miRNAs in cerebrospinal fluid associated with Alzheimer's disease: A systematic review and pathway analysis using a data mining and machine learning approach

• Published in: Journal of neurochemistry 2024-06, Vol.168 (6), p.977-994
• Main Authors: Pereira, Jessica Diniz, Teixeira, Lívia Cristina Ribeiro, Mamede, Izabela, Alves, Michelle Teodoro, Caramelli, Paulo, Luizon, Marcelo Rizzatti, Veloso, Adriano Alonso, Gomes, Karina Braga
• Format: Article
• Language: English
• Subjects: Alzheimer Disease - cerebrospinal fluid; Alzheimer Disease - diagnosis; Alzheimer Disease - genetics; Alzheimer's disease; Biomarkers; Biomarkers - cerebrospinal fluid; Cerebrospinal fluid; Data Mining; Dementia disorders; Diagnosis; DNA damage; Gene expression; Growth factors; Humans; Learning algorithms; Machine Learning; MAP kinase; MicroRNAs; MicroRNAs - cerebrospinal fluid; MicroRNAs - genetics; miRNA; miRNAs; Neurodegenerative diseases; Systematic review
• ISSN: 0022-3042; 1471-4159; 1471-4159
• DOI: 10.1111/jnc.16060

Alzheimer's disease (AD) is the most common type and accounts for 60%–70% of the reported cases of dementia. MicroRNAs (miRNAs) are small non‐coding RNAs that play a crucial role in gene expression regulation. Although the diagnosis of AD is primarily clinical, several miRNAs have been associated with AD and considered as potential markers for diagnosis and progression of AD. We sought to match AD‐related miRNAs in cerebrospinal fluid (CSF) found in the GeoDataSets, evaluated by machine learning, with miRNAs listed in a systematic review, and a pathway analysis. Using machine learning approaches, we identified most differentially expressed miRNAs in Gene Expression Omnibus (GEO), which were validated by the systematic review, using the acronym PECO—Population (P): Patients with AD, Exposure (E): expression of miRNAs, Comparison (C): Healthy individuals, and Objective (O): miRNAs differentially expressed in CSF. Additionally, pathway enrichment analysis was performed to identify the main pathways involving at least four miRNAs selected. Four miRNAs were identified for differentiating between patients with and without AD in machine learning combined to systematic review, and followed the pathways analysis: miRNA‐30a‐3p, miRNA‐193a‐5p, miRNA‐143‐3p, miRNA‐145‐5p. The pathways epidermal growth factor, MAPK, TGF‐beta and ATM‐dependent DNA damage response, were regulated by these miRNAs, but only the MAPK pathway presented higher relevance after a randomic pathway analysis. These findings have the potential to assist in the development of diagnostic tests for AD using miRNAs as biomarkers, as well as provide understanding of the relationship between different pathophysiological mechanisms of AD. An integrative review aimed to identify key miRNAs for distinguishing AD patients from cognitively healthy individuals. Notable miRNAs included miRNA‐30a‐3p, miRNA‐193a‐5p, miRNA‐143‐3p, miRNA‐145‐5p. Pathway enrichment analysis highlighted the significance of the MAPK pathway, which plays a vital role in AD‐related events. These findings offer potential for AD diagnostic tests and monitoring disease progression. Furthermore, further exploration of these signalling pathways contributes to a deeper understanding of AD pathophysiology.