Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Circulating microRNAs associated to cerebrospinal fluid amyloid-β and phosphorylated tau levels as potential non-invasive biomarkers for mild cognitive impairment and Alzheimer’s disease

ABSTRACT: To identify the potential biomarkers of Alzheimer's disease (AD) based on circulating microRNAs (miRNAs), we developed a new approach using feature selection and linear mixed model. The miRNA sequencing data of 105 plasma and 112 serum samples from 112 subjects including 28 AD cases, 63 mild cognitive impairment (MCI), and 21 controls were used to identify cerebrospinal fluid biomarkers associated miRNAs. The potential of these miRNAs as biomarkers of AD or MCI was researched and validated via both internal and external dataset. Patient classification was effectuated in compliance with the NIA-AA criteria for “MCI due to AD” and “Dementia due to AD”.

ORGANISM(S): Homo sapiens

PROVIDER: GSE215789 | GEO | 2024/02/26

REPOSITORIES: GEO

ACCESS DATA

Json Xml

Dataset's files

Source:

			Action	DRS
		Other

Items per page:

1 - 1 of 1

Similar Datasets

RNA-seq of human plasma microRNAs in Alzheimer Disease

Project description:MicroRNAs (miRNAs) could play an important role as potential Alzheimer Disease (AD) biomarkers. Plasma samples were collected from participants: Mild cognitive impairment (MCI) due to AD patients (n= 20), preclinical AD patients (n= 8) and healthy controls (n= 20). Then, small RNA sequencing analysis, followed by miRNA differential expression analysis comparing different methods (DESeq2, edgeR, NOISeq) were carried out.

2022-05-08 | E-MTAB-11103 | biostudies-arrayexpress

Prognosis prediction model for Alzheimer’s disease conversion from mild cognitive impairment by integrative analysis of multi-omics data

Project description:Mild cognitive impairment (MCI) is a clinical precursor of Alzheimer’s disease (AD). Many MCI subjects convert to AD, although others remain stable as MCI or sometimes return to cognitive normal. Currently, there are no curative treatments for patients who are already in AD, and therefore biomarkers for early detection of high risk MCI-to-AD conversion subjects are rapidly required. Here, we investigated potential biomarkers from blood-based miRNA (miR) expression profiles and genomic data of 197 Japanese MCI patients. Using the candidate biomarkers, we constructed a prognosis prediction model based on a Cox proportional hazard model. The final prediction model, composed of 24 miR-eQTLs (i.e. SNP-miRNA pairs) and three clinical factors (age, sex and ApoE ε4 carriers), successfully classified MCI patients into two groups, low and high, in terms of risk of MCI-to-AD conversion (log-rank trend test P=3.44×10^(-4)), and achieved a concordance index of 0.702 on an independent test set. Network-based meta-analysis using target genes of the miR-eQTLs further revealed four important hub genes (SHC1, FOXO1, GSK3B, and PTEN) associated with the pathogenesis of AD. Statistically significant differences were observed in PTEN expression between MCI and AD and SHC1 expression between cognitively normal elder subjects (CN) and AD when examining RNA-seq data from 610 blood samples (PTEN, P=0.023; SHC1, P=0.049), although FOXO1 and GSK3B showed low levels of expression in blood. Accurate prediction of MCI-to-AD conversion enables earlier appropriate intervention for those MCI patients and can lead to a reduction of MCI patients that convert to AD with high risk. We believe that further investigation with larger sample sizes will contribute to practical clinical use of our approach in MCI-to-AD conversion in the near future.

2020-11-11 | GSE150693 | GEO

Changes in cognitive ability and serum microRNAs during aging in mice

Project description:Mild cognitive impairment (MCI) is considered an early stage leading to dementia. MCI can be reversed, and early diagnosis at the MCI stage is vital to control the progression to dementia. Dementia is currently diagnosed based on interviews and screening tests. However, novel biomarkers must be identified to enable early detection of MCI. Therefore, this study aimed to discover novel biomarkers in the form of blood microRNAs (miRNAs) for the diagnosis of MCI or early dementia.

2023-12-06 | GSE249248 | GEO

Transcriptomic predictors of rapid progression from mild cognitive impairment to Alzheimer's Disease

Project description:Background: Effective treatment for Alzheimer’s disease (AD) remains an unmet need. Thus, identifying patients with mild cognitive impairment (MCI) who are at high-risk of progressing to AD is crucial for early intervention. Methods: Blood-based transcriptomics analyses were performed using a longitudinal study cohort to compare progressive MCI (P-MCI, n=28), stable MCI (S-MCI, n=39), and AD patients (n=49). Statistical DESeq2 analysis and machine learning methods were employed to identify differentially expressed genes (DEGs) and develop prediction models. Results: We discovered a remarkable gender-specific difference in DEGs that distinguish P-MCI from S-MCI. Machine learning models achieved high accuracy in distinguishing P-MCI from S-MCI (AUC 0.93), AD from S-MCI (AUC 0.94), and AD from P-MCI (AUC 0.92). An 8-gene signature was identified for distinguishing P-MCI from S-MCI.

2024-11-30 | GSE282742 | GEO

Proteins present in tears from control, MCI and Alzheimer disease patients

Project description:we investigated tear fluid as a novel source of disease-specific protein biomarkers for Alzheimer disease (AD) development using samples from patients suffering from mild cognitive impairment (MCI) and AD. Tear protein content was evaluated via liquid chromatography–mass spectrometry.

2019-11-04 | PXD014475 | Pride

Shotgun-based Proteomics of Extracellular Vesicles in Alzheimer’s Disease Reveals Biomarkers Involved in Immunological and Coagulation Pathways

Project description:Alzheimer’s dementia (AD) is the most common form of dementia and without readily available clinical biomarkers. Blood-derived proteins are routinely used for diagnostics, however, comprehensive plasma profiling is challenging due to the large dynamic range in protein concentrations. Extracellular vesicles (EVs) can cross the blood-brain barrier and may provide a source for AD related biomarkers. We investigated plasma-derived EV proteins for biomarkers towards AD diagnostics from 10 AD patients, 10 Mild Cognitive Impairment (MCI) patients, and 9 healthy controls (Con) using liquid chromatography - tandem mass spectrometry (LC-MS/MS). EV enrichment was performed using a double centrifugation of 100,000 × g, 1h, 4°C with a wash of the pellet. Some AD patients presented with highly elevated FXIIIA1 (log2 FC: 4.6, p-value: 0.005) and FXIIIB (log2 FC: 4.9, p-value: 0.018). A group of proteins was identified discriminating Con and AD (AUC: 0.91, CI: 0.67-1.00) with ORM2 (AUC: 1.00, CI: 1.00-1.00), RBP4 (AUC: 0.99, CI: 0.95-1.00), and HYDIN (AUC: 0.89, CI: 0.72-1.00) found especially relevant for AD. This indicates that EVs provide an easily accessible matrix for possible biomarkers for AD. Some of the MCI patients, with similar protein profiles as the AD group progressed to AD within a 2-year timespan.

2022-02-16 | PXD024216 | Pride

Plasma microRNA biomarker detection for mild cognitive impairment using differential correlation analysis

Project description:Background: Mild cognitive impairment (MCI) is an intermediate state between normal aging, and Alzheimer’s disease, and other dementias. Early detection of dementia, and MCI, is a crucial issue in terms of secondary prevention. Blood biomarker detection is a possible way for early detection of MCI. Although disease biomarkers are detected by, in general, using single molecular analysis such as t-test, another possible approach is based on interaction between molecules. Results: Differential correlation analysis, which detects difference on correlation of two variables in case/control study, was carried out to the dataset with 745 microRNAs (miRNAs) from plasma samples of 30 age-matched controls and 23 MCI patients in Japan. The 20 pairs of miRNAs, which consist of 20 miRNAs, were selected as MCI markers. Two pairs of miRNAs (hsa-miR-191 and hsa-miR-101, and hsa-miR-103 and hsa-miR-222) out of 20 attained the highest area under the curve (AUC) value of 0.962 for MCI detection. Other two miRNA pairs that include hsa-miR-191 and hsa-miR-125b also attained high AUC value of ≥ 0.95. Pathway analysis was performed to the MCI markers for further understanding of biological implications. As a result, collapsed correlation on hsa-miR-191 and emerged correlation on hsa-miR-125b may have key role in MCI, and dementia progression. Conclusion: Differential correlation analysis, a bioinformatics tool to elucidate complicated and interdependent biological systems behind diseases, detects effective MCI markers that cannot be found by single molecule analysis such as t-test.

2016-12-10 | GSE90828 | GEO

Characterization of Global DNA Methylome in Mild Cognitive Impairment and Alzheimer's Disease

Project description:Genome-wide profiling of DNA methylation in blood leukocytes from Chinese patients with mild cognitive impairment (MCI) and Alzheimer’s disease (AD). The Illumina Infinium MethylationEPIC BeadChip array (850K chip) was used to detect DNA methylation profiles throughout approximately 850,000 CpG sites in peripheral blood white cells of MCI- and AD-affected Chinese patients, as well as cognitively healthy controls. All samples included 20 Chinese patients with MCI, 20 Chinese patients with AD, and 20 cognitively healthy controls.

2023-09-11 | GSE208623 | GEO

Music compensates for altered gene expression in age-related cognitive disorders

Project description:Extensive literature has explored the beneficial effects of music in age-related cognitive disorders (ACD), but limited knowledge exists regarding its impact on gene expression. We analyzed transcriptomes of ACD patients and healthy controls, pre-post a music session (n=60), and main genes/pathways were compared to those dysregulated in mild cognitive impairment (MCI) and Alzheimer’s disease (AD) as revealed by a multi-cohort study (n=1269 MCI/AD and controls). Music was associated with 2.3 times more whole-genome gene expression, particularly on neurodegeneration-related genes, in ACD than controls. Co-expressed gene-modules and pathways analysis demonstrated that music impacted autophagy, vesicle and endosome organization, biological processes commonly dysregulated in MCI/AD. Notably, the data indicated a strong negative correlation between musically-modified genes/pathways in ACD and those dysregulated in MCI/AD. These findings highlight the compensatory effect of music on genes/biological processes affected in MCI/AD, providing insights into the molecular mechanisms underlying the benefits of music on these disorders.

2023-10-26 | GSE239282 | GEO

Transcription profiling of human peripheral blood mononuclear cells of dementia and Alzheimer patients

Project description:With the aging population, there is a growing focus on dementia, especially Alzheimer’s disease (AD). The molecular basis underlying the pathogenesis of AD is gradually being elucidated. Increasing evidence has shown that the immunological function of leukocytes plays a crucial role in the development of neurodegenerative disorders. However, there have been few studies among the Taiwanese population. The aim of this study was to investigate potential biomarkers for early diagnosis of Alzheimer’s disease from blood leukocytes. Experiment Overall Design: The peripheral blood mononuclear cells (PBMC) transcriptomes from 5 patients with mild cognitive impairment (MCI), 4 AD, as well as 4 normal controls (NC), were analyzed by microarray analysis.

2009-12-04 | E-GEOD-18309 | biostudies-arrayexpress

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data