Integrative Transcriptomic, Network, and Machine Learning Analyses Identify Genistein and Resveratrol-Associated Therapeutic Targets in Alzheimer's Disease.

Overview

Authors: Murat Isıyel¹, Hamid Ceylan^1,2, Yeliz Demir^3,4

Faculty of Science, Department of Molecular Biology and Genetics, Atatürk University,Erzurum, Türkiye
East Anatolian High Technology Research and Application Center (DAYTAM), Atatürk University,Erzurum, 25240 Türkiye
Department of Pharmacy Services, Nihat Delibalta Göle Vocational High School, Ardahan University,Ardahan, 75700 Türkiye
Faculty of Science, Department of Chemistry, Atatürk University,Erzurum, Türkiye

Institutions: Atatürk University (Türkiye); Ardahan University (Türkiye)

Journal: Molecular neurobiology, volume 63, issue 1, article 666

Dates: received 1 January 2026; accepted 25 May 2026; published online 2 June 2026; in print 2026

Type: Research article · Language: English

License: CC BY

Identifiers: DOI 10.1007/s12035-026-05973-y · PMID 42228255 · PMCID PMC13230253 · OpenAlex W7163149612

Open access: hybrid, a free copy (OpenAlex)

Status: data only (how the code is found and verified)

Categories: genetics / omics (modality), human (organism), Alzheimer's / dementia (population), cellular / molecular (subfield)

Methods: Statistics, Machine learning

Keywords: Alzheimer’s disease, Machine learning, Transcriptomics, Genistein, Resveratrol

MeSH: Alzheimer Disease*, Gene Regulatory Networks*, Genistein*, Machine Learning*, Resveratrol*, Transcriptome*, Gene Expression Profiling, Humans, Protein Interaction Maps (* major topic)

Topic: Alzheimer's disease research and treatments (Cellular and Molecular Neuroscience, Neuroscience), according to OpenAlex

Funding: Ardahan University

Citations: not cited yet (Europe PMC); 53 references in the paper

Abstract

Alzheimer’s disease (AD) is a multifactorial neurodegenerative disorder characterized by complex molecular alterations across multiple brain regions. In this study, we applied an integrative systems-level framework combining multi-region transcriptomic analysis, protein–protein interaction (PPI) network topology, machine learning–based validation, and in silico approaches to identify robust and pharmacologically relevant molecular targets in AD. Gene expression data from four AD-related brain regions (entorhinal cortex, frontal cortex, hippocampus, and temporal cortex) were obtained from the GSE5281 dataset, yielding 467 genes consistently dysregulated across all regions. Network analysis identified a subset of downregulated hub genes predominantly associated with mitochondrial bioenergetics and proteostasis pathways. Intersection with experimentally curated targets of genistein and resveratrol prioritized four elite genes, COX5B, ENO1, HSP90AB1, and SDHB as shared and biologically central nodes. To assess the collective discriminative capacity of the identified gene set, a random forest classifier was trained using shared differentially expressed genes. The model demonstrated strong classification performance with a low out-of-bag error rate, while feature importance analysis identified SDHB as the most influential contributor, followed by COX5B and ENO1, indicating synergistic multigene effects rather than isolated biomarker behavior. Machine learning was applied as an integrative validation layer to reinforce biological relevance, rather than as a standalone diagnostic tool. Finally, molecular docking analyses revealed favorable binding affinities of genistein and resveratrol toward all four elite targets. Overall, these findings highlight a convergent mitochondrial–proteostasis dysfunction axis in AD and suggest COX5B, ENO1, HSP90AB1, and SDHB as promising multi-target nodes for polyphenol-based therapeutic strategies.

Supplementary Information: The online version contains supplementary material available at 10.1007/s12035-026-05973-y.

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

Code

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Data

Datasets cited

ncbi.nlm.nih.gov/geo2r, at NCBI; found in the text, “Data Processing and Target Identification”

Data Availability

All data used in this study are available in the GEO repository. Data are available from the corresponding author upon reasonable request.

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

Versions

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Version 2, 28 September 2026

Publisher: n/a → Springer Science+Business Media

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 3 authors, 5 keywords, 9 MeSH terms, 1 funder, 53 references.

Cite

This paper

Isıyel, M., Ceylan, H., & Demir, Y. (2026). Integrative Transcriptomic, Network, and Machine Learning Analyses Identify Genistein and Resveratrol-Associated Therapeutic Targets in Alzheimer's Disease. Molecular neurobiology, 63(1), 666. https://doi.org/10.1007/s12035-026-05973-y

BibTeX

@article{isyel2026integrative, author = {Isıyel, Murat and Ceylan, Hamid and Demir, Yeliz}, title = {{Integrative Transcriptomic, Network, and Machine Learning Analyses Identify Genistein and Resveratrol-Associated Therapeutic Targets in Alzheimer's Disease}}, journal = {Molecular neurobiology}, year = {2026}, month = jun, volume = {63}, number = {1}, pages = {666}, publisher = {Springer Science+Business Media}, issn = {0893-7648}, doi = {10.1007/s12035-026-05973-y}, url = {https://doi.org/10.1007/s12035-026-05973-y}, pmid = {42228255}, pmcid = {PMC13230253} }

RIS

TY - JOUR AU - Isıyel, Murat AU - Ceylan, Hamid AU - Demir, Yeliz TI - Integrative Transcriptomic, Network, and Machine Learning Analyses Identify Genistein and Resveratrol-Associated Therapeutic Targets in Alzheimer's Disease T2 - Molecular neurobiology J2 - Mol Neurobiol PY - 2026 DA - 2026/06/02 VL - 63 IS - 1 SP - 666 SN - 0893-7648 PB - Springer Science+Business Media DO - 10.1007/s12035-026-05973-y UR - https://doi.org/10.1007/s12035-026-05973-y LA - en ER -

CSL-JSON

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