Potential of hsa-miR200a-3p and hsa-miR502-3p as blood-based biomarker for Alzheimer's disease

miR-129-5p as a biomarker for pathology and cognitive decline in Alzheimer's disease

BACKGROUND

Alzheimer's dementia (AD) pathogenesis involves complex mechanisms, including microRNA (miRNA) dysregulation. Integrative network and machine learning analysis of miRNA can provide insights into AD pathology and prognostic/diagnostic biomarkers.

METHODS

We performed co-expression network analysis to identify network modules associated with AD, its neuropathology markers, and cognition using brain tissue miRNA profiles from the Religious Orders Study and Rush Memory and Aging Project (ROS/MAP) (N = 702) as a discovery dataset. We performed association analysis of hub miRNAs with AD, its neuropathology markers, and cognition. After selecting target genes of the hub miRNAs, we performed association analysis of the hub miRNAs with their target genes and then performed pathway-based enrichment analysis. For replication, we performed a consensus miRNA co-expression network analysis using the ROS/MAP dataset and an independent dataset (N = 16) from the Gene Expression Omnibus (GEO). Furthermore, we performed a machine learning approach to assess the performance of hub miRNAs for AD classification.

RESULTS

Network analysis identified a glucose metabolism pathway-enriched module (M3) as significantly associated with AD and cognition. Five hub miRNAs (miR-129-5p, miR-433, miR-1260, miR-200a, and miR-221) of M3 had significant associations with AD clinical and/or pathologic traits, with miR129-5p by far the strongest across all phenotypes. Gene-set enrichment analysis of target genes associated with their corresponding hub miRNAs identified significantly enriched biological pathways including ErbB, AMPK, MAPK, and mTOR signaling pathways. Consensus network analysis identified two AD-associated consensus network modules and two hub miRNAs (miR-129-5p and miR-221). Machine learning analysis showed that the AD classification performance (area under the curve (AUC) = 0.807) of age, sex, and APOE ε4 carrier status was significantly improved by 6.3% with inclusion of five AD-associated hub miRNAs.

CONCLUSIONS

Integrative network and machine learning analysis identified miRNA signatures, especially miR-129-5p, as associated with AD, its neuropathology markers, and cognition, enhancing our understanding of AD pathogenesis and leading to better performance of AD classification as potential diagnostic/prognostic biomarkers.

Application of mesenchymal stem cells (MSCs) in neurodegenerative disorders: History, findings, and prospective challenges

Over the past few decades, the application of mesenchymal stem cells has captured the attention of researchers and practitioners worldwide. These cells can be obtained from practically every tissue in the body and are used to treat a broad variety of conditions, most notably neurological diseases such as Parkinson's disease, multiple sclerosis, amyotrophic lateral sclerosis, and Alzheimer's disease. Studies are still being conducted, and the results of these studies have led to the identification of several different molecular pathways involved in the neuroglial speciation process. These molecular systems are closely regulated and interconnected due to the coordinated efforts of many components that make up the machinery responsible for cell signaling. Within the scope of this study, we compared and contrasted the numerous mesenchymal cell sources and their cellular features. These many sources of mesenchymal cells included adipocyte cells, fetal umbilical cord tissue, and bone marrow. In addition, we investigated whether these cells can potentially treat and modify neurodegenerative illnesses.