Uncovering the Achilles heel of genetic heterogeneity: machine learning-based classification and immunological properties of necroptosis clusters in Alzheimer’s disease

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Alzheimer’s disease remains difficult to diagnose and treat due to pronounced clinical heterogeneity. This study integrates necroptosis-related gene expression with immune landscape analysis using the GSE33000 dataset, then applies consensus clustering to group samples into two necroptosis clusters with distinct immune signatures and pathway enrichments. Key genes are derived through weighted gene co-expression network analysis, intersected with Alzheimer’s disease-relevant genes, and used to compare four machine learning classification strategies. Extreme gradient boosting yields the most reliable diagnostic performance, supported by nomogram, calibration, decision curve analysis, and external validation, highlighting five informative variables linked to Alzheimer’s pathological features.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages15

Reading Time38 min

AccessFree to download

Introduction

Methods

Data and gene expression analysis

Consensus clustering and network analysis

Machine learning diagnostic modeling

Results

Conclusion

TYPE Original Research PUBLISHED 20 September 2023 DOI 10.3389/fnagi.2023.1249682 OPEN ACCESS EDITED BY Vijaykumar Muley, National Autonomous University of Mexico, Mexico REVIEWED BY Han She, Army Medical University, China Jinchao Hou, Washington University in St. Louis, United States *CORRESPONDENCE Yulin Yuan [yuanyulin202301@163.com](yuanyulin202301@163.com)[ ](yuanyulin202301@163.com)Lichuan Lai [michaellailichuan@163.com](michaellailichuan@163.com) †These authors have contributed equally to this work RECEIVED 29 June 2023 ACCEPTED 30 August 2023 PUBLISHED 20 September 2023 CITATION Wei H, Wu C, Yuan Y and Lai L (2023) Uncovering the Achilles heel of genetic heterogeneity: machine learning-based classification and immunological properties of necroptosis clusters in Alzheimer’s disease. Front. Aging Neurosci. 15:1249682 . doi: 10.3389/fnagi.2023.1249682 COPYRIGHT © 2023 Wei, Wu, Yuan and Lai. This is an openaccess article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. Uncovering the Achilles heel of genetic heterogeneity: machine learning-based classification and immunological properties of necroptosis clusters in Alzheimer’s disease Huangwei Wei 1†, Chunle Wu 2†, Yulin Yuan3* and Lichuan Lai3* 1 Department of Neurology, The People’s Hospital of Guangxi Zhuang Autonomous Region, Nanning, China, 2 Department of Blood Transfusion, The People’s Hospital of Guangxi Zhuang Autonomous Region, Nanning, China, 3 Department of Laboratory, The People’s Hospital of Guangxi Zhuang Autonomous Region, Nanning, China 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 necroptosisrelated 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 fivegene 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 diagn

FAQs

What clinical problem does the study address in Alzheimer’s disease?

It targets the difficulty of diagnosis and inadequate therapies caused by high clinical heterogeneity in Alzheimer’s disease.

How were necroptosis clusters and immune landscapes identified?

The study used the GSE33000 dataset to analyze necroptosis-related genes and immune features, then applied consensus clustering to derive two necroptosis clusters with distinct immune landscapes.

Which machine learning model performed best for AD diagnosis?

Extreme gradient boosting (XGB) was the most optimal model, and its diagnostic value was confirmed through nomogram, calibration plots, decision curve analysis, and external independent validation.

Aug 5, 2026

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