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Identification of telomere-related genes associated with aging-related molecular clusters and the construction of a diagnostic model in Alzheimer's disease based on a bioinformatic analysis. | LitMetric

Identification of telomere-related genes associated with aging-related molecular clusters and the construction of a diagnostic model in Alzheimer's disease based on a bioinformatic analysis.

Comput Biol Med

Laboratory of Systematic Forest and Forest Products Sciences, Graduate School of Bioresource and Bioenvironmental Sciences, Kyushu University, Fukuoka, 819-0395, Japan. Electronic address:

Published: June 2023

AI Article Synopsis

  • Alzheimer's disease (AD) is linked to aging, with telomere-related genes (TRGs) potentially influencing its development through their roles in aging processes.
  • Researchers analyzed gene expression from 97 AD samples to identify aging-related clusters, assess immune characteristics, and explore TRGs.
  • They found two aging clusters in AD patients; one cluster (Cluster A) showed distinct immune features, and a generalized linear model (GLM) proved the most accurate in predicting AD and its subtypes based on TRG data.

Article Abstract

Background: Alzheimer's disease (AD) is a neurodegenerative disease that is strongly associated with aging. Telomeres are DNA sequences that protect chromosomes from damage and shorten with age. Telomere-related genes (TRGs) may play a role in AD's pathogenesis.

Objectives: To identify TRGs related to aging clusters in AD patients, explore their immunological characteristics, and build a TRG-based prediction model for AD and AD subtypes.

Methods: We analyzed the gene expression profiles of 97 AD samples from the GSE132903 dataset, using aging-related genes (ARGs) as clustering variables. We also assessed immune-cell infiltration in each cluster. We performed a weighted gene co-expression network analysis to identify cluster-specific differentially expressed TRGs. We compared four machine-learning models (random forest, generalized linear model [GLM], gradient boosting model, and support vector machine) for predicting AD and AD subtypes based on TRGs and validated TRGs by conducting an artificial neural network (ANN) analysis and a nomogram model.

Results: We identified two aging clusters in AD patients with distinct immunological features: Cluster A had higher immune scores than Cluster B. Cluster A and the immune system are intimately associated, and this association could affect immunological function and result in AD via the digestive system. The GLM predicted AD and AD subtypes most accurately and was validated by the ANN analysis and nomogram model.

Conclusion: Our analyses revealed novel TRGs associated with aging clusters in AD patients and their immunological characteristics. We also developed a promising prediction model based on TRGs for assessing AD risk.

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Source
http://dx.doi.org/10.1016/j.compbiomed.2023.106922DOI Listing

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