Identification of diagnostic biomarkers in Alzheimer's disease by integrated bioinformatic analysis and machine learning strategies

Boru Jin; Xiaoqin Cheng; Guoqiang Fei; Shaoming Sang; Chunjiu Zhong

doi:10.3389/fnagi.2023.1169620

Identification of diagnostic biomarkers in Alzheimer's disease by integrated bioinformatic analysis and machine learning strategies

Front Aging Neurosci. 2023 Jun 26:15:1169620. doi: 10.3389/fnagi.2023.1169620. eCollection 2023.

Authors

Boru Jin^#^{1

2}, Xiaoqin Cheng^#^{1

2}, Guoqiang Fei^{1

2}, Shaoming Sang³, Chunjiu Zhong^{1

2}

Affiliations

¹ Department of Neurology, Zhongshan Hospital, Fudan University, Shanghai, China.
² Collaborative Innovation Center for Brain Science, Fudan University, Shanghai, China.
³ Shanghai Raising Pharmaceutical Technology Co., Ltd. Shanghai, China.

^# Contributed equally.

Abstract

Background: Alzheimer's disease (AD) is the most prevalent form of dementia, and is becoming one of the most burdening and lethal diseases. More useful biomarkers for diagnosing AD and reflecting the disease progression are in need and of significance.

Methods: The integrated bioinformatic analysis combined with machine-learning strategies was applied for exploring crucial functional pathways and identifying diagnostic biomarkers of AD. Four datasets (GSE5281, GSE131617, GSE48350, and GSE84422) with samples of AD frontal cortex are integrated as experimental datasets, and another two datasets (GSE33000 and GSE44772) with samples of AD frontal cortex were used to perform validation analyses. Functional Correlation enrichment analyses were conducted based on Gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and the Reactome database to reveal AD-associated biological functions and key pathways. Four models were employed to screen the potential diagnostic biomarkers, including one bioinformatic analysis of Weighted gene co-expression network analysis (WGCNA)and three machine-learning algorithms: Least absolute shrinkage and selection operator (LASSO), support vector machine-recursive feature elimination (SVM-RFE) and random forest (RF) analysis. The correlation analysis was performed to explore the correlation between the identified biomarkers with CDR scores and Braak staging.

Results: The pathways of the immune response and oxidative stress were identified as playing a crucial role during AD. Thioredoxin interacting protein (TXNIP), early growth response 1 (EGR1), and insulin-like growth factor binding protein 5 (IGFBP5) were screened as diagnostic markers of AD. The diagnostic efficacy of TXNIP, EGR1, and IGFBP5 was validated with corresponding AUCs of 0.857, 0.888, and 0.856 in dataset GSE33000, 0.867, 0.909, and 0.841 in dataset GSE44770. And the AUCs of the combination of these three biomarkers as a diagnostic tool for AD were 0.954 and 0.938 in the two verification datasets.

Conclusion: The pathways of immune response and oxidative stress can play a crucial role in the pathogenesis of AD. TXNIP, EGR1, and IGFBP5 are useful biomarkers for diagnosing AD and their mRNA level may reflect the development of the disease by correlation with the CDR scores and Breaking staging.

Keywords: Alzheimer’s disease; Bioinformatics; biomarker; diagnosis; machine learning (ML).

Grants and funding

This study was supported by grants from the National Natural Science Foundation of China (81901081, 81870822, 91332201, and 82171408), Shanghai Municipal Science and Technology Major Project, and the Natural Science Foundation of Fujian Province (2020CXB049).