Construction and evaluation of Alzheimer’s disease diagnostic prediction model based on genes involved in mitophagy
IntroductionAlzheimer’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...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-03-01
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| Series: | Frontiers in Aging Neuroscience |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fnagi.2023.1146660/full |
| _version_ | 1797861894515589120 |
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| author | Yongyan Pei Sijia Chen Fengling Zhou Tao Xie Hua Cao |
| author_facet | Yongyan Pei Sijia Chen Fengling Zhou Tao Xie Hua Cao |
| author_sort | Yongyan Pei |
| collection | DOAJ |
| description | IntroductionAlzheimer’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.MethodsFirstly, 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.ResultsIn 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.DiscussionThe analysis results and diagnostic model of this study are helpful for the follow-up clinical work and mechanism research of AD. |
| first_indexed | 2024-04-09T22:11:44Z |
| format | Article |
| id | doaj.art-6509ee926810443e81dadd454491d7d7 |
| institution | Directory Open Access Journal |
| issn | 1663-4365 |
| language | English |
| last_indexed | 2024-04-09T22:11:44Z |
| publishDate | 2023-03-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Aging Neuroscience |
| spelling | doaj.art-6509ee926810443e81dadd454491d7d72023-03-23T10:48:14ZengFrontiers Media S.A.Frontiers in Aging Neuroscience1663-43652023-03-011510.3389/fnagi.2023.11466601146660Construction and evaluation of Alzheimer’s disease diagnostic prediction model based on genes involved in mitophagyYongyan Pei0Sijia Chen1Fengling Zhou2Tao Xie3Hua Cao4School of Chemistry and Chemical Engineering, Guangdong Pharmaceutical University, Zhongshan, ChinaSchool of Chemistry and Chemical Engineering, Guangdong Pharmaceutical University, Zhongshan, ChinaSchool of Chemistry and Chemical Engineering, Guangdong Pharmaceutical University, Zhongshan, ChinaDepartment of Neurology, The Third Affiliated Hospital of Naval Medical University, Shanghai, ChinaSchool of Chemistry and Chemical Engineering, Guangdong Pharmaceutical University, Zhongshan, ChinaIntroductionAlzheimer’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.MethodsFirstly, 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.ResultsIn 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.DiscussionThe analysis results and diagnostic model of this study are helpful for the follow-up clinical work and mechanism research of AD.https://www.frontiersin.org/articles/10.3389/fnagi.2023.1146660/fullmitophagyAlzheimer’s diseasebiomarkersdiagnostic modelmachine learning |
| spellingShingle | Yongyan Pei Sijia Chen Fengling Zhou Tao Xie Hua Cao Construction and evaluation of Alzheimer’s disease diagnostic prediction model based on genes involved in mitophagy Frontiers in Aging Neuroscience mitophagy Alzheimer’s disease biomarkers diagnostic model machine learning |
| title | Construction and evaluation of Alzheimer’s disease diagnostic prediction model based on genes involved in mitophagy |
| title_full | Construction and evaluation of Alzheimer’s disease diagnostic prediction model based on genes involved in mitophagy |
| title_fullStr | Construction and evaluation of Alzheimer’s disease diagnostic prediction model based on genes involved in mitophagy |
| title_full_unstemmed | Construction and evaluation of Alzheimer’s disease diagnostic prediction model based on genes involved in mitophagy |
| title_short | Construction and evaluation of Alzheimer’s disease diagnostic prediction model based on genes involved in mitophagy |
| title_sort | construction and evaluation of alzheimer s disease diagnostic prediction model based on genes involved in mitophagy |
| topic | mitophagy Alzheimer’s disease biomarkers diagnostic model machine learning |
| url | https://www.frontiersin.org/articles/10.3389/fnagi.2023.1146660/full |
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