Research Paper Volume 13, Issue 11 pp 14940—14967

Identification of key transcriptome biomarkers based on a vital gene module associated with pathological changes in Alzheimer’s disease

Tong Zhang1, , Yang Shen1, , Yiqing Guo1, , Junyan Yao1, ,

  • 1 Department of Anesthesiology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China

Received: August 27, 2020       Accepted: April 5, 2021       Published: May 24, 2021
How to Cite

Copyright: © 2021 Zhang 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.


Dysregulation of transcriptome expression has been reported to play an increasingly significant role in AD. In this study, we firstly identified a vital gene module associated with the accumulation of β-amyloid (Aβ) and phosphorylated tau (p-tau) using the WGCNA method. The vital module, named target module, was then employed for the identification of key transcriptome biomarkers. For coding RNA, GNA13 and GJA1 were identified as key biomarkers based on ROC analysis. As for non-coding RNA, MEG3, miR-106a-3p, and miR-24-3p were determined as key biomarkers based on analysis of a ceRNA network and ROC analysis. Experimental analyses firstly confirmed that GNA13, GJA1, and ROCK2, a downstream effector of GNA13, were all increased in 5XFAD mice, compared to littermate mice. Moreover, their expression was increased with aging in 5XFAD mice, as Aβ and p-tau pathology developed. Besides, the expression of key ncRNA biomarkers was verified to be decreased in 5XFAD mice. GSEA results indicated that GNA13 and GJA1 were respectively involved in ribosome and spliceosome dysfunction. MEG3, miR-106a-3p, and miR-24-3p were identified to be involved in MAPK pathway and PI3K-Akt pathway based on enrichment analysis. In summary, we identified several key transcriptome biomarkers, which promoted the prediction and diagnosis of AD.


AD: Alzheimer’s disease; AsymAD: asymptomatic AD; 5XFAD: five familial AD mutations; LM: littermate; Aβ: β-amyloid; β-CTF: β-C-terminal fragment; p-tau: phosphorylated tau; NFT: neurofibrillary tangles; APP: amyloid precursor protein; PS1: presenilins; ncRNA: noncoding RNA; lncRNA: long noncoding RNA; miRNA: microRNA; ceRNA: competing endogenous RNA; GEO: Gene Expression Omnibus; GSE: gene expression omnibus series; GPL: gene expression omnibus platform; STRING: Search Tool for the Retrieval of Interacting Genes; MSigDB: Molecular Signature Database; WGCNA: weighted gene co-expression network analysis; TOM: topological overlap matrix; ME: Module eigengene; GS: gene significance; MM: module membership; PPI: protein-protein interaction; GO: gene ontology; BP: biological processes; MF: molecular functions; CC: cellular components; KEGG: Kyoto encyclopedia of genes and genomes; ROC: receiver operating characteristic; AUC: area under curve; GSEA: gene set enrichment analysis; NES: normalized enrichment score; FDR: false discovery rate; qRT-PCR: quantitative real-time PCR; RT: room temperature.