN, PTGS2, TOMM20, and VDAC1) were identified as biomarkers. A diagnostic prediction model was constructed, and the reliability of the model was verified by receiver operating characteristic (ROC) curve analysis of GSE122063 and GSE63061. Then, we combine four mitophagy-related genes with age to establish a nomogram model. The ROC, C index and calibration curve show that the model has good prediction performance. Finally, multiple independent datasets, AD cell model samples and clinical peripheral blood samples confirmed that the expression levels of four mitophagy-related genes were consistent with the results of bioinformatics analysis. Discussion The analysis results and diagnostic model of this study are helpful for the follow-up clinical work and mechanism research of AD. Keywords: mitophagy, Alzheimer’s disease, biomarkers, diagnostic model, machine learning

CITATION EXPORT

Yongyan Pei, Sijia Chen, Fengling Zhou et al.. Construction and evaluation of Alzheimer’s disease diagnostic prediction model based on genes involved in mitophagy. Frontiers in Aging Neuroscience. (2023). https://doi.org/10.3389/fnagi.2023.1146660

AUTHORS

5 · 2 CENTRES

Yongyan Pei, Sijia Chen, Fengling Zhou, Tao Xie, Hua Cao

1School of Chemistry and Chemical Engineering, Guangdong Pharmaceutical University, Zhongshan, China
2Department of Neurology, The Third Affiliated Hospital of Naval Medical University, Shanghai, China