Loading publication...

Noodle always counts identifier-free feature events for service operation. With your permission, Google Analytics can also estimate audience and engagement. Search text, publication identifiers, cookies, IP addresses and browser fingerprints are never included in Noodle's first-party event counters.

Privacy details

Article

Identification of the function and mechanism of m6A reader IGF2BP2 in Alzheimer’s disease

27 Oct 2021

Abstract excerpt

Alzheimer's disease, the most common form of dementia in the elderly, is a kind of neurodegenerative disease. However, its pathogenesis and diagnosis remain unclear. M6A is related to nervous system development and neurodegenerative diseases. Here in this study, using multiple RNA-seq datasets of Alzheimer's brain tissues, along with bioinformatic analysis, we innovatively found that m6A reader protein IGF2BP2...

Read the complete abstract on PubMed

Topics

Share this publication in a Topic to start or enrich a Post.

Share to Cancer-related gene regulation
Share to Peptidase Inhibition and Analysis
Share to RNA modifications and cancer

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Source

Identifiers

PMID: 34705667
DOI: 10.18632/aging.203652

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 2 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

Identification of the function and mechanism of m6A reader IGF2BP2 in Alzheimer’s diseasePMID 34705667Read ↗

Integrated Analysis of Weighted Gene Coexpression Network Analysis Identifying Six Genes as Novel Biomarkers for Alzheimer’s DiseaseSemantic proximity Read ↗

Identification of molecular correlations of RBM8A with autophagy in Alzheimer's diseaseSemantic proximity Read ↗

Exploring the Key Genes and Identification of Potential Diagnosis Biomarkers in Alzheimer’s Disease Using Bioinformatics AnalysisSemantic proximity Read ↗

Identification of candidate biomarkers and signaling pathways associated with Alzheimer’s disease using bioinformatics analysis of next generation sequencing dataSemantic proximity Read ↗

Integrated Bioinformatic Analysis and Validation Identifies Immune Microenvironment-Related Potential Biomarkers in Alzheimer's DiseaseSemantic proximity Read ↗

Identification of hub genes contributed to the negative correlation between the incidence of Alzheimer's disease and colorectal cancer via integrated bioinformatics analysis and machine learningSemantic proximity Read ↗

Identification of UBE2N as a biomarker of Alzheimer's disease by combining WGCNA with machine learning algorithmsSemantic proximity Read ↗

Investigating gene expression datasets of hippocampus tissue to discover Alzheimer's disease-associated molecular markersSemantic proximity Read ↗

Microarray Analysis of Differential Gene Expression in Alzheimer’s Disease Identifies Potential Biomarkers with Diagnostic ValueSemantic proximity Read ↗

Transcriptomics-Guided Multi-Cohort Machine Learning for Alzheimer’s Disease DiagnosisSemantic proximity Read ↗

Bioinformatics analyses of significant genes, related pathways and candidate prognostic biomarkers in Alzheimer’s diseaseSemantic proximity Read ↗

Identification of candidate biomarkers and signaling pathways associated with Alzheimer's disease using bioinformatics analysis of next generation sequencing data and molecular docking studiesSemantic proximity Read ↗

⌁Select a neighboring publication to make it the new centre.