Research Paper Volume 15, Issue 19 pp 10389—10406

Novel diagnostic biomarkers of oxidative stress, immunological characterization and experimental validation in Alzheimer’s disease

Di Hu1, *, , Xiaocong Mo2, *, , Luo Jihang2,3, *, , Cheng Huang1, , Hesong Xie1, , Ling Jin4, ,

  • 1 Department of Neurology and Stroke Centre, The First Affiliated Hospital of Jinan University, Guangzhou, China
  • 2 Department of Oncology, The First Affiliated Hospital of Jinan University, Jinan University, Guangzhou, China
  • 3 Department of Infectious Diseases, Affiliated Hospital of Zunyi Medical University, Zunyi, China
  • 4 Department of Traditional Chinese Medicine, The First Affiliated Hospital of Jinan University, Jinan University, Guangzhou, China
* Equal contribution

Received: May 21, 2023       Accepted: September 2, 2023       Published: October 5, 2023
How to Cite

Copyright: © 2023 Hu et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.


Alzheimer’s disease (AD) is a neurodegenerative condition causing cognitive decline. Oxidative stress (OS) is believed to contribute to neuronal death and dysfunction in AD. We conducted a study to identify differentially expressed OS-related genes (DEOSGs) through bioinformatics analysis and experimental validation, aiming to develop a diagnostic model for AD. We analyzed the GSE33000 dataset to identify OS regulator expression profiles and create molecular clusters (C1 and C2) associated with immune cell infiltration using 310 AD samples. Cluster analysis revealed significant heterogeneity in immune infiltration. The ‘WGCNA’ algorithm identified cluster-specific and disease-specific differentially expressed genes (DGEs). Four machine learning models (random forest (RF), support vector machine (SVM), generalized linear model (GLM) and extreme gradient boosting (XGB)) were compared, with GLM performing the best (AUC = 0.812). Five DEOSGs (NFKBIA, PLCE1, CLIC1, SLCO4A1, TRAF3IP2) were identified based on the GLM model. AD subtype prediction accuracy was validated using nomograms and calibration curves. External datasets (GSE122063 and GSE106241) confirmed the expression levels and clinical significance of important genes. Experimental validation through RT-qPCR showed increased expression of NFKBIA, CLIC1, SLCO4A1, TRAF3IP2, and decreased expression of PLCE1 in the temporal cortex of AD mice. This study provides insights for AD research and treatment, particularly focusing on the five model-related DEOSGs.


AD: Alzheimer’s disease; OS: oxidative stress; DEOSGs: differentially expressed genes related to OS; GLM: generalized linear model; XGB: eXtreme gradient boosting; SVM: support vector machine model; RF: random forest model; Aβ: amyloid β-protein; CDF: cumulative distribution function; TOM: topological overlap matrix; WGCNA: weighted gene co-expression network analysis; GSVA: gene set variation analysis; WB: western blotting; IHC: immunohistochemistry; RT-qPCR: Real-time qPCR.