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Biomarker Identification for Alzheimer's Disease Using a Multi-Filter Gene Selection Approach.

Elnaz Pashaei1, Elham Pashaei1, Nizamettin Aydin2

  • 1Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN 46202, USA.

International Journal of Molecular Sciences
|March 13, 2025
PubMed
Summary
This summary is machine-generated.

Researchers identified 50 key genes for Alzheimer's disease (AD) using a novel multi-filter approach. These Alzheimer's biomarkers show high predictive value, offering new therapeutic targets for dementia.

Keywords:
Alzheimer’s disease (AD)biomarkershub genes in ADmachine learning in ADmulti-filter gene selectionprotein–protein interaction (PPI) networkrandom forest (RF)

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Area of Science:

  • Neuroscience
  • Genetics
  • Biomarker Discovery

Background:

  • Alzheimer's disease (AD) lacks effective therapies, necessitating research into reliable biomarkers and therapeutic targets.
  • Dementia and cognitive decline associated with AD underscore the urgent need for advanced research strategies.

Purpose of the Study:

  • To develop and validate an aggregative multi-filter gene selection approach for identifying robust Alzheimer's disease biomarkers.
  • To uncover potential therapeutic targets by analyzing differentially expressed genes in AD.

Main Methods:

  • Integrated hub gene ranking (degree, bottleneck) with feature selection (Random Forest, Double Input Symmetrical Relevance) and ranking aggregation.
  • Analyzed five AD-related microarray datasets (GSE48350, GSE36980, GSE132903, GSE118553, GSE5281) and validated findings on an independent dataset (GSE109887).
  • Utilized logistic regression to assess the predictive value of identified genes, achieving an AUC of 86.8 on the validation set.

Main Results:

  • Identified 803 overlapping differentially expressed genes from 464 AD and 492 normal cases across diverse brain regions.
  • Prioritized 50 genes with significant predictive value for Alzheimer's disease.
  • Pathway analysis indicated involvement of these genes in synaptic vesicle cycles, neurodegeneration, and cognitive function.

Conclusions:

  • The developed gene selection approach effectively identifies robust AD biomarkers.
  • The 50 prioritized genes offer promising therapeutic targets for Alzheimer's disease.
  • Findings provide critical insights into the biological mechanisms underlying AD, paving the way for novel treatments.