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

doi:10.3389/fnagi.2022.770136

Cited by 12 publications

(7 citation statements)

References 55 publications

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“…Previously, studies have identified molecular signatures of blood cell origin and potential therapeutic targets through a comprehensive analysis of AD-related datasets [44]. Some studies have also analyzed brain tissue samples from AD patients by bioinformatics to look for biomarkers of AD [45][46][47]. Another study was used a web-based approach to identify biomarkers and therapeutic agents for AD [48].…”

Section: Discussionmentioning

confidence: 99%

Exploring Early Physical Examination Diagnostic Biomarkers for Alzheimer’s Disease Based on Least Absolute Shrinkage and Selection Operator

Hua

Tang

Liang

et al. 2022

Computational and Mathematical Methods in Medicine

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Neurodegenerative diseases such as Alzheimer’s disease (AD) are an increasing public health challenge. There is an urgent need to shift the focus to accurate detection of clinical AD at the physical examination stage. The purpose of this study was to identify biomarkers for AD diagnosis. Differential expression analysis was performed on a dataset including prefrontal cortical samples and peripheral blood samples of AD to identify shared differentially expressed genes (DEGs) shared between the two datasets. In addition, a minimum absolute contraction and selection operator (LASSO) model based on shared-DEGs identified nine signature genes (MT1X, IGF1, DLEU7, TRIM36, PTPRC, WNK2, SPG20, C8orf59, and BRWD1) that accurately predict AD occurrence. Enrichment analysis showed that the signature gene was significantly associated with the AD-related p53 signaling pathway, T-cell receptor signaling pathway, HIF-1 signaling pathway, AMPK signaling pathway, and FoxO signaling pathway. Thus, our results identify not only biomarkers for diagnosing AD but also potentially specific pathways. The AD biomarkers proposed in this study could serve as indicators for prevention and diagnosis during physical examination.

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Section: Discussionmentioning

confidence: 99%

Exploring Early Physical Examination Diagnostic Biomarkers for Alzheimer’s Disease Based on Least Absolute Shrinkage and Selection Operator

Hua

Tang

Liang

et al. 2022

Computational and Mathematical Methods in Medicine

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“…The protein RBM8A may regulate many genes related to AD pathophysiology ( Zou D. et al, 2019 ). Furthermore, we identified hub genes associated with molecular subtypes as potential biomarkers for AD, as well as candidate therapeutic targets ( Ma et al, 2022 ). Identifying biomarkers for AD and VD may help individualize treatment for patients as a function of their precise symptoms.…”

Section: Introductionmentioning

confidence: 99%

REPS1 as a Potential Biomarker in Alzheimer’s Disease and Vascular Dementia

Luo

Chen²,

Huang³

et al. 2022

Front. Aging Neurosci.

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Vascular dementia (VD) and Alzheimer’s disease (AD) are common types of dementia for which no curative therapies are known. In this study, we identified hub genes associated with AD and VD in order to explore new potential therapeutic targets. Genes differentially expressed in VD and AD in all three datasets (GSE122063, GSE132903, and GSE5281) were identified and used to construct a protein–protein interaction network. We identified 10 modules containing 427 module genes in AD and VD. Module genes showing an area under the diagnostic curve > 0.60 for AD or VD were used to construct a least absolute shrinkage and selection operator model and were entered into a support vector machine-recursive feature elimination algorithm, which identified REPS1 as a hub gene in AD and VD. Furthermore, REPS1 was associated with activation of pyruvate metabolism and inhibition of Ras signaling pathway. Module genes, together with differentially expressed microRNAs from the dataset GSE46579, were used to construct a regulatory network. REPS1 was predicted to bind to the microRNA hsa_miR_5701. Single-sample gene set enrichment analysis was used to explore immune cell infiltration, which suggested a negative correlation between REPS1 expression and infiltration by plasmacytoid dendritic cells in AD and VD. In conclusion, our results suggest core pathways involved in both AD and VD, and they identify REPS1 as a potential biomarker of both diseases. This protein may aid in early diagnosis, monitoring of treatment response, and even efforts to prevent these debilitating disorders.

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“…CXCR4, EGFR, MAP4K4, IGF1R ( 18 ), and RBM8A ( 19 ) may serve as biomarkers for AD diagnosis. A study identified significant marker genes, which could be related to a potential AD molecular type ( 20 ). Studies have shown that REPS1 serves as a potential biomarker for AD and Vascular dementia that were both associated with Ras signaling ( 21 ).…”

Section: Introductionmentioning

confidence: 99%

Identification of molecular signatures associated with sleep disorder and Alzheimer’s disease

et al. 2022

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BackgroundAlzheimer’s disease (AD) and sleep disorders are both neurodegenerative conditions characterized by impaired or absent sleep. However, potential common pathogenetic mechanisms of these diseases are not well characterized.MethodsDifferentially expressed genes (DEGs) were identified using publicly available human gene expression profiles GSE5281 for AD and GSE40562 for sleep disorder. DEGs common to the two datasets were used for enrichment analysis, and we performed multi-scale embedded gene co-expression network analysis (MEGENA) for common DEGs. Fast gene set enrichment analysis (fGSEA) was used to obtain common pathways, while gene set variation analysis (GSVA) was applied to quantify those pathways. Subsequently, we extracted the common genes between module genes identified by MEGENA and genes of the common pathways, and we constructed protein-protein interaction (PPI) networks. The top 10 genes with the highest degree of connectivity were classified as hub genes. Common genes were used to perform Metascape enrichment analysis for functional enrichment. Furthermore, we quantified infiltrating immune cells in patients with AD or sleep disorder and in controls.ResultsDEGs common to the two disorders were involved in the citrate cycle and the HIF-1 signaling pathway, and several common DEGs were related to signaling pathways regulating the pluripotency of stem cells, as well as 10 other pathways. Using MEGENA, we identified 29 modules and 1,498 module genes in GSE5281, and 55 modules and 1,791 module genes in GSE40562. Hub genes involved in AD and sleep disorder were ATP5A1, ATP5B, COX5A, GAPDH, NDUFA9, NDUFS3, NDUFV2, SOD1, UQCRC1, and UQCRC2. Plasmacytoid dendritic cells and T helper 17 cells had the most extensive infiltration in both AD and sleep disorder.ConclusionAD pathology and pathways of neurodegeneration participate in processes contributing in AD and sleep disorder. Hub genes may be worth exploring as potential candidates for targeted therapy of AD and sleep disorder.

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Identification of Alzheimer’s Disease Molecular Subtypes Based on Parallel Large-Scale Sequencing

Cited by 12 publications

References 55 publications

Exploring Early Physical Examination Diagnostic Biomarkers for Alzheimer’s Disease Based on Least Absolute Shrinkage and Selection Operator

Exploring Early Physical Examination Diagnostic Biomarkers for Alzheimer’s Disease Based on Least Absolute Shrinkage and Selection Operator

REPS1 as a Potential Biomarker in Alzheimer’s Disease and Vascular Dementia

Identification of molecular signatures associated with sleep disorder and Alzheimer’s disease

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