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Alzheimer's disease (AD) is the most common cause of dementia, and one of the most common health problems all over the world. However, the specific molecular mechanisms of AD have not been fully investigated. The current investigation aimed to elucidate potential key candidate genes and signaling pathways in AD. Next generation sequencing (NGS) dataset GSE203206 was downloaded from the Gene Expression Omnibus (GEO) database, which included data from 39 AD samples and 8 normal control samples. Differentially expressed genes (DEGs) were identified using t-tests in the limma R bioconductor package. These DEGs were subsequently investigated by Gene ontology (GO) and pathway enrichment analysis, and a protein-protein interaction (PPI) network and modules were constructed and analyzed. The miRNA-hub gene regulatory network and TF-hub gene regulatory network analysis were performed to identify key miRNAs and TFs. The receiver operating characteristic (ROC) curve analysis was performed to estimate the clinical diagnostic value of the hub genes. A total of 958 DEGs, including 479 up regulated genes and 479 down regulated genes, were screened between AD and normal control samples. GO and pathway enrichment analysis results revealed that the up regulated genes were mainly enriched in response to stimulus, cytoplasm, small molecule binding and signal transduction, whereas down regulated genes were mainly enriched in multicellular organism development, cell junction, ion binding and cardiac conduction. The PPI network contained 4886 nodes and 10342 edges. HSP90AA1, FN1, KIT, YAP1, LSM2, SKP1, EIF5A2, TAF9, DDX39B and CDK7 were identified as the top hub genes. The regulatory network analysis revealed that microRNA (miRNA) hsa-mir-545-3p and hsa-miR-548f-5p, and transcription factor (TF) PLAG1 and MEF2A might be involved in the development of AD. These findings provide new insights into the pathogenesis of AD. The hub genes, miRNAs and TFs have the potential to be used as diagnostic and therapeutic markers.
Alzheimer's disease (AD) is the most common cause of dementia, and one of the most common health problems all over the world. However, the specific molecular mechanisms of AD have not been fully investigated. The current investigation aimed to elucidate potential key candidate genes and signaling pathways in AD. Next generation sequencing (NGS) dataset GSE203206 was downloaded from the Gene Expression Omnibus (GEO) database, which included data from 39 AD samples and 8 normal control samples. Differentially expressed genes (DEGs) were identified using t-tests in the limma R bioconductor package. These DEGs were subsequently investigated by Gene ontology (GO) and pathway enrichment analysis, and a protein-protein interaction (PPI) network and modules were constructed and analyzed. The miRNA-hub gene regulatory network and TF-hub gene regulatory network analysis were performed to identify key miRNAs and TFs. The receiver operating characteristic (ROC) curve analysis was performed to estimate the clinical diagnostic value of the hub genes. A total of 958 DEGs, including 479 up regulated genes and 479 down regulated genes, were screened between AD and normal control samples. GO and pathway enrichment analysis results revealed that the up regulated genes were mainly enriched in response to stimulus, cytoplasm, small molecule binding and signal transduction, whereas down regulated genes were mainly enriched in multicellular organism development, cell junction, ion binding and cardiac conduction. The PPI network contained 4886 nodes and 10342 edges. HSP90AA1, FN1, KIT, YAP1, LSM2, SKP1, EIF5A2, TAF9, DDX39B and CDK7 were identified as the top hub genes. The regulatory network analysis revealed that microRNA (miRNA) hsa-mir-545-3p and hsa-miR-548f-5p, and transcription factor (TF) PLAG1 and MEF2A might be involved in the development of AD. These findings provide new insights into the pathogenesis of AD. The hub genes, miRNAs and TFs have the potential to be used as diagnostic and therapeutic markers.
Alzheimer’s disease (AD) is a neurodegenerative disease characterized by memory loss and progressive deterioration of cognitive functions. Being able to identify reliable biomarkers in easily available body fluids such as blood plasma is vital for the disease. To achieve this, we used a technique that applied human plasma to organotypic brain slice culture via microcontact printing. After a 2-week culture period, we performed immunolabeling for neurofilament and myelin oligodendrocyte glycoprotein (MOG) to visualize newly formed nerve fibers and oligodendrocytes. There was no significant change in the number of new nerve fibers in the AD plasma group compared to the healthy control group, while the length of the produced fibers significantly decreased. A significant increase in the number of MOG+ dots around these new fibers was detected in the patient group. According to our hypothesis, there are factors in the plasma of AD patients that affect the growth of new nerve fibers, which also affect the oligodendrocytes. Based on these findings, we selected the most promising plasma samples and conducted mass spectrometry using a differential approach and we identified three putative biomarkers: aldehyde-dehydrogenase 1A1, alpha-synuclein and protein S100-A4. Our method represents a novel and innovative approach for translating research findings from mouse models to human applications.
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