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Transcriptomic Insights Into Alzheimer's Disease: Differentially Expressed Genes and Cholesterol Metabolism.

Overview

Authors: Rui Sun1, Xu Wang1, Zaibao Wang1,2, Chunliu Li1,2, Qing Shao1,2, Xiangru Liu1,2, Hongrui Zhu1, Sheng Wang1, Keqiang He1
ORCID iDs: Zaibao Wang, Xiangru Liu, Hongrui Zhu, Sheng Wang
  1. Department of Anesthesiology, the First Affiliated Hospital of USTC, Division of Life Sciences and Medicine University of Science and Technology of China Hefei Anhui China
  2. Department of Anesthesiology Graduate School of Bengbu Medical University Bengbu Anhui China
Institutions: University of Science and Technology of China (China); Bengbu Medical College (China)
Journal: CNS neuroscience & therapeutics, volume 32, issue 3, article e70833
Dates: received 30 November 2025; accepted 4 March 2026; published online 19 March 2026; in print March 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1002/cns.70833 · PMID 41854441 · PMCID PMC13093267 · OpenAlex W7138914790
Open access: gold, a free copy (OpenAlex)
Status: data only
Categories: genetics / omics (modality), human (organism), mouse (organism), Alzheimer's / dementia (population), cellular / molecular (subfield)
Methods: Spectral & time-frequency, Statistics, Smoothing, state filtering, decompositions, Machine learning
Keywords: Alzheimer's disease, cholesterol metabolism, machine learning, mendelian randomization
MeSH: Alzheimer Disease*, Cholesterol*, Transcriptome*, Animals, Cohort Studies, Female, Gene Expression Profiling, Humans, Machine Learning, Male, Mendelian Randomization Analysis, Mice (* major topic)
Topic: Alzheimer's disease research and treatments (Physiology, Medicine), according to OpenAlex
Funding: Hubei Chen Xiaoping Science and Technology Development Foundation (CXPJJH125001‐2537); Health Research Project of the Anhui Provincial Health Commission (AHWJ2023BAc20089); University of Science and Technology of China
Citations: cited by 1 paper (Europe PMC); 39 references in the paper

Abstract

Background: Alzheimer's disease (AD) is a progressive neurodegenerative disorder characterized by cognitive decline and memory impairment, posing significant challenges to affected individuals, their families, and healthcare systems globally. With projections indicating that the prevalence of AD could escalate to 152 million cases by 2050, there is an urgent need to elucidate the underlying mechanisms driving this condition. Additionally, developing effective diagnostic tools to aid in its early detection and management is crucial.

Methods: In this study, we utilized a combination of Mendelian randomization and advanced machine learning techniques to analyze transcriptomic data from five distinct cohorts of Alzheimer's Disease (AD) patients. After addressing batch effects, we identified differentially expressed genes (DEGs) between the AD and control groups. Mendelian randomization analysis was conducted to assess the causal relationships between DEGs and AD risk. A Venn diagram was subsequently used to identify genes associated with cholesterol metabolism from the screened gene set. The shared DEGs were subjected to functional enrichment analyses. Furthermore, immune analysis was quantified using Gene Set Enrichment Analysis (GSEA). A diagnostic model for AD was developed by evaluating 113 combinations of 12 machine learning algorithms with 10‐fold cross‐validation on the training datasets, followed by external validation on test datasets. Finally, immunofluorescence staining was performed on mouse brain slices to verify the expression level of KLHL21.

Results: Our analyses identified a substantial number of differentially expressed genes (DEGs) demonstrating significant differences between Alzheimer's disease (AD) patients and control groups. Among these, we identified 29 genes associated with AD, with 21 of them linked to cholesterol metabolism, highlighting its pivotal role in the disease's pathogenesis. From this set, we developed a robust 8‐gene diagnostic signature (comprising CHSY1, FIBP, DHCR24, HVCN1, KIFAP3, KLHL21, LETMD1, and SLC25A29), which outperformed existing AD diagnostic models in both training and testing cohorts. Additionally, complementary animal experiments were conducted to validate the biological relevance of these genes, further elucidating their roles in AD pathology.

Conclusions: Our research identified critical genes and proposed novel pathways for early diagnosis and potential therapeutic interventions, paving the way for enhanced clinical applications in Alzheimer's disease management.

Reproduced under the paper's license (CC BY), from the paper cited above.

Code

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Data

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Data Availability Statement

The data that support the findings of this study are openly available in sun00814/ad260118 at https://doi.org/10.5281/zenodo.18289856.

Reproduced under the paper's license (CC BY), from the paper cited above.

Versions

The history of this record: each version stored by the harvester or made by a correction of its authors or of the maintainers of its code, and what changed in its facts. The texts of the paper (its abstract, its availability statements) are not part of it; versions that changed only those are not listed.

Version 1, 30 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 9 authors, 4 keywords, 12 MeSH terms, 3 funders, 39 references.

Cite

This paper

Sun, R., Wang, X., Wang, Z., Li, C., Shao, Q., Liu, X., Zhu, H., Wang, S., & He, K. (2026). Transcriptomic Insights Into Alzheimer's Disease: Differentially Expressed Genes and Cholesterol Metabolism. CNS neuroscience & therapeutics, 32(3), e70833. https://doi.org/10.1002/cns.70833

BibTeX

@article{sun2026transcriptomic,
author = {Sun, Rui and Wang, Xu and Wang, Zaibao and Li, Chunliu and Shao, Qing and Liu, Xiangru and Zhu, Hongrui and Wang, Sheng and He, Keqiang},
title = {{Transcriptomic Insights Into Alzheimer's Disease: Differentially Expressed Genes and Cholesterol Metabolism}},
journal = {CNS neuroscience \& therapeutics},
year = {2026},
month = mar,
volume = {32},
number = {3},
pages = {e70833},
publisher = {Wiley},
issn = {1755-5930},
doi = {10.1002/cns.70833},
url = {https://doi.org/10.1002/cns.70833},
pmid = {41854441},
pmcid = {PMC13093267}
}

RIS

TY - JOUR
AU - Sun, Rui
AU - Wang, Xu
AU - Wang, Zaibao
AU - Li, Chunliu
AU - Shao, Qing
AU - Liu, Xiangru
AU - Zhu, Hongrui
AU - Wang, Sheng
AU - He, Keqiang
TI - Transcriptomic Insights Into Alzheimer's Disease: Differentially Expressed Genes and Cholesterol Metabolism
T2 - CNS neuroscience & therapeutics
J2 - CNS Neurosci Ther
PY - 2026
DA - 2026/03/01
VL - 32
IS - 3
SP - e70833
SN - 1755-5930
PB - Wiley
DO - 10.1002/cns.70833
UR - https://doi.org/10.1002/cns.70833
LA - en
ER -

CSL-JSON

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