Utilizing machine learning algorithms to identify biomarkers associated with Alzheimer's disease and ferroptosis-related genes.

IF 1.4 4区医学 Q4 ENGINEERING, BIOMEDICAL

Technology and Health Care Pub Date : 2025-03-03 DOI:10.1177/09287329251322278

Di Wang, Chunsheng Lin, Gang Liu, Xin Wang, Shengwang Han, Zengxin Han

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引用次数: 0

Abstract

Background: Alzheimer's disease (AD) is a complex neurodegenerative disorder that complicates our understanding of its origins. Identifying AD-specific biomarkers can reveal its mechanisms and foster the development of innovative diagnostics and therapies, aiming to unlock new ways to combat this pervasive condition.

Methods: We analyzed gene expression data using Weighted Gene Co-expression Network Analysis (WGCNA) and machine learning (random forest, lasso regression, and SVM-REF) to differentiate AD patients from controls and explore gene functions.

Results: We identified 641 differentially expressed genes (DEGs) and 22 co-expressed genes, with functional enrichment analysis revealing their involvement in immune responses. Notably, EGR1 emerged as a potential diagnostic and therapeutic target.

Conclusion: In our study, we applied WGCNA, DEGs and diverse machine learning approaches to uncover potential biomarkers linked to Alzheimer's Disease (AD) and ferroptosis. A particular hub gene emerged as a promising candidate for novel diagnostic and therapeutic markers specifically within the context of ferroptosis in AD. This discovery sheds new light on the pathogenesis of AD, potentially facilitating the development of groundbreaking diagnostic and therapeutic techniques.

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利用机器学习算法确定与阿尔茨海默病和铁蛋白沉积相关基因有关的生物标记物。

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来源期刊

Technology and Health Care HEALTH CARE SCIENCES & SERVICES-ENGINEERING, BIOMEDICAL

CiteScore

2.10

自引率

6.20%

发文量

282

审稿时长

>12 weeks

期刊介绍： Technology and Health Care is intended to serve as a forum for the presentation of original articles and technical notes, observing rigorous scientific standards. Furthermore, upon invitation, reviews, tutorials, discussion papers and minisymposia are featured. The main focus of THC is related to the overlapping areas of engineering and medicine. The following types of contributions are considered: 1.Original articles: New concepts, procedures and devices associated with the use of technology in medical research and clinical practice are presented to a readership with a widespread background in engineering and/or medicine. In particular, the clinical benefit deriving from the application of engineering methods and devices in clinical medicine should be demonstrated. Typically, full length original contributions have a length of 4000 words, thereby taking duly into account figures and tables. 2.Technical Notes and Short Communications: Technical Notes relate to novel technical developments with relevance for clinical medicine. In Short Communications, clinical applications are shortly described. 3.Both Technical Notes and Short Communications typically have a length of 1500 words. Reviews and Tutorials (upon invitation only): Tutorial and educational articles for persons with a primarily medical background on principles of engineering with particular significance for biomedical applications and vice versa are presented. The Editorial Board is responsible for the selection of topics. 4.Minisymposia (upon invitation only): Under the leadership of a Special Editor, controversial or important issues relating to health care are highlighted and discussed by various authors. 5.Letters to the Editors: Discussions or short statements (not indexed).