Estimating progression of Alzheimer’s disease with extracellular vesicle-related multi-omics risk models
BackgroundAlzheimer’s Disease (AD) is heterogeneous and shows complex interconnected pathways at various biological levels. Risk scores contribute greatly to disease prognosis and biomarker discovery but typically represent generic risk factors. However, large-scale multi-omics data can generate ind...
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Frontiers Media S.A.
2025-07-01
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| Series: | Frontiers in Aging Neuroscience |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fnagi.2025.1617611/full |
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| author | Xiao Zhang Xiao Zhang Sanoji Wijenayake Shakhawat Hossain Qian Liu Qian Liu |
| author_facet | Xiao Zhang Xiao Zhang Sanoji Wijenayake Shakhawat Hossain Qian Liu Qian Liu |
| author_sort | Xiao Zhang |
| collection | DOAJ |
| description | BackgroundAlzheimer’s Disease (AD) is heterogeneous and shows complex interconnected pathways at various biological levels. Risk scores contribute greatly to disease prognosis and biomarker discovery but typically represent generic risk factors. However, large-scale multi-omics data can generate individualized risk factors. Filtering these risk factors with brain-derived extracellular vesicles (EVs) could yield key pathologic pathways and vesicular vehicles for treatment delivery.MethodsA list of 460 EV-related genes was curated from brain tissue samples in the ExoCarta database. This list was used to select genes from transcriptomics, proteomics, and DNA methylation data. Significant risk factors included demographic features (age, sex) and genes significant for progression in transcriptomics data. These genes were selected using Cox regression, aided by the Least Absolute Shrinkage and Selection Operator (LASSO), and were used to construct three risk models at different omics levels. Gene signatures from the significant risk factors were used as biomarkers for further evaluation, including gene set enrichment analysis (GSEA) and drug perturbation analysis.ResultsNine EV-related genes were identified as significant risk factors. All three risk models predicted high/low risk groups with significant separation in Kaplan-Meier analysis. Training the transcriptomics risk models on EV-related genes yielded better AD classification results than using all genes in an independent dataset. GSEA revealed Mitophagy and several other significant pathways related to AD. Four drugs showed therapeutic potential to target the identified risk factors based on Connectivity Map analysis.ConclusionThe proposed risk score model demonstrates a novel approach to AD using EV-related large-scale multi-omics data. Potential biomarkers and pathways related to AD were identified for further investigation. Drug candidates were identified for further evaluation in biological experiments, potentially transported to targeted tissues via bioengineered EVs. |
| format | Article |
| id | doaj-art-b9ab6b48a0ab426aa6575e3fd649f7d5 |
| institution | Matheson Library |
| issn | 1663-4365 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Aging Neuroscience |
| spelling | doaj-art-b9ab6b48a0ab426aa6575e3fd649f7d52025-07-24T05:33:19ZengFrontiers Media S.A.Frontiers in Aging Neuroscience1663-43652025-07-011710.3389/fnagi.2025.16176111617611Estimating progression of Alzheimer’s disease with extracellular vesicle-related multi-omics risk modelsXiao Zhang0Xiao Zhang1Sanoji Wijenayake2Shakhawat Hossain3Qian Liu4Qian Liu5Max Rady College of Medicine, University of Manitoba, Winnipeg, MB, CanadaDepartment of Applied Computer Science, University of Winnipeg, Winnipeg, MB, CanadaDepartment of Biology, University of Winnipeg, Winnipeg, MB, CanadaDepartment of Mathematics and Statistics, University of Winnipeg, Winnipeg, MB, CanadaMax Rady College of Medicine, University of Manitoba, Winnipeg, MB, CanadaDepartment of Applied Computer Science, University of Winnipeg, Winnipeg, MB, CanadaBackgroundAlzheimer’s Disease (AD) is heterogeneous and shows complex interconnected pathways at various biological levels. Risk scores contribute greatly to disease prognosis and biomarker discovery but typically represent generic risk factors. However, large-scale multi-omics data can generate individualized risk factors. Filtering these risk factors with brain-derived extracellular vesicles (EVs) could yield key pathologic pathways and vesicular vehicles for treatment delivery.MethodsA list of 460 EV-related genes was curated from brain tissue samples in the ExoCarta database. This list was used to select genes from transcriptomics, proteomics, and DNA methylation data. Significant risk factors included demographic features (age, sex) and genes significant for progression in transcriptomics data. These genes were selected using Cox regression, aided by the Least Absolute Shrinkage and Selection Operator (LASSO), and were used to construct three risk models at different omics levels. Gene signatures from the significant risk factors were used as biomarkers for further evaluation, including gene set enrichment analysis (GSEA) and drug perturbation analysis.ResultsNine EV-related genes were identified as significant risk factors. All three risk models predicted high/low risk groups with significant separation in Kaplan-Meier analysis. Training the transcriptomics risk models on EV-related genes yielded better AD classification results than using all genes in an independent dataset. GSEA revealed Mitophagy and several other significant pathways related to AD. Four drugs showed therapeutic potential to target the identified risk factors based on Connectivity Map analysis.ConclusionThe proposed risk score model demonstrates a novel approach to AD using EV-related large-scale multi-omics data. Potential biomarkers and pathways related to AD were identified for further investigation. Drug candidates were identified for further evaluation in biological experiments, potentially transported to targeted tissues via bioengineered EVs.https://www.frontiersin.org/articles/10.3389/fnagi.2025.1617611/fullAlzheimer’s diseasemultiomicsextracellular vesicles (EV)LASSOCox regressionbiomarkers |
| spellingShingle | Xiao Zhang Xiao Zhang Sanoji Wijenayake Shakhawat Hossain Qian Liu Qian Liu Estimating progression of Alzheimer’s disease with extracellular vesicle-related multi-omics risk models Frontiers in Aging Neuroscience Alzheimer’s disease multiomics extracellular vesicles (EV) LASSO Cox regression biomarkers |
| title | Estimating progression of Alzheimer’s disease with extracellular vesicle-related multi-omics risk models |
| title_full | Estimating progression of Alzheimer’s disease with extracellular vesicle-related multi-omics risk models |
| title_fullStr | Estimating progression of Alzheimer’s disease with extracellular vesicle-related multi-omics risk models |
| title_full_unstemmed | Estimating progression of Alzheimer’s disease with extracellular vesicle-related multi-omics risk models |
| title_short | Estimating progression of Alzheimer’s disease with extracellular vesicle-related multi-omics risk models |
| title_sort | estimating progression of alzheimer s disease with extracellular vesicle related multi omics risk models |
| topic | Alzheimer’s disease multiomics extracellular vesicles (EV) LASSO Cox regression biomarkers |
| url | https://www.frontiersin.org/articles/10.3389/fnagi.2025.1617611/full |
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