Integrative bioinformatics and machine learning approaches reveal oxidative stress and glucose metabolism related genes as therapeutic targets and drug candidates in Alzheimer’s disease
BackgroundAlzheimer’s disease (AD), the most common form of dementia, has treatments that slow but do not stop cognitive decline. Additional treatments are based on its pathogenic mechanisms are needed. Evidence has long highlighted oxidative stress and impaired glucose metabolism as crucial factors...
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Frontiers Media S.A.
2025-06-01
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| Series: | Frontiers in Immunology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fimmu.2025.1572468/full |
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| author | Fatima Noor Sidra Aslam Ignazio S. Piras Cecilia Tremblay Thomas G. Beach Geidy E. Serrano |
| author_facet | Fatima Noor Sidra Aslam Ignazio S. Piras Cecilia Tremblay Thomas G. Beach Geidy E. Serrano |
| author_sort | Fatima Noor |
| collection | DOAJ |
| description | BackgroundAlzheimer’s disease (AD), the most common form of dementia, has treatments that slow but do not stop cognitive decline. Additional treatments are based on its pathogenic mechanisms are needed. Evidence has long highlighted oxidative stress and impaired glucose metabolism as crucial factors in AD pathogenesis. Therefore, in this study we aimed to find key AD pathogenic pathways combining genes involved in oxidative stress and glucose metabolism as well as potential small-molecule therapeutic agents.MethodsUsing autopsy brain RNA sequencing data (GSE125583) derived from the Arizona Study of Aging and Brain and Body Donation Program, AD-related genes were identified via differential gene expression, pathway and coexpression analysis. Oxidative stress and glucose metabolism genes were correlated to pinpoint module genes. GSE173955 was used an independent dataset was used for validation, conducting molecular docking, assessing hub genes for AD, and integrating machine learning approaches.ResultsWe identified 13,982 differentially expressed genes (DEGs) in AD patients. Through WGCNA coexpression analysis, 1,068 genes were linked to AD-specific modules. Pearson’s correlation analysis highlighted 99 genes involved in oxidative stress and glucose metabolism. Overlap analysis of DEGs, module genes, and these metabolic genes revealed 21 key overlapping targets. PPI network and receiving operating curve (ROC) curve analyses then identified AKT1 and PPARGC1A as diagnostic hub genes for AD. Machine learning-based virtual screening of small molecules identified various inhibitors and enhancers with drug-like potential targeting AKT1 (upregulated) and PPARGC1A (downregulated), respectively. Among others, the Random Forest model was the most reliable for predicting molecular activity. Molecular docking further validated the binding affinities of these small molecules (inhibitors/enhancers) to AKT1 and PPARGC1A.ConclusionThis study identified AKT1 and PPARGC1A as potential therapeutic targets in AD. We discovered drug candidates with strong binding affinities, offering new avenues for effective AD treatment strategies. |
| format | Article |
| id | doaj-art-0d14ea79e689429492ae9698568db010 |
| institution | DOAJ |
| issn | 1664-3224 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Frontiers Media S.A. |
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| series | Frontiers in Immunology |
| spelling | doaj-art-0d14ea79e689429492ae9698568db0102025-08-20T03:23:59ZengFrontiers Media S.A.Frontiers in Immunology1664-32242025-06-011610.3389/fimmu.2025.15724681572468Integrative bioinformatics and machine learning approaches reveal oxidative stress and glucose metabolism related genes as therapeutic targets and drug candidates in Alzheimer’s diseaseFatima Noor0Sidra Aslam1Ignazio S. Piras2Cecilia Tremblay3Thomas G. Beach4Geidy E. Serrano5Institute of Molecular Biology and Biotechnology, The University of Lahore, Lahore, PakistanDepartment of Pathology, Banner Sun Health Research Institute, Sun City, AZ, United StatesNeurogenomics Division, Translational Genomics Research Institute, Phoenix, AZ, United StatesDepartment of Pathology, Banner Sun Health Research Institute, Sun City, AZ, United StatesDepartment of Pathology, Banner Sun Health Research Institute, Sun City, AZ, United StatesDepartment of Pathology, Banner Sun Health Research Institute, Sun City, AZ, United StatesBackgroundAlzheimer’s disease (AD), the most common form of dementia, has treatments that slow but do not stop cognitive decline. Additional treatments are based on its pathogenic mechanisms are needed. Evidence has long highlighted oxidative stress and impaired glucose metabolism as crucial factors in AD pathogenesis. Therefore, in this study we aimed to find key AD pathogenic pathways combining genes involved in oxidative stress and glucose metabolism as well as potential small-molecule therapeutic agents.MethodsUsing autopsy brain RNA sequencing data (GSE125583) derived from the Arizona Study of Aging and Brain and Body Donation Program, AD-related genes were identified via differential gene expression, pathway and coexpression analysis. Oxidative stress and glucose metabolism genes were correlated to pinpoint module genes. GSE173955 was used an independent dataset was used for validation, conducting molecular docking, assessing hub genes for AD, and integrating machine learning approaches.ResultsWe identified 13,982 differentially expressed genes (DEGs) in AD patients. Through WGCNA coexpression analysis, 1,068 genes were linked to AD-specific modules. Pearson’s correlation analysis highlighted 99 genes involved in oxidative stress and glucose metabolism. Overlap analysis of DEGs, module genes, and these metabolic genes revealed 21 key overlapping targets. PPI network and receiving operating curve (ROC) curve analyses then identified AKT1 and PPARGC1A as diagnostic hub genes for AD. Machine learning-based virtual screening of small molecules identified various inhibitors and enhancers with drug-like potential targeting AKT1 (upregulated) and PPARGC1A (downregulated), respectively. Among others, the Random Forest model was the most reliable for predicting molecular activity. Molecular docking further validated the binding affinities of these small molecules (inhibitors/enhancers) to AKT1 and PPARGC1A.ConclusionThis study identified AKT1 and PPARGC1A as potential therapeutic targets in AD. We discovered drug candidates with strong binding affinities, offering new avenues for effective AD treatment strategies.https://www.frontiersin.org/articles/10.3389/fimmu.2025.1572468/fulloxidative stressAlzheimer’s diseaseglucose metabolismbioinformaticsmachine learning |
| spellingShingle | Fatima Noor Sidra Aslam Ignazio S. Piras Cecilia Tremblay Thomas G. Beach Geidy E. Serrano Integrative bioinformatics and machine learning approaches reveal oxidative stress and glucose metabolism related genes as therapeutic targets and drug candidates in Alzheimer’s disease Frontiers in Immunology oxidative stress Alzheimer’s disease glucose metabolism bioinformatics machine learning |
| title | Integrative bioinformatics and machine learning approaches reveal oxidative stress and glucose metabolism related genes as therapeutic targets and drug candidates in Alzheimer’s disease |
| title_full | Integrative bioinformatics and machine learning approaches reveal oxidative stress and glucose metabolism related genes as therapeutic targets and drug candidates in Alzheimer’s disease |
| title_fullStr | Integrative bioinformatics and machine learning approaches reveal oxidative stress and glucose metabolism related genes as therapeutic targets and drug candidates in Alzheimer’s disease |
| title_full_unstemmed | Integrative bioinformatics and machine learning approaches reveal oxidative stress and glucose metabolism related genes as therapeutic targets and drug candidates in Alzheimer’s disease |
| title_short | Integrative bioinformatics and machine learning approaches reveal oxidative stress and glucose metabolism related genes as therapeutic targets and drug candidates in Alzheimer’s disease |
| title_sort | integrative bioinformatics and machine learning approaches reveal oxidative stress and glucose metabolism related genes as therapeutic targets and drug candidates in alzheimer s disease |
| topic | oxidative stress Alzheimer’s disease glucose metabolism bioinformatics machine learning |
| url | https://www.frontiersin.org/articles/10.3389/fimmu.2025.1572468/full |
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