Cargando…

Identification of the molecular subgroups in Alzheimer's disease by transcriptomic data

BACKGROUND: Alzheimer's disease (AD) is a heterogeneous pathological disease with genetic background accompanied by aging. This inconsistency is present among molecular subtypes, which has led to diagnostic ambiguity and failure in drug development. We precisely distinguished patients of AD at...

Descripción completa

Detalles Bibliográficos
Autores principales: Li, He, Wei, Meiqi, Ye, Tianyuan, Liu, Yiduan, Qi, Dongmei, Cheng, Xiaorui
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9530954/
https://www.ncbi.nlm.nih.gov/pubmed/36204002
http://dx.doi.org/10.3389/fneur.2022.901179
_version_ 1784801797686165504
author Li, He
Wei, Meiqi
Ye, Tianyuan
Liu, Yiduan
Qi, Dongmei
Cheng, Xiaorui
author_facet Li, He
Wei, Meiqi
Ye, Tianyuan
Liu, Yiduan
Qi, Dongmei
Cheng, Xiaorui
author_sort Li, He
collection PubMed
description BACKGROUND: Alzheimer's disease (AD) is a heterogeneous pathological disease with genetic background accompanied by aging. This inconsistency is present among molecular subtypes, which has led to diagnostic ambiguity and failure in drug development. We precisely distinguished patients of AD at the transcriptome level. METHODS: We collected 1,240 AD brain tissue samples collected from the GEO dataset. Consensus clustering was used to identify molecular subtypes, and the clinical characteristics were focused on. To reveal transcriptome differences among subgroups, we certificated specific upregulated genes and annotated the biological function. According to RANK METRIC SCORE in GSEA, TOP10 was defined as the hub gene. In addition, the systematic correlation between the hub gene and “A/T/N” was analyzed. Finally, we used external data sets to verify the diagnostic value of hub genes. RESULTS: We identified three molecular subtypes of AD from 743 AD samples, among which subtypes I and III had high-risk factors, and subtype II had protective factors. All three subgroups had higher neuritis plaque density, and subgroups I and III had higher clinical dementia scores and neurofibrillary tangles than subgroup II. Our results confirmed a positive association between neurofibrillary tangles and dementia, but not neuritis plaques. Subgroup I genes clustered in viral infection, hypoxia injury, and angiogenesis. Subgroup II showed heterogeneity in synaptic pathology, and we found several essential beneficial synaptic proteins. Due to presenilin one amplification, Subgroup III was a risk subgroup suspected of familial AD, involving abnormal neurogenic signals, glial cell differentiation, and proliferation. Among the three subgroups, the highest combined diagnostic value of the hub genes were 0.95, 0.92, and 0.83, respectively, indicating that the hub genes had sound typing and diagnostic ability. CONCLUSION: The transcriptome classification of AD cases played out the pathological heterogeneity of different subgroups. It throws daylight on the personalized diagnosis and treatment of AD.
format Online
Article
Text
id pubmed-9530954
institution National Center for Biotechnology Information
language English
publishDate 2022
publisher Frontiers Media S.A.
record_format MEDLINE/PubMed
spelling pubmed-95309542022-10-05 Identification of the molecular subgroups in Alzheimer's disease by transcriptomic data Li, He Wei, Meiqi Ye, Tianyuan Liu, Yiduan Qi, Dongmei Cheng, Xiaorui Front Neurol Neurology BACKGROUND: Alzheimer's disease (AD) is a heterogeneous pathological disease with genetic background accompanied by aging. This inconsistency is present among molecular subtypes, which has led to diagnostic ambiguity and failure in drug development. We precisely distinguished patients of AD at the transcriptome level. METHODS: We collected 1,240 AD brain tissue samples collected from the GEO dataset. Consensus clustering was used to identify molecular subtypes, and the clinical characteristics were focused on. To reveal transcriptome differences among subgroups, we certificated specific upregulated genes and annotated the biological function. According to RANK METRIC SCORE in GSEA, TOP10 was defined as the hub gene. In addition, the systematic correlation between the hub gene and “A/T/N” was analyzed. Finally, we used external data sets to verify the diagnostic value of hub genes. RESULTS: We identified three molecular subtypes of AD from 743 AD samples, among which subtypes I and III had high-risk factors, and subtype II had protective factors. All three subgroups had higher neuritis plaque density, and subgroups I and III had higher clinical dementia scores and neurofibrillary tangles than subgroup II. Our results confirmed a positive association between neurofibrillary tangles and dementia, but not neuritis plaques. Subgroup I genes clustered in viral infection, hypoxia injury, and angiogenesis. Subgroup II showed heterogeneity in synaptic pathology, and we found several essential beneficial synaptic proteins. Due to presenilin one amplification, Subgroup III was a risk subgroup suspected of familial AD, involving abnormal neurogenic signals, glial cell differentiation, and proliferation. Among the three subgroups, the highest combined diagnostic value of the hub genes were 0.95, 0.92, and 0.83, respectively, indicating that the hub genes had sound typing and diagnostic ability. CONCLUSION: The transcriptome classification of AD cases played out the pathological heterogeneity of different subgroups. It throws daylight on the personalized diagnosis and treatment of AD. Frontiers Media S.A. 2022-09-20 /pmc/articles/PMC9530954/ /pubmed/36204002 http://dx.doi.org/10.3389/fneur.2022.901179 Text en Copyright © 2022 Li, Wei, Ye, Liu, Qi and Cheng. 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 Neurology
Li, He
Wei, Meiqi
Ye, Tianyuan
Liu, Yiduan
Qi, Dongmei
Cheng, Xiaorui
Identification of the molecular subgroups in Alzheimer's disease by transcriptomic data
title Identification of the molecular subgroups in Alzheimer's disease by transcriptomic data
title_full Identification of the molecular subgroups in Alzheimer's disease by transcriptomic data
title_fullStr Identification of the molecular subgroups in Alzheimer's disease by transcriptomic data
title_full_unstemmed Identification of the molecular subgroups in Alzheimer's disease by transcriptomic data
title_short Identification of the molecular subgroups in Alzheimer's disease by transcriptomic data
title_sort identification of the molecular subgroups in alzheimer's disease by transcriptomic data
topic Neurology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9530954/
https://www.ncbi.nlm.nih.gov/pubmed/36204002
http://dx.doi.org/10.3389/fneur.2022.901179
work_keys_str_mv AT lihe identificationofthemolecularsubgroupsinalzheimersdiseasebytranscriptomicdata
AT weimeiqi identificationofthemolecularsubgroupsinalzheimersdiseasebytranscriptomicdata
AT yetianyuan identificationofthemolecularsubgroupsinalzheimersdiseasebytranscriptomicdata
AT liuyiduan identificationofthemolecularsubgroupsinalzheimersdiseasebytranscriptomicdata
AT qidongmei identificationofthemolecularsubgroupsinalzheimersdiseasebytranscriptomicdata
AT chengxiaorui identificationofthemolecularsubgroupsinalzheimersdiseasebytranscriptomicdata