Identification of potential blood biomarkers for early diagnosis of Alzheimer’s disease through RNA sequencing analysis

Abstract

Background: With demographic shifts toward older populations, the number of people with dementia is steadily increasing. Alzheimer's disease (AD) is the most common cause of dementia, and no curative treatment is available. The current best strategy is to delay disease progression and to practice early intervention to reduce the number of patients that ultimately develop AD. Therefore, promising novel biomarkers for early diagnosis are urgently required. Methods: To identify blood-based biomarkers for early diagnosis of AD, we performed RNA sequencing (RNA-seq) analysis of 610 blood samples, representing 271 patients with AD, 91 cognitively normal (CN) adults, and 248 subjects with mild cognitive impairment (MCI). We first estimated cell-type proportions among AD, MCI, and CN samples from the bulk RNA-seq data using CIBERSORT and then examined the differentially expressed genes (DEGs) between AD and CN samples. To gain further insight into the biological functions of the DEGs, we performed gene set enrichment analysis (GSEA) and network-based meta-analysis. Results: In the cell-type distribution analysis, we found a significant association between the proportion of neutrophils and AD prognosis at a false discovery rate (FDR) < 0.05. Furthermore, a similar trend emerged in the results of routine blood tests from a large number of samples (n = 3,099: AD, 1,605; MCI, 994; CN, 500). In addition, GSEA and network-based meta-analysis based on DEGs between AD and CN samples revealed functional modules and important hub genes associated with the pathogenesis of AD. The risk prediction model constructed by using the proportion of neutrophils and the most important hub genes (EEF2 and RPL7) achieved a high AUC of 0.878 in a validation cohort; when further applied to a prospective cohort, the model achieved a high accuracy of 0.727. Conclusions: Our model was demonstrated to be effective in prospective AD risk prediction. These findings indicate the discovery of potential biomarkers for early diagnosis of AD, and their further improvement may lead to future practical clinical use.

Lower DNA methylation levels in CpG island shores of CR1, CLU, and PICALM in the blood of Japanese Alzheimer’s disease patients

“…The three SNPs associated with the risk of AD on CR1 , CLU , and PICALM chromosome loci (rs3818361, rs11136000 and rs3851179) were genotyped in 148 NCGG samples, representing 80 patients with AD and 68 cognitively normal (CN) adults. We further performed RNA-sequencing analysis of 32 blood samples, composed of 5 ADs and 27 CNs [ 28 ]. The gene expression data were normalized using the trimmed mean of M values (TMM) [ 29 ].…”

Shared Blood Transcriptomic Signatures between Alzheimer’s Disease and Diabetes Mellitus

“…In this study, we identified the shared transcriptomic signatures between AD and DM by integrating co-expression, DE, and DC analyses. Numerous studies used the DE method between two statuses (disease and control) to identify genes related with a specific disease [ 7 , 8 , 9 , 12 , 13 , 14 ]. However, the DE-based methods cannot reveal two genes that are simultaneously upregulated or downregulated.…”

“…This analysis identified the immune and inflammation-, energy-, WNT-, endoplasmic reticulum-, and cancer-related pathways as the underlying mechanisms associated with the AD status [ 12 ]. Moreover, RNA-sequencing data of blood samples collected from AD Japanese subjects stored at the National Center for Geriatrics and Gerontology (NCGG) Biobank also revealed several other pathways (i.e., signal-recognition particle (SRP)-dependent cotranslational protein targeting to membrane, translational initiation, and ribosome) associated with the AD status [ 13 ].…”

Gene Expression Signature of Traumatic Brain Injury

“…The differential expression of RPL27 and RPS15A was validated in TBI mice (Harper et al, 2020 ). Recent studies have shown that RPL7 and RPL23A are differentially expressed in senile dementia and may be potential biomarkers (Shigemizu et al, 2020 ). RPS4X interacts with exogenous lactate dehydrogenase A (LDHA) in the central nervous system and is critical for the proliferation of vascular endothelial cells (Lin et al, 2018 ).…”