Uncovering the Achilles heel of genetic heterogeneity: machine learning-based classification and immunological properties of necroptosis clusters in Alzheimer’s disease
Open accessAbstract
Background: Alzheimer’s disease (AD) is an age-associated neurodegenerative disease, and the currently available diagnostic modalities and therapeutic agents are unsatisfactory due to its high clinical heterogeneity. Necroptosis is a common type of programmed cell death that has been shown to be activated in AD. Methods: In this study, we first investigated the expression profiles of necroptosis-related genes (NRGs) and the immune landscape of AD based on GSE33000 dataset. Next, the AD samples in the GSE33000 dataset were extracted and subjected to consensus clustering based upon the differentially expressed NRGs. Key genes associated with necroptosis clusters were identified using Weighted Gene Co-Expression Network Analysis (WGCNA) algorithm, and then intersected with the key gene related to AD. Finally, we developed a diagnostic model for AD by comparing four different machine learning approaches. The discrimination performance and clinical relevance of the diagnostic model were assessed using various evaluation metrics, including the nomogram, calibration plot, decision curve analysis (DCA), and independent validation datasets. Results: Aberrant expression patterns of NRGs and specific immune landscape were identified in the AD samples. Consensus clustering revealed that patients in the GSE33000 dataset could be classified into two necroptosis clusters, each with distinct immune landscapes and enriched pathways. The Extreme Gradient Boosting (XGB) was found to be the most optimal diagnostic model for the AD based on the predictive ability and reliability of the models constructed by four machine learning approaches. The five most important variables, including ACAA2, BHLHB4, CACNA2D3, NRN1, and TAC1, were used to construct a five-gene diagnostic model. The constructed nomogram, calibration plot, DCA, and external independent validation datasets exhibited outstanding diagnostic performance for AD and were closely related with the pathologic hallmarks of AD. Conclusion: This work presents a novel diagnostic model that may serve as a framework to study disease heterogeneity and provide a plausible mechanism underlying neuronal loss in AD.
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The interplay between phenotypes and genotypes is part of the heterogeneity of AD.
“The interplay between phenotypes and genotypes is part of the heterogeneity of AD.”
From Recent Advances in Therapeutics for the Treatment of Alzheimer’s Disease (2024) · cites this paper as [192]
Increasing research recognizes etiological and clinical heterogeneity of AD as a hallmark of AD and related diseases (ADRDs), making diagnosis, treatment, and drug development/testing more difficult.
“More and more research is recognizing the etiological and clinical heterogeneity of AD as a hallmark of AD and related diseases (ADRDs), which makes it more difficult to diagnose, treat, and develop and test new medications for AD.”
From Recent Advances in Therapeutics for the Treatment of Alzheimer’s Disease (2024) · cites this paper as [192]
Cognitive, neurological, psychological, and functional deficits in AD are complicated and different due to interactions with environmental factors.
“Cognitive, neurological, psychological, and functional deficits are complicated and different as a result of these interactions with environmental factors.”
From Recent Advances in Therapeutics for the Treatment of Alzheimer’s Disease (2024) · cites this paper as [192]
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Recent Advances in Therapeutics for the Treatment of Alzheimer’s Disease
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- Authors
- Wei, H., Wu, C., Yuan, Y., & Lai, L.
- Affiliations
- The People's Hospital of Guangxi Zhuang Autonomous Region (CN)
- Corresponding author
- Yuan, Yulin
- Lai, Lichuan
- Venue
- Frontiers in Aging Neuroscience
- Publisher
- Frontiers Media SA
- Published
- 2023-09-20
- Volume
- 15
- Pages
- 1249682-1249682
- ISSN
- 1663-4365 (ISSN-L)
- PMC
- PMC10548137
- OpenAlex
- W4386942039
- Cited by
- 3
- References
- 55
- Citations by year
- 2026: 1 · 2024: 2
- FWCI
- 0.53
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Frontiers in Aging Neuroscience (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Health Sciences › Medicine › Physiology › Alzheimer's disease research and treatments
- Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
- Life Sciences › Neuroscience › Neurology › Neuroinflammation and Neurodegeneration Mechanisms
- Keywords
- Alzheimer's disease, necroptosis, necroptosis-related genes, disease heterogeneity, immune landscape, consensus clustering, weighted gene co-expression network analysis, nomogram, XGBoost, ACAA2, CACNA2D3, NRN1, TAC1, programmed cell death, neuronal loss
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