Exploring the common pathogenesis of Alzheimer’s disease and type 2 diabetes mellitus via microarray data analysis
BackgroundAlzheimer’s Disease (AD) and Type 2 Diabetes Mellitus (DM) have an increased incidence in modern society. Although more and more evidence has supported that DM is prone to AD, the interrelational mechanisms remain fully elucidated.PurposeThe primary purpose of this study is to explore the...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-02-01
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| Series: | Frontiers in Aging Neuroscience |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fnagi.2023.1071391/full |
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| author | Xian-wen Ye Xian-wen Ye Xian-wen Ye Meng-nan Liu Meng-nan Liu Xuan Wang Shui-qing Cheng Chun-shuai Li Yu-ying Bai Lin-lin Yang Xu-xing Wang Jia Wen Wen-juan Xu Shu-yan Zhang Xin-fang Xu Xin-fang Xu Xin-fang Xu Xiang-ri Li Xiang-ri Li Xiang-ri Li |
| author_facet | Xian-wen Ye Xian-wen Ye Xian-wen Ye Meng-nan Liu Meng-nan Liu Xuan Wang Shui-qing Cheng Chun-shuai Li Yu-ying Bai Lin-lin Yang Xu-xing Wang Jia Wen Wen-juan Xu Shu-yan Zhang Xin-fang Xu Xin-fang Xu Xin-fang Xu Xiang-ri Li Xiang-ri Li Xiang-ri Li |
| author_sort | Xian-wen Ye |
| collection | DOAJ |
| description | BackgroundAlzheimer’s Disease (AD) and Type 2 Diabetes Mellitus (DM) have an increased incidence in modern society. Although more and more evidence has supported that DM is prone to AD, the interrelational mechanisms remain fully elucidated.PurposeThe primary purpose of this study is to explore the shared pathophysiological mechanisms of AD and DM.MethodsDownload the expression matrix of AD and DM from the Gene Expression Omnibus (GEO) database with sequence numbers GSE97760 and GSE95849, respectively. The common differentially expressed genes (DEGs) were identified by limma package analysis. Then we analyzed the six kinds of module analysis: gene functional annotation, protein–protein interaction (PPI) network, potential drug screening, immune cell infiltration, hub genes identification and validation, and prediction of transcription factors (TFs).ResultsThe subsequent analyses included 339 common DEGs, and the importance of immunity, hormone, cytokines, neurotransmitters, and insulin in these diseases was underscored by functional analysis. In addition, serotonergic synapse, ovarian steroidogenesis, estrogen signaling pathway, and regulation of lipolysis are closely related to both. DEGs were input into the CMap database to screen small molecule compounds with the potential to reverse AD and DM pathological functions. L-690488, exemestane, and BMS-345541 ranked top three among the screened small molecule compounds. Finally, 10 essential hub genes were identified using cytoHubba, including PTGS2, RAB10, LRRK2, SOS1, EEA1, NF1, RAB14, ADCY5, RAPGEF3, and PRKACG. For the characteristic Aβ and Tau pathology of AD, RAPGEF3 was associated significantly positively with AD and NF1 significantly negatively with AD. In addition, we also found ADCY5 and NF1 significant correlations with DM phenotypes. Other datasets verified that NF1, RAB14, ADCY5, and RAPGEF3 could be used as key markers of DM complicated with AD. Meanwhile, the immune cell infiltration score reflects the different cellular immune microenvironments of the two diseases.ConclusionThe common pathogenesis of AD and DM was revealed in our research. These common pathways and hub genes directions for further exploration of the pathogenesis or treatment of these two diseases. |
| first_indexed | 2024-04-10T07:06:58Z |
| format | Article |
| id | doaj.art-2c895e551ebf487185b6ae30d04444c3 |
| institution | Directory Open Access Journal |
| issn | 1663-4365 |
| language | English |
| last_indexed | 2024-04-10T07:06:58Z |
| publishDate | 2023-02-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Aging Neuroscience |
| spelling | doaj.art-2c895e551ebf487185b6ae30d04444c32023-02-27T05:48:01ZengFrontiers Media S.A.Frontiers in Aging Neuroscience1663-43652023-02-011510.3389/fnagi.2023.10713911071391Exploring the common pathogenesis of Alzheimer’s disease and type 2 diabetes mellitus via microarray data analysisXian-wen Ye0Xian-wen Ye1Xian-wen Ye2Meng-nan Liu3Meng-nan Liu4Xuan Wang5Shui-qing Cheng6Chun-shuai Li7Yu-ying Bai8Lin-lin Yang9Xu-xing Wang10Jia Wen11Wen-juan Xu12Shu-yan Zhang13Xin-fang Xu14Xin-fang Xu15Xin-fang Xu16Xiang-ri Li17Xiang-ri Li18Xiang-ri Li19Centre of TCM Processing Research, Beijing University of Chinese Medicine, Beijing, ChinaBeijing Key Laboratory for Quality Evaluation of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing, ChinaSchool of Traditional Chinese Medicine, Beijing University of Chinese Medicine, Beijing, ChinaCentre of TCM Processing Research, Beijing University of Chinese Medicine, Beijing, ChinaSchool of Traditional Chinese Medicine, Beijing University of Chinese Medicine, Beijing, ChinaSchool of Traditional Chinese Medicine, Beijing University of Chinese Medicine, Beijing, ChinaSchool of Traditional Chinese Medicine, Beijing University of Chinese Medicine, Beijing, ChinaSchool of Traditional Chinese Medicine, Beijing University of Chinese Medicine, Beijing, ChinaSchool of Traditional Chinese Medicine, Beijing University of Chinese Medicine, Beijing, ChinaSchool of Traditional Chinese Medicine, Beijing University of Chinese Medicine, Beijing, ChinaSchool of Traditional Chinese Medicine, Beijing University of Chinese Medicine, Beijing, ChinaSchool of Traditional Chinese Medicine, Beijing University of Chinese Medicine, Beijing, ChinaSchool of Traditional Chinese Medicine, Beijing University of Chinese Medicine, Beijing, ChinaSchool of Traditional Chinese Medicine, Beijing University of Chinese Medicine, Beijing, ChinaCentre of TCM Processing Research, Beijing University of Chinese Medicine, Beijing, ChinaBeijing Key Laboratory for Quality Evaluation of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing, ChinaSchool of Traditional Chinese Medicine, Beijing University of Chinese Medicine, Beijing, ChinaCentre of TCM Processing Research, Beijing University of Chinese Medicine, Beijing, ChinaBeijing Key Laboratory for Quality Evaluation of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing, ChinaSchool of Traditional Chinese Medicine, Beijing University of Chinese Medicine, Beijing, ChinaBackgroundAlzheimer’s Disease (AD) and Type 2 Diabetes Mellitus (DM) have an increased incidence in modern society. Although more and more evidence has supported that DM is prone to AD, the interrelational mechanisms remain fully elucidated.PurposeThe primary purpose of this study is to explore the shared pathophysiological mechanisms of AD and DM.MethodsDownload the expression matrix of AD and DM from the Gene Expression Omnibus (GEO) database with sequence numbers GSE97760 and GSE95849, respectively. The common differentially expressed genes (DEGs) were identified by limma package analysis. Then we analyzed the six kinds of module analysis: gene functional annotation, protein–protein interaction (PPI) network, potential drug screening, immune cell infiltration, hub genes identification and validation, and prediction of transcription factors (TFs).ResultsThe subsequent analyses included 339 common DEGs, and the importance of immunity, hormone, cytokines, neurotransmitters, and insulin in these diseases was underscored by functional analysis. In addition, serotonergic synapse, ovarian steroidogenesis, estrogen signaling pathway, and regulation of lipolysis are closely related to both. DEGs were input into the CMap database to screen small molecule compounds with the potential to reverse AD and DM pathological functions. L-690488, exemestane, and BMS-345541 ranked top three among the screened small molecule compounds. Finally, 10 essential hub genes were identified using cytoHubba, including PTGS2, RAB10, LRRK2, SOS1, EEA1, NF1, RAB14, ADCY5, RAPGEF3, and PRKACG. For the characteristic Aβ and Tau pathology of AD, RAPGEF3 was associated significantly positively with AD and NF1 significantly negatively with AD. In addition, we also found ADCY5 and NF1 significant correlations with DM phenotypes. Other datasets verified that NF1, RAB14, ADCY5, and RAPGEF3 could be used as key markers of DM complicated with AD. Meanwhile, the immune cell infiltration score reflects the different cellular immune microenvironments of the two diseases.ConclusionThe common pathogenesis of AD and DM was revealed in our research. These common pathways and hub genes directions for further exploration of the pathogenesis or treatment of these two diseases.https://www.frontiersin.org/articles/10.3389/fnagi.2023.1071391/fullnetwork pharmacologyAlzheimer’s diseasetype 2 diabetes mellitusbioinformatics analysispathophysiological mechanisms |
| spellingShingle | Xian-wen Ye Xian-wen Ye Xian-wen Ye Meng-nan Liu Meng-nan Liu Xuan Wang Shui-qing Cheng Chun-shuai Li Yu-ying Bai Lin-lin Yang Xu-xing Wang Jia Wen Wen-juan Xu Shu-yan Zhang Xin-fang Xu Xin-fang Xu Xin-fang Xu Xiang-ri Li Xiang-ri Li Xiang-ri Li Exploring the common pathogenesis of Alzheimer’s disease and type 2 diabetes mellitus via microarray data analysis Frontiers in Aging Neuroscience network pharmacology Alzheimer’s disease type 2 diabetes mellitus bioinformatics analysis pathophysiological mechanisms |
| title | Exploring the common pathogenesis of Alzheimer’s disease and type 2 diabetes mellitus via microarray data analysis |
| title_full | Exploring the common pathogenesis of Alzheimer’s disease and type 2 diabetes mellitus via microarray data analysis |
| title_fullStr | Exploring the common pathogenesis of Alzheimer’s disease and type 2 diabetes mellitus via microarray data analysis |
| title_full_unstemmed | Exploring the common pathogenesis of Alzheimer’s disease and type 2 diabetes mellitus via microarray data analysis |
| title_short | Exploring the common pathogenesis of Alzheimer’s disease and type 2 diabetes mellitus via microarray data analysis |
| title_sort | exploring the common pathogenesis of alzheimer s disease and type 2 diabetes mellitus via microarray data analysis |
| topic | network pharmacology Alzheimer’s disease type 2 diabetes mellitus bioinformatics analysis pathophysiological mechanisms |
| url | https://www.frontiersin.org/articles/10.3389/fnagi.2023.1071391/full |
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