International Journal of Molecular Medical Science, 2025, Vol.15, No.4, 175-184 http://medscipublisher.com/index.php/ijmms 180 results of cognitive ability tests, such as MMSE and ADAS-cog. For example, the lower the levels of miR-342-3p and miR-193b, the poorer the cognitive score is usually, indicating that they may indicate the severity of the disease (Cheng et al., 2014; Lugli et al., 2015; Yang et al., 2018; Manna et al., 2020). This association has been verified in different patient groups and detection methods. Figure 2 Exosomal microRNAs qRT-PCR (Adopted from Duan et al., 2024) Image caption: (A) The difference of exosomal microRNA level between AD and ND is compared with nonparametric tests Mann-Whitney U-test. The y-axis indicates microRNA expression levels by log10 change; *p<0.05,***p<0.001; The expression of miR-125b-1-3p in serum exosomes of AD is upregulated significantly (p<0.001); The exosomal miR-193a-5p of AD is downregulated significantly comparing with ND (p=0.020); The expression of miR-378a-3p in serum exosomes of AD is upregulated significantly (p<0.001); Comparing with ND, the expression of exosomal miR-378i of AD is downregulated significantly (p=0.042); The expression of miR-451a in exosomes of AD is upregulated significantly (p<0.001) (B); The receiver operating characteristic (ROC) curve of serum exosomal miR-125b-1-3p; Area Under the Curve(AUC) of miR-125b-1-3p is 0.765 to distinguish AD from normal with a sensitivity and specificity of 82.1% and 67.7% (C); The ROC curve of serum exosomal miR-451a. AUC of miR-451a is 0.728 to distinguish AD from ND with a sensitivity and specificity of 67.9% and 72.6%, respectively (Adopted from Duan et al., 2024) In addition, combining exosome miRNA information with brain scan data (such as amyloid PET) and known risk factors (such as APOE genotype) can further improve the accuracy of diagnosis and may even help identify high-risk populations before symptoms appear (Cheng et al., 2014; Yang et al., 2018). This combined approach highlights the potential of exosomal mirnas in the blood as convenient and non-invasive biomarkers, which can reflect the molecular lesions and clinical symptoms of AD. 6 Path Analysis and Prediction Models Based on Different Mirnas 6.1 Target gene prediction and GO/KEGG pathway results To identify which genes (target genes) are regulated by differentially expressed mirnas, comprehensive tools such as TargetScan, miRTarBase and miRDB are usually employed. These tools predict the interaction between mirnas and genes through sequence matching, conservation degree and experimental data, and provide a list of genes that each miRNA may regulate (Lu et al., 2012; Chen and Wang, 2019). After identifying the target genes, pathway enrichment analysis is conducted using resources such as GO and KEGG, with the aim of discovering which biological activities or signaling pathways are particularly abundant (significantly enriched) in the target genes (Lu et al., 2012; Backes et al., 2016; Garcia-Moreno et al., 2022; Tastsoglou et al., 2023). This is helpful for understanding which functions these mirnas mainly affect. Enrichment analysis commonly uses statistical methods (such as hypergeometric tests) to evaluate which pathways are the most important, and corrections are made to reduce errors (Lu et al., 2012; Backes et al., 2016; Garcia-Moreno et al., 2022). Some online tools (such as Dia-MirPath, miEAA, MIENTURNET) can visually display the key pathways and gene networks affected by miRNA (Backes et al., 2016; Licursi et al., 2019; Aparicio-Puerta et al., 2023; Tastsoglou et al., 2023). These analyses often identify pathways associated with Alzheimer's disease (AD), such as MAPK signaling, cell death (apoptosis), and synaptic function, as groups of target genes have been found in these categories (Lu et al., 2012; Pereira et al., 2024).
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