Application of weighted co-expression network analysis and machine learning to identify the pathological mechanism of Alzheimer's disease.
Aberrant deposits of neurofibrillary tangles (NFT), the main characteristic of Alzheimer's disease (AD), are highly related to cognitive impairment. However, the pathological mechanism of NFT formation is still unclear. This study explored dierences in gene expression patterns in multiple brain regi...
| Publicado en: | Frontiers in Aging Neuroscience Vol. 14; pp. 1 - 11 |
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| Autores principales: | , , , , , , , , , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Frontiers Media S.A.
7/13/2022
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=158257585&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 158257585 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 16634365 BG2U jtl: Frontiers in Aging Neuroscience issn: 16634365 maglogo: N pubinfo: dt: 7/13/2022 vid: 14 pid: 40038 pub: Frontiers Media S.A. artinfo: ui: 158257585 158257585 158257585 10.3389/fnagi.2022.837770 158257585 ppf: 1 ppct: 10 formats: tig: atl: Application of weighted co-expression network analysis and machine learning to identify the pathological mechanism of Alzheimer's disease. aug: au: Keping Chai Xiaolin Zhang Shufang Chen Huaqian Gu Huitao Tang Panlong Cao Gangqiang Wang Weiping Ye Feng Wan Jiawei Liang Daojiang Shen affil: Department of Pediatrics, Zhejiang Hospital, Hangzhou, China sug: subj: Alzheimer's Disease Pathology Neurons Pathology Gene Expression Profiling Evaluation Machine Learning Utilization Human Nerve Tissue Proteins Brain Anatomy and Histology Random Forest Neurodegenerative Diseases Pathology Genomics Signal Transduction ab: Aberrant deposits of neurofibrillary tangles (NFT), the main characteristic of Alzheimer's disease (AD), are highly related to cognitive impairment. However, the pathological mechanism of NFT formation is still unclear. This study explored dierences in gene expression patterns in multiple brain regions [entorhinal, temporal, and frontal cortex (EC, TC, FC)] with distinct Braak stages (0-VI), and identified the hub genes via weighted gene co-expression network analysis (WGCNA) and machine learning. For WGCNA, consensus modules were detected and correlated with the single sample gene set enrichment analysis (ssGSEA) scores. Overlapping the dierentially expressed genes (DEGs, Braak stages 0 vs. I-VI) with that in the interestmodule, metascape analysis, and Random Forest were conducted to explore the function of overlapping genes and obtain the most significant genes. We found that the three brain regions have high similarities in the gene expression pattern and that oxidative damage plays a vital role in NFT formation via machine learning. Through further filtering of genes from interested modules by Random Forest, we screened out key genes, such as LYN, LAPTM5, and IFI30. These key genes, including LYN, LAPTM5, and ARHGDIB, may play an important role in the development of AD through the inflammatory response pathway mediated by microglia. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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