Construction and evaluation of Alzheimer’s disease diagnostic prediction model based on genes involved in mitophagy - Pei, Yongyan; Chen, Sijia; Zhou, Fengling; Xie, Tao; Cao, Hua (Frontiers in Aging Neuroscience, 2023)
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. The aim of this study is to identify and verify mitophagy-related genes with diagnostic potential for AD through a series of analytical methods, and to establish a diagnostic model for AD. GSE63061 was downloaded from Gene expression Omnibus (GEO), and mitophagy-related genes were obtained from multiple databases and literatures. Then 72 differentially expressed mitophagy-related related genes were identified by limma, which were mainly involved in biological functions such as autophagy, apoptosis and neurological diseases. 4 mitophagy-related genes (OPTN, PTGS2, TOMM20 and VDAC1) were identified as biomarkers by protein-protein interactions (PPI), weighted correlation network analysis (WGCNA), univariate analysis, random forest classification algorithm, minimum absolute contraction and selection operation (LASSO) regression method, support vector machine (SVM) classification and multivariate logistic regression. 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 4 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 4 mitophagy-related genes were consistent with the results of bioinformatics analysis. In summary, the analysis results and diagnostic model of this study are helpful for the follow-up clinical work and mechanism research of AD.