MiR-۱۵b and let-۷a as Non-invasive Diagnostic Biomarkers of Alzheimer’s Disease Using an Artificial Neural Network

Background: Gaining insight into the underlying molecular mechanisms of Alzheimer’s disease (AD) is crucial. Objectives: This study aimed to employ a systems biology approach to identify new non-invasive diagnostic biomarkers for AD. Methods: Gene expression data series GSE۱۲۲۰۶۳ and microRNA (miRNA) expression data series GSE۹۰۸۲۸ were obtained from the Gene Expression Omnibus database. The Limma package under R software was used to assess differentially expressed miRNAs and differentially expressed genes (DEGs). Afterward the protein-protein interaction (PPI) network was constructed by the STRING software and evaluated with Cytoscape software. The multilayer perceptron neural network (MLP-NN), a widely used artificial neural network (ANN), was employed to classify two groups. Results: A total of ۱۳۸۸ DEGs were identified in AD patients compared to the control group, and ۱۱ differentially expressed miRNAs were found in patients with mild cognitive impairment (MCI) in comparison to the control group. The results revealed that EGFR, identified as a hub gene, was targeted by miR-۱۵b-۳p and let-۷a-۵p, while TLR۴, another hub gene, was targeted by miR-۱۵b-۳p. The MLP-NN constructed using both hsa-let-۷a-۵p and hsa-miR-۱۵b-۳p achieved a sensitivity of ۰.۸۵۷ and an area under the curve of ۰.۹۱۷ in detecting Alzheimer’s patients. Conclusion: Our findings suggest that miR-۱۵b-۳p, by targeting EGFR and TLR۴, and let-۷a-۵p, by targeting EGFR, may play a significant role in AD. Additionally, the constructed ANN utilizing the expression levels of plasma miR-۱۵b-۳p and let-۷a-۵p could serve as a potential non-invasive diagnostic tool with high sensitivity for AD detection.