Development and validation of a 13-gene signature associated with immune function for the detection of Alzheimer's disease

Min Zhu; Tingting Hou; Longfei Jia; Qihua Tan; Chengxuan Qiu; Yifeng Du; Alzheimer's Disease Neuroimaging Initiative

doi:10.1016/j.neurobiolaging.2022.12.014

Development and validation of a 13-gene signature associated with immune function for the detection of Alzheimer's disease

Neurobiol Aging. 2023 May:125:62-73. doi: 10.1016/j.neurobiolaging.2022.12.014. Epub 2022 Dec 31.

Authors

Min Zhu¹, Tingting Hou², Longfei Jia³, Qihua Tan⁴, Chengxuan Qiu⁵, Yifeng Du⁶; Alzheimer's Disease Neuroimaging Initiative

Affiliations

¹ Department of Neurology, Shandong Provincial Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China; Department of Neurology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China.
² Department of Neurology, Shandong Provincial Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China; Department of Neurology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China. Electronic address: houtingting@sdfmu.edu.cn.
³ Innovation Center for Neurological Disorders and Department of Neurology, Xuanwu Hospital, Capital Medical University, National Clinical Research Center for Geriatric Diseases, Beijing, China.
⁴ Department of Public Health, Epidemiology and Biostatistics, University of Southern Denmark, Odense, Denmark.
⁵ Department of Neurology, Shandong Provincial Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China; Department of Neurobiology, Care Sciences and Society, Aging Research Center, Karolinska Institutet and Stockholm University, Stockholm, Sweden. Electronic address: chengxuan.qiu@ki.se.
⁶ Department of Neurology, Shandong Provincial Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China; Department of Neurology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China. Electronic address: duyifeng2013@163.com.

PMID: 36842362
DOI: 10.1016/j.neurobiolaging.2022.12.014

Abstract

Current knowledge of Alzheimer's disease (AD) etiology and effective therapy remains limited. Thus, the identification of biomarkers is crucial to improve the detection and treatment of patients with AD. Using robust rank aggregation method to analyze the microarray data from Gene Expression Omnibus database, we identified 1138 differentially expressed genes in AD. We then explored 13 hub genes by weighted gene co-expression network analysis, least absolute shrinkage, and selection operator, and logistic regression in the training dataset. The detection model, which composed of CD163, CDC42SE1, CECR6, CSF1R, CYP27A1, EIF4E3, H2AFJ, IFIT2, IL10RA, KIAA1324, PSTPIP1, SLA, and TBC1D2 genes, along with APOE gene, showed that the area under the curve for detecting AD was 0.821 (95% confidence interval [CI] = 0.782-0.861) and the model was validated in ADNI dataset (area under the curve = 0.776; 95%CI = 0.686-0.865). Notably, the 13 genes in the model were highly enriched in immune function. These findings have implications for the detection and therapeutic target of AD.

Keywords: Alzheimer's disease; Apolipoprotein E; Detection model; Immune function.

Publication types

Research Support, Non-U.S. Gov't
Research Support, N.I.H., Extramural
Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

Alzheimer Disease* / diagnosis
Alzheimer Disease* / genetics
Databases, Factual
Gene Expression Profiling
Humans
Immunity
Microarray Analysis

Abstract

Publication types

MeSH terms

Grants and funding