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miR-129-5p as a biomarker for pathology and cognitive decline in Alzheimer's disease
Sang-Won Han1, Jung-Min Pyun2, Paula J Bice3
1Department of Neurology, Chuncheon Sacred Heart Hospital, Hallym University College of Medicine, 77 Sakju-ro, Chuncheon-si, Gangwon-do, 24253, Republic of Korea.
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.
Insights
MicroRNA (miRNA) dysregulation is implicated in Alzheimer's dementia (AD). This study identified specific miRNAs, particularly miR-129-5p, associated with AD, improving diagnostic classification and offering insights into disease mechanisms.
Area of Science:
- Neuroscience
- Genetics
- Biochemistry
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.
Purpose of the Study:
- To identify miRNA signatures associated with AD, its neuropathology markers, and cognition.
- To evaluate the potential of these miRNAs as diagnostic and prognostic biomarkers for AD.
Main Methods:
- Co-expression network analysis of miRNA profiles from human brain tissue (ROS/MAP dataset).
- Association analysis of hub miRNAs with AD traits and their target genes, followed by pathway enrichment analysis.
- Consensus network analysis for replication and machine learning for AD classification performance assessment.
Main Results:
- A glucose metabolism pathway-enriched module (M3) was significantly associated with AD and cognition.
- Five hub miRNAs (miR-129-5p, miR-433, miR-1260, miR-200a, miR-221) showed significant associations with AD traits, with miR-129-5p being the strongest.
- Machine learning models incorporating these miRNAs significantly improved AD classification performance (AUC = 0.807, +6.3% improvement).
Conclusions:
- Integrative network and machine learning analyses identified specific miRNA signatures, notably miR-129-5p, linked to AD.
- These findings enhance understanding of AD pathogenesis and demonstrate the potential of miRNAs as diagnostic/prognostic biomarkers for improved AD classification.
