Submitted: 05 Dec 2023
Revision: 11 Dec 2023
Accepted: 13 Dec 2023
ePublished: 29 Dec 2023
EndNote EndNote

(Enw Format - Win & Mac)

BibTeX BibTeX

(Bib Format - Win & Mac)

Bookends Bookends

(Ris Format - Mac only)

EasyBib EasyBib

(Ris Format - Win & Mac)

Medlars Medlars

(Txt Format - Win & Mac)

Mendeley Web Mendeley Web
Mendeley Mendeley

(Ris Format - Win & Mac)

Papers Papers

(Ris Format - Win & Mac)

ProCite ProCite

(Ris Format - Win & Mac)

Reference Manager Reference Manager

(Ris Format - Win only)

Refworks Refworks

(Refworks Format - Win & Mac)

Zotero Zotero

(Ris Format - Firefox Plugin)

Avicenna J Med Biochem. 2023;11(2): 138-145.
doi: 10.34172/ajmb.2475
  Abstract View: 312
  PDF Download: 171

Original Article

MiR-15b and let-7a as Non-invasive Diagnostic Biomarkers of Alzheimer’s Disease Using an Artificial Neural Network

Masoumeh Darvishi Talemi 1 ORCID logo, Leili Tapak 2, Alireza Rastgoo Haghi 3, Parisa Ahghari 4, Shadi Moradi 4, Saeid Afshar 4,5* ORCID logo

1 Department of Biology, Faculty of Basic Sciences, Bu-Ali Sina University, Hamedan, Iran
2 Department of Biostatistics, School of Public Health, Hamadan University of Medical Sciences, Hamadan, Iran
3 Department of Pathology, Hamadan University of Medical Sciences, Hamadan, Iran
4 Research Center for Molecular Medicine, Hamadan University of Medical Sciences, Hamadan, Iran
5 Department of Medical Biotechnology, School of Advanced Medical Sciences and Technologies, Hamadan University of Medical Sciences, Hamadan, Iran
*Corresponding Author: Saeid Afshar, Email: safshar.h@gmail.com, Email: safshar.h@gmail.com


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 GSE122063 and microRNA (miRNA) expression data series GSE90828 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 1388 DEGs were identified in AD patients compared to the control group, and 11 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-15b-3p and let-7a-5p, while TLR4, another hub gene, was targeted by miR-15b-3p. The MLP-NN constructed using both hsa-let-7a-5p and hsa-miR-15b-3p achieved a sensitivity of 0.857 and an area under the curve of 0.917 in detecting Alzheimer’s patients.

Conclusion: Our findings suggest that miR-15b-3p, by targeting EGFR and TLR4, and let-7a-5p, by targeting EGFR, may play a significant role in AD. Additionally, the constructed ANN utilizing the expression levels of plasma miR-15b-3p and let-7a-5p could serve as a potential non-invasive diagnostic tool with high sensitivity for AD detection.

Please cite this article as follows: Darvishi Talemi M, Tapak L, Rastgoo Haghi A, Ahghari P, Moradi S, Afshar S. Mir-15b and let-7a as non-invasive diagnostic biomarkers of alzheimer’s disease using an artificial neural network. Avicenna J Med Biochem. 2023; 11(2):138-145. doi:10.34172/ajmb.2475
First Name
Last Name
Email Address
Security code

Abstract View: 313

Your browser does not support the canvas element.

PDF Download: 171

Your browser does not support the canvas element.