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

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

doi:10.3389/fnagi.2023.1146660

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

Front Aging Neurosci. 2023 Mar 23:15:1146660. doi: 10.3389/fnagi.2023.1146660. eCollection 2023.

Authors

Yongyan Pei¹, Sijia Chen¹, Fengling Zhou¹, Tao Xie², Hua Cao¹

Affiliations

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

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: Alzheimer’s disease; biomarkers; diagnostic model; machine learning; mitophagy.

Grants and funding

This work was supported by the Scientific Research Project of Guangdong Bureau of Traditional Chinese Medicine (20221215), the Project of Young Creative Talents in Colleges and Universities of Guangdong Province Department of Education (2021KQNCX037), and Guangdong Medical Research Foundation (B2023365).