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Identification of Alzheimer’s Disease Molecular Subtypes Based on Parallel Large-Scale Sequencing
The incidence of Alzheimer’s disease (AD) is constantly increasing as the older population grows, and no effective treatment is currently available. In this study, we focused on the identification of AD molecular subtypes to facilitate the development of effective drugs. AD sequencing data collected...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9112923/ https://www.ncbi.nlm.nih.gov/pubmed/35592696 http://dx.doi.org/10.3389/fnagi.2022.770136 |
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author | Ma, Meigang Liao, Yuhan Huang, Xiaohua Zou, Chun Chen, Liechun Liang, Lucong Meng, Youshi Wu, Yuan Zou, Donghua |
author_facet | Ma, Meigang Liao, Yuhan Huang, Xiaohua Zou, Chun Chen, Liechun Liang, Lucong Meng, Youshi Wu, Yuan Zou, Donghua |
author_sort | Ma, Meigang |
collection | PubMed |
description | The incidence of Alzheimer’s disease (AD) is constantly increasing as the older population grows, and no effective treatment is currently available. In this study, we focused on the identification of AD molecular subtypes to facilitate the development of effective drugs. AD sequencing data collected from the Gene Expression Omnibus (GEO) database were subjected to cluster sample analysis. Each sample module was then identified as a specific AD molecular subtype, and the biological processes and pathways were verified. The main long non-coding RNAs and transcription factors regulating each “typing pathway” and their potential mechanisms were determined using the RNAInter and TRRUST databases. Based on the marker genes of each “typing module,” a classifier was developed for molecular typing of AD. According to the pathways involved, five sample clustering modules were identified (mitogen-activated protein kinase, synaptic, autophagy, forkhead box class O, and cell senescence), which may be regulated through multiple pathways. The classifier showed good classification performance, which may be useful for developing novel AD drugs and predicting their indications. |
format | Online Article Text |
id | pubmed-9112923 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-91129232022-05-18 Identification of Alzheimer’s Disease Molecular Subtypes Based on Parallel Large-Scale Sequencing Ma, Meigang Liao, Yuhan Huang, Xiaohua Zou, Chun Chen, Liechun Liang, Lucong Meng, Youshi Wu, Yuan Zou, Donghua Front Aging Neurosci Neuroscience The incidence of Alzheimer’s disease (AD) is constantly increasing as the older population grows, and no effective treatment is currently available. In this study, we focused on the identification of AD molecular subtypes to facilitate the development of effective drugs. AD sequencing data collected from the Gene Expression Omnibus (GEO) database were subjected to cluster sample analysis. Each sample module was then identified as a specific AD molecular subtype, and the biological processes and pathways were verified. The main long non-coding RNAs and transcription factors regulating each “typing pathway” and their potential mechanisms were determined using the RNAInter and TRRUST databases. Based on the marker genes of each “typing module,” a classifier was developed for molecular typing of AD. According to the pathways involved, five sample clustering modules were identified (mitogen-activated protein kinase, synaptic, autophagy, forkhead box class O, and cell senescence), which may be regulated through multiple pathways. The classifier showed good classification performance, which may be useful for developing novel AD drugs and predicting their indications. Frontiers Media S.A. 2022-04-28 /pmc/articles/PMC9112923/ /pubmed/35592696 http://dx.doi.org/10.3389/fnagi.2022.770136 Text en Copyright © 2022 Ma, Liao, Huang, Zou, Chen, Liang, Meng, Wu and Zou. 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 Ma, Meigang Liao, Yuhan Huang, Xiaohua Zou, Chun Chen, Liechun Liang, Lucong Meng, Youshi Wu, Yuan Zou, Donghua Identification of Alzheimer’s Disease Molecular Subtypes Based on Parallel Large-Scale Sequencing |
title | Identification of Alzheimer’s Disease Molecular Subtypes Based on Parallel Large-Scale Sequencing |
title_full | Identification of Alzheimer’s Disease Molecular Subtypes Based on Parallel Large-Scale Sequencing |
title_fullStr | Identification of Alzheimer’s Disease Molecular Subtypes Based on Parallel Large-Scale Sequencing |
title_full_unstemmed | Identification of Alzheimer’s Disease Molecular Subtypes Based on Parallel Large-Scale Sequencing |
title_short | Identification of Alzheimer’s Disease Molecular Subtypes Based on Parallel Large-Scale Sequencing |
title_sort | identification of alzheimer’s disease molecular subtypes based on parallel large-scale sequencing |
topic | Neuroscience |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9112923/ https://www.ncbi.nlm.nih.gov/pubmed/35592696 http://dx.doi.org/10.3389/fnagi.2022.770136 |
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