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 pra...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2020-07-01
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| Series: | Alzheimer’s Research & Therapy |
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| Online Access: | http://link.springer.com/article/10.1186/s13195-020-00654-x |
| _version_ | 1818274796365938688 |
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| author | Daichi Shigemizu Taiki Mori Shintaro Akiyama Sayuri Higaki Hiroshi Watanabe Takashi Sakurai Shumpei Niida Kouichi Ozaki |
| author_facet | Daichi Shigemizu Taiki Mori Shintaro Akiyama Sayuri Higaki Hiroshi Watanabe Takashi Sakurai Shumpei Niida Kouichi Ozaki |
| author_sort | Daichi Shigemizu |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-12T22:19:33Z |
| format | Article |
| id | doaj.art-ed8cfcfbc5c04a5097be69ed80347ec9 |
| institution | Directory Open Access Journal |
| issn | 1758-9193 |
| language | English |
| last_indexed | 2024-12-12T22:19:33Z |
| publishDate | 2020-07-01 |
| publisher | BMC |
| record_format | Article |
| series | Alzheimer’s Research & Therapy |
| spelling | doaj.art-ed8cfcfbc5c04a5097be69ed80347ec92022-12-22T00:09:58ZengBMCAlzheimer’s Research & Therapy1758-91932020-07-0112111210.1186/s13195-020-00654-xIdentification of potential blood biomarkers for early diagnosis of Alzheimer’s disease through RNA sequencing analysisDaichi Shigemizu0Taiki Mori1Shintaro Akiyama2Sayuri Higaki3Hiroshi Watanabe4Takashi Sakurai5Shumpei Niida6Kouichi Ozaki7Medical Genome Center, National Center for Geriatrics and GerontologyMedical Genome Center, National Center for Geriatrics and GerontologyMedical Genome Center, National Center for Geriatrics and GerontologyMedical Genome Center, National Center for Geriatrics and GerontologyMedical Genome Center, National Center for Geriatrics and GerontologyThe Center for Comprehensive Care and Research on Memory Disorders, National Center for Geriatrics and GerontologyMedical Genome Center, National Center for Geriatrics and GerontologyMedical Genome Center, National Center for Geriatrics and GerontologyAbstract 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.http://link.springer.com/article/10.1186/s13195-020-00654-xAlzheimer’s diseaseRNA sequencingBiomarkers for early diagnosis |
| spellingShingle | Daichi Shigemizu Taiki Mori Shintaro Akiyama Sayuri Higaki Hiroshi Watanabe Takashi Sakurai Shumpei Niida Kouichi Ozaki Identification of potential blood biomarkers for early diagnosis of Alzheimer’s disease through RNA sequencing analysis Alzheimer’s Research & Therapy Alzheimer’s disease RNA sequencing Biomarkers for early diagnosis |
| title | Identification of potential blood biomarkers for early diagnosis of Alzheimer’s disease through RNA sequencing analysis |
| title_full | Identification of potential blood biomarkers for early diagnosis of Alzheimer’s disease through RNA sequencing analysis |
| title_fullStr | Identification of potential blood biomarkers for early diagnosis of Alzheimer’s disease through RNA sequencing analysis |
| title_full_unstemmed | Identification of potential blood biomarkers for early diagnosis of Alzheimer’s disease through RNA sequencing analysis |
| title_short | Identification of potential blood biomarkers for early diagnosis of Alzheimer’s disease through RNA sequencing analysis |
| title_sort | identification of potential blood biomarkers for early diagnosis of alzheimer s disease through rna sequencing analysis |
| topic | Alzheimer’s disease RNA sequencing Biomarkers for early diagnosis |
| url | http://link.springer.com/article/10.1186/s13195-020-00654-x |
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