Cargando…

Discovery and validation of Ferroptosis-related molecular patterns and immune characteristics in Alzheimer’s disease

BACKGROUND: To date, the pathogenesis of Alzheimer’s disease is still not fully elucidated. Much evidence suggests that Ferroptosis plays a crucial role in the pathogenesis of AD, but little is known about its molecular immunological mechanisms. Therefore, this study aims to comprehensively analyse...

Descripción completa

Detalles Bibliográficos
Autores principales: He, Yi-Jie, Cong, Lin, Liang, Song-Lan, Ma, Xu, Tian, Jia-Nan, Li, Hui, Wu, Yun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9727409/
https://www.ncbi.nlm.nih.gov/pubmed/36506471
http://dx.doi.org/10.3389/fnagi.2022.1056312
_version_ 1784845012223131648
author He, Yi-Jie
Cong, Lin
Liang, Song-Lan
Ma, Xu
Tian, Jia-Nan
Li, Hui
Wu, Yun
author_facet He, Yi-Jie
Cong, Lin
Liang, Song-Lan
Ma, Xu
Tian, Jia-Nan
Li, Hui
Wu, Yun
author_sort He, Yi-Jie
collection PubMed
description BACKGROUND: To date, the pathogenesis of Alzheimer’s disease is still not fully elucidated. Much evidence suggests that Ferroptosis plays a crucial role in the pathogenesis of AD, but little is known about its molecular immunological mechanisms. Therefore, this study aims to comprehensively analyse and explore the molecular mechanisms and immunological features of Ferroptosis-related genes in the pathogenesis of AD. MATERIALS AND METHODS: We obtained the brain tissue dataset for AD from the GEO database and downloaded the Ferroptosis-related gene set from FerrDb for analysis. The most relevant Hub genes for AD were obtained using two machine learning algorithms (Least absolute shrinkage and selection operator (LASSO) and multiple support vector machine recursive feature elimination (mSVM-RFE)). The study of the Hub gene was divided into two parts. In the first part, AD patients were genotyped by unsupervised cluster analysis, and the different clusters’ immune characteristics were analysed. A PCA approach was used to quantify the FRGscore. In the second part: we elucidate the biological functions involved in the Hub genes and their role in the immune microenvironment by integrating algorithms (GSEA, GSVA and CIBERSORT). Analysis of Hub gene-based drug regulatory networks and mRNA-miRNA-lncRNA regulatory networks using Cytoscape. Hub genes were further analysed using logistic regression models. RESULTS: Based on two machine learning algorithms, we obtained a total of 10 Hub genes. Unsupervised clustering successfully identified two different clusters, and immune infiltration analysis showed a significantly higher degree of immune infiltration in type A than in type B, indicating that type A may be at the peak of AD neuroinflammation. Secondly, a Hub gene-based Gene-Drug regulatory network and a ceRNA regulatory network were successfully constructed. Finally, a logistic regression algorithm-based AD diagnosis model and Nomogram diagram were developed. CONCLUSION: Our study provides new insights into the role of Ferroptosis-related molecular patterns and immune mechanisms in AD, as well as providing a theoretical basis for the addition of diagnostic markers for AD.
format Online
Article
Text
id pubmed-9727409
institution National Center for Biotechnology Information
language English
publishDate 2022
publisher Frontiers Media S.A.
record_format MEDLINE/PubMed
spelling pubmed-97274092022-12-08 Discovery and validation of Ferroptosis-related molecular patterns and immune characteristics in Alzheimer’s disease He, Yi-Jie Cong, Lin Liang, Song-Lan Ma, Xu Tian, Jia-Nan Li, Hui Wu, Yun Front Aging Neurosci Neuroscience BACKGROUND: To date, the pathogenesis of Alzheimer’s disease is still not fully elucidated. Much evidence suggests that Ferroptosis plays a crucial role in the pathogenesis of AD, but little is known about its molecular immunological mechanisms. Therefore, this study aims to comprehensively analyse and explore the molecular mechanisms and immunological features of Ferroptosis-related genes in the pathogenesis of AD. MATERIALS AND METHODS: We obtained the brain tissue dataset for AD from the GEO database and downloaded the Ferroptosis-related gene set from FerrDb for analysis. The most relevant Hub genes for AD were obtained using two machine learning algorithms (Least absolute shrinkage and selection operator (LASSO) and multiple support vector machine recursive feature elimination (mSVM-RFE)). The study of the Hub gene was divided into two parts. In the first part, AD patients were genotyped by unsupervised cluster analysis, and the different clusters’ immune characteristics were analysed. A PCA approach was used to quantify the FRGscore. In the second part: we elucidate the biological functions involved in the Hub genes and their role in the immune microenvironment by integrating algorithms (GSEA, GSVA and CIBERSORT). Analysis of Hub gene-based drug regulatory networks and mRNA-miRNA-lncRNA regulatory networks using Cytoscape. Hub genes were further analysed using logistic regression models. RESULTS: Based on two machine learning algorithms, we obtained a total of 10 Hub genes. Unsupervised clustering successfully identified two different clusters, and immune infiltration analysis showed a significantly higher degree of immune infiltration in type A than in type B, indicating that type A may be at the peak of AD neuroinflammation. Secondly, a Hub gene-based Gene-Drug regulatory network and a ceRNA regulatory network were successfully constructed. Finally, a logistic regression algorithm-based AD diagnosis model and Nomogram diagram were developed. CONCLUSION: Our study provides new insights into the role of Ferroptosis-related molecular patterns and immune mechanisms in AD, as well as providing a theoretical basis for the addition of diagnostic markers for AD. Frontiers Media S.A. 2022-11-23 /pmc/articles/PMC9727409/ /pubmed/36506471 http://dx.doi.org/10.3389/fnagi.2022.1056312 Text en Copyright © 2022 He, Cong, Liang, Ma, Tian, Li and Wu. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Neuroscience
He, Yi-Jie
Cong, Lin
Liang, Song-Lan
Ma, Xu
Tian, Jia-Nan
Li, Hui
Wu, Yun
Discovery and validation of Ferroptosis-related molecular patterns and immune characteristics in Alzheimer’s disease
title Discovery and validation of Ferroptosis-related molecular patterns and immune characteristics in Alzheimer’s disease
title_full Discovery and validation of Ferroptosis-related molecular patterns and immune characteristics in Alzheimer’s disease
title_fullStr Discovery and validation of Ferroptosis-related molecular patterns and immune characteristics in Alzheimer’s disease
title_full_unstemmed Discovery and validation of Ferroptosis-related molecular patterns and immune characteristics in Alzheimer’s disease
title_short Discovery and validation of Ferroptosis-related molecular patterns and immune characteristics in Alzheimer’s disease
title_sort discovery and validation of ferroptosis-related molecular patterns and immune characteristics in alzheimer’s disease
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9727409/
https://www.ncbi.nlm.nih.gov/pubmed/36506471
http://dx.doi.org/10.3389/fnagi.2022.1056312
work_keys_str_mv AT heyijie discoveryandvalidationofferroptosisrelatedmolecularpatternsandimmunecharacteristicsinalzheimersdisease
AT conglin discoveryandvalidationofferroptosisrelatedmolecularpatternsandimmunecharacteristicsinalzheimersdisease
AT liangsonglan discoveryandvalidationofferroptosisrelatedmolecularpatternsandimmunecharacteristicsinalzheimersdisease
AT maxu discoveryandvalidationofferroptosisrelatedmolecularpatternsandimmunecharacteristicsinalzheimersdisease
AT tianjianan discoveryandvalidationofferroptosisrelatedmolecularpatternsandimmunecharacteristicsinalzheimersdisease
AT lihui discoveryandvalidationofferroptosisrelatedmolecularpatternsandimmunecharacteristicsinalzheimersdisease
AT wuyun discoveryandvalidationofferroptosisrelatedmolecularpatternsandimmunecharacteristicsinalzheimersdisease