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

Liang, Lucong; Yan, Jing; Huang, Xiaohua; Zou, Chun; Chen, Liechun; Li, Rongjie; Xie, Jieqiong; Pan, Mika; Zou, Donghua; Liu, Ying

doi:10.3389/fpsyt.2022.925012

Cited by 10 publications

(6 citation statements)

References 59 publications

(61 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…DCs play specific roles in the immune microenvironment during processes such as aging and in AD ( Mrdjen et al, 2018 ). pDCs showed markedly extensive infiltration in AD ( Liang et al, 2022 ). Furthermore, naive B cells and M2 macrophages found a high infiltrated degree in patients with AD when we used the CIBERSORT method.…”

Section: Discussionmentioning

confidence: 99%

Exploration of novel biomarkers in Alzheimer’s disease based on four diagnostic models

Zou

Pan

et al. 2023

Front. Aging Neurosci.

Self Cite

View full text Add to dashboard Cite

BackgroundDespite tremendous progress in diagnosis and prediction of Alzheimer’s disease (AD), the absence of treatments implies the need for further research. In this study, we screened AD biomarkers by comparing expression profiles of AD and control tissue samples and used various models to identify potential biomarkers. We further explored immune cells associated with these biomarkers that are involved in the brain microenvironment.MethodsBy differential expression analysis, we identified differentially expressed genes (DEGs) of four datasets (GSE125583, GSE118553, GSE5281, GSE122063), and common expression direction of genes of four datasets were considered as intersecting DEGs, which were used to perform enrichment analysis. We then screened the intersecting pathways between the pathways identified by enrichment analysis. DEGs in intersecting pathways that had an area under the curve (AUC) > 0.7 constructed random forest, least absolute shrinkage and selection operator (LASSO), logistic regression, and gradient boosting machine models. Subsequently, using receiver operating characteristic curve (ROC) and decision curve analysis (DCA) to select an optimal diagnostic model, we obtained the feature genes. Feature genes that were regulated by differentially expressed miRNAs (AUC > 0.85) were explored further. Furthermore, using single-sample GSEA to calculate infiltration of immune cells in AD patients.ResultsScreened 1855 intersecting DEGs that were involved in RAS and AMPK signaling. The LASSO model performed best among the four models. Thus, it was used as the optimal diagnostic model for ROC and DCA analyses. This obtained eight feature genes, including ATP2B3, BDNF, DVL2, ITGA10, SLC6A12, SMAD4, SST, and TPI1. SLC6A12 is regulated by miR-3176. Finally, the results of ssGSEA indicated dendritic cells and plasmacytoid dendritic cells were highly infiltrated in AD patients.ConclusionThe LASSO model is the optimal diagnostic model for identifying feature genes as potential AD biomarkers, which can supply new strategies for the treatment of patients with AD.

show abstract

Section: Discussionmentioning

confidence: 99%

Exploration of novel biomarkers in Alzheimer’s disease based on four diagnostic models

Zou

Pan

et al. 2023

Front. Aging Neurosci.

Self Cite

View full text Add to dashboard Cite

show abstract

“…Additionally, while no studies directly related to ALS were discovered, other studies related to other neurological diseases were found in the cases of the EEF1A1 [ 49 ], COX5A [ 50 ] and RPL12 and RPL15 genes [ 51 ].…”

Section: Resultsmentioning

confidence: 99%

Sporadic Amyotrophic Lateral Sclerosis Skeletal Muscle Transcriptome Analysis: A Comprehensive Examination of Differentially Expressed Genes

Gascón,

Zaragoza,

Calvo

et al. 2024

Biomolecules

View full text Add to dashboard Cite

Amyotrophic lateral sclerosis (ALS) that comprises sporadic (sALS) and familial (fALS) cases, is a devastating neurodegenerative disorder characterized by progressive degeneration of motor neurons, leading to muscle atrophy and various clinical manifestations. However, the complex underlying mechanisms affecting this disease are not yet known. On the other hand, there is also no good prognosis of the disease due to the lack of biomarkers and therapeutic targets. Therefore, in this study, by means of bioinformatics analysis, sALS-affected muscle tissue was analyzed using the GEO GSE41414 dataset, identifying 397 differentially expressed genes (DEGs). Functional analysis revealed 320 up-regulated DEGs associated with muscle development and 77 down-regulated DEGs linked to energy metabolism. Protein–protein interaction network analysis identified 20 hub genes, including EIF4A1, HNRNPR and NDUFA4. Furthermore, miRNA target gene networks revealed 17 miRNAs linked to hub genes, with hsa-mir-206, hsa-mir-133b and hsa-mir-100-5p having been previously implicated in ALS. This study presents new potential biomarkers and therapeutic targets for ALS by correlating the information obtained with a comprehensive literature review, providing new potential targets to study their role in ALS.

show abstract

“…In the past literature, there is the identification of AD-related hub genes by using only the modular genes in DEGs or WGCNA to identify AD hub genes, which is still slightly insufficient in terms of in data selection and processing ( Wu et al, 2021 ; Liang et al, 2022 ). In comparison, the strengths in our study: 1.…”

Section: Discussionmentioning

confidence: 99%

Identification of novel hub genes for Alzheimer’s disease associated with the hippocampus using WGCNA and differential gene analysis

Chen,

Li,

et al. 2024

Front. Neurosci.

View full text Add to dashboard Cite

BackgroundAlzheimer’s disease (AD) is a common, refractory, progressive neurodegenerative disorder in which cognitive and memory deficits are highly correlated with abnormalities in hippocampal brain regions. There is still a lack of hippocampus-related markers for AD diagnosis and prevention.MethodsDifferently expressed genes were identified in the gene expression profile GSE293789 in the hippocampal brain region. Enrichment analyses GO, KEGG, and GSEA were used to identify biological pathways involved in the DEGs and AD-related group. WGCNA was used to identify the gene modules that are highly associated with AD in the samples. The intersecting genes of the genes in DEGs and modules were extracted and the top ten ranked hub genes were identified. Finally GES48350 was used as a validation cohort to predict the diagnostic efficacy of hub genes.ResultsFrom GSE293789, 225 DEGs were identified, which were mainly associated with calcium response, glutamatergic synapses, and calcium-dependent phospholipid-binding response. WGCNA analysis yielded dark green and bright yellow modular genes as the most relevant to AD. From these two modules, 176 genes were extracted, which were taken to be intersected with DEGs, yielding 51 intersecting genes. Then 10 hub genes were identified in them: HSPA1B, HSPB1, HSPA1A, DNAJB1, HSPB8, ANXA2, ANXA1, SOX9, YAP1, and AHNAK. Validation of these genes was found to have excellent diagnostic performance.ConclusionTen AD-related hub genes in the hippocampus were identified, contributing to further understanding of AD development in the hippocampus and development of targets for therapeutic prevention.

show abstract

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

Cited by 10 publications

References 59 publications

Exploration of novel biomarkers in Alzheimer’s disease based on four diagnostic models

Exploration of novel biomarkers in Alzheimer’s disease based on four diagnostic models

Sporadic Amyotrophic Lateral Sclerosis Skeletal Muscle Transcriptome Analysis: A Comprehensive Examination of Differentially Expressed Genes

Identification of novel hub genes for Alzheimer’s disease associated with the hippocampus using WGCNA and differential gene analysis

Contact Info

Product

Resources

About