Experimental verification and identifying biomarkers related to insomnia
IntroductionInsomnia is the most common form of sleep deprivation (SD) observed in clinics. Although there are differences between insomnia and SD, they have similar symptoms and the same animal model. Currently, there is a lack of microarray data on insomnia. Therefore, for now, we are going to app...
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| Format: | Article |
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Frontiers Media S.A.
2023-11-01
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| Series: | Frontiers in Neurology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fneur.2023.1189076/full |
| _version_ | 1797449303387537408 |
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| author | Qianfei Wang Qianfei Wang Dong Liu Dong Liu Tianci Gao Tianci Gao Yulei Tao Yulei Tao Xin Li Xin Li Yuan Liu Yuan Liu Zhiliang Liu Jianqiang Mei Fenqiao Chen |
| author_facet | Qianfei Wang Qianfei Wang Dong Liu Dong Liu Tianci Gao Tianci Gao Yulei Tao Yulei Tao Xin Li Xin Li Yuan Liu Yuan Liu Zhiliang Liu Jianqiang Mei Fenqiao Chen |
| author_sort | Qianfei Wang |
| collection | DOAJ |
| description | IntroductionInsomnia is the most common form of sleep deprivation (SD) observed in clinics. Although there are differences between insomnia and SD, they have similar symptoms and the same animal model. Currently, there is a lack of microarray data on insomnia. Therefore, for now, we are going to apply the SD data to insomnia. Although many studies have explained the possible mechanisms associated with insomnia, no previous studies have considered the key genes associated with insomnia or the relationship between insomnia and immune cells. In this study, we analyzed the relationship between key genes and immune cells by identifying biomarkers for the diagnosis of insomnia. Next, we verified the efficacy of these biomarkers experimentally.MethodsFirst, we downloaded four microarrays (GSE11755, GSE12624, GSE28750, and GSE48080) from the Gene Expression Omnibus (GEO) database, which included data from 239 normal human blood samples and 365 blood specimens from patients with SD. Then, we analyzed two groups of differentially expressed genes (DEGs) and used Support Vector Machine Recursive Feature Elimination (SVM-RFE) analysis and the Least Absolute Shrinkage and Selection Operator (LASSO) regression model to investigate these key genes. Next, we used CIBERSORT to investigate the composition of 22 immune cell components of key genes in SD patients. Finally, the expression levels of key biomarkers in sleep-deprived patients were examined by quantitative real-time polymerase chain reaction (qRT-PCR).ResultsA total of 50 DEGs were identified: six genes were significantly upregulated, and 44 genes were significantly downregulated. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis showed that Salmonella infection, NOD-like receptor (NLR) signaling pathway, Kaposi sarcoma-associated herpesvirus infection, and Th17 cell differentiation were significant. Based on machine learning, we identified C2CD2L, SPINT2, APOL3, PKNOX1, and A2M as key genes for SD; these were confirmed by receiver operating characteristic (ROC) analysis. Immune cell infiltration analysis showed that C2CD2L, SPINT2, APOL3, PKNOX1, and A2M were related in different degrees to regulatory T cells (Tregs), follicular T helper cells, CD8 cells, and other immune cells. The qRT-PCR experiments confirmed that the expression levels of C2CD2L concurred with the results derived from machine learning, but PKNOX1 and APOL3 did not.DiscussionIn summary, we identified a key gene (C2CD2L) that may facilitate the development of biomarkers for insomnia. |
| first_indexed | 2024-03-09T14:23:50Z |
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| id | doaj.art-a7ff14c555a54c199bebd7a806491a1e |
| institution | Directory Open Access Journal |
| issn | 1664-2295 |
| language | English |
| last_indexed | 2024-03-09T14:23:50Z |
| publishDate | 2023-11-01 |
| publisher | Frontiers Media S.A. |
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| series | Frontiers in Neurology |
| spelling | doaj.art-a7ff14c555a54c199bebd7a806491a1e2023-11-28T08:07:38ZengFrontiers Media S.A.Frontiers in Neurology1664-22952023-11-011410.3389/fneur.2023.11890761189076Experimental verification and identifying biomarkers related to insomniaQianfei Wang0Qianfei Wang1Dong Liu2Dong Liu3Tianci Gao4Tianci Gao5Yulei Tao6Yulei Tao7Xin Li8Xin Li9Yuan Liu10Yuan Liu11Zhiliang Liu12Jianqiang Mei13Fenqiao Chen14The Graduate School, Hebei University of Chinese Medicine, Shijiazhuang, ChinaThe First Affiliated Hospital, Hebei University of Chinese Medicine, Shijiazhuang, ChinaThe Graduate School, Hebei University of Chinese Medicine, Shijiazhuang, ChinaThe First Affiliated Hospital, Hebei University of Chinese Medicine, Shijiazhuang, ChinaThe Graduate School, Hebei University of Chinese Medicine, Shijiazhuang, ChinaThe First Affiliated Hospital, Hebei University of Chinese Medicine, Shijiazhuang, ChinaThe Graduate School, Hebei University of Chinese Medicine, Shijiazhuang, ChinaThe First Affiliated Hospital, Hebei University of Chinese Medicine, Shijiazhuang, ChinaThe Graduate School, Hebei University of Chinese Medicine, Shijiazhuang, ChinaThe First Affiliated Hospital, Hebei University of Chinese Medicine, Shijiazhuang, ChinaThe Graduate School, Hebei University of Chinese Medicine, Shijiazhuang, ChinaThe First Affiliated Hospital, Hebei University of Chinese Medicine, Shijiazhuang, ChinaThe Emergency Department, Hebei Yiling Hospital, Shijiazhuang, ChinaThe First Affiliated Hospital, Hebei University of Chinese Medicine, Shijiazhuang, ChinaThe First Affiliated Hospital, Hebei University of Chinese Medicine, Shijiazhuang, ChinaIntroductionInsomnia is the most common form of sleep deprivation (SD) observed in clinics. Although there are differences between insomnia and SD, they have similar symptoms and the same animal model. Currently, there is a lack of microarray data on insomnia. Therefore, for now, we are going to apply the SD data to insomnia. Although many studies have explained the possible mechanisms associated with insomnia, no previous studies have considered the key genes associated with insomnia or the relationship between insomnia and immune cells. In this study, we analyzed the relationship between key genes and immune cells by identifying biomarkers for the diagnosis of insomnia. Next, we verified the efficacy of these biomarkers experimentally.MethodsFirst, we downloaded four microarrays (GSE11755, GSE12624, GSE28750, and GSE48080) from the Gene Expression Omnibus (GEO) database, which included data from 239 normal human blood samples and 365 blood specimens from patients with SD. Then, we analyzed two groups of differentially expressed genes (DEGs) and used Support Vector Machine Recursive Feature Elimination (SVM-RFE) analysis and the Least Absolute Shrinkage and Selection Operator (LASSO) regression model to investigate these key genes. Next, we used CIBERSORT to investigate the composition of 22 immune cell components of key genes in SD patients. Finally, the expression levels of key biomarkers in sleep-deprived patients were examined by quantitative real-time polymerase chain reaction (qRT-PCR).ResultsA total of 50 DEGs were identified: six genes were significantly upregulated, and 44 genes were significantly downregulated. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis showed that Salmonella infection, NOD-like receptor (NLR) signaling pathway, Kaposi sarcoma-associated herpesvirus infection, and Th17 cell differentiation were significant. Based on machine learning, we identified C2CD2L, SPINT2, APOL3, PKNOX1, and A2M as key genes for SD; these were confirmed by receiver operating characteristic (ROC) analysis. Immune cell infiltration analysis showed that C2CD2L, SPINT2, APOL3, PKNOX1, and A2M were related in different degrees to regulatory T cells (Tregs), follicular T helper cells, CD8 cells, and other immune cells. The qRT-PCR experiments confirmed that the expression levels of C2CD2L concurred with the results derived from machine learning, but PKNOX1 and APOL3 did not.DiscussionIn summary, we identified a key gene (C2CD2L) that may facilitate the development of biomarkers for insomnia.https://www.frontiersin.org/articles/10.3389/fneur.2023.1189076/fullinsomniasleep deprivationgenepathwaysRNAimmune infiltration |
| spellingShingle | Qianfei Wang Qianfei Wang Dong Liu Dong Liu Tianci Gao Tianci Gao Yulei Tao Yulei Tao Xin Li Xin Li Yuan Liu Yuan Liu Zhiliang Liu Jianqiang Mei Fenqiao Chen Experimental verification and identifying biomarkers related to insomnia Frontiers in Neurology insomnia sleep deprivation gene pathways RNA immune infiltration |
| title | Experimental verification and identifying biomarkers related to insomnia |
| title_full | Experimental verification and identifying biomarkers related to insomnia |
| title_fullStr | Experimental verification and identifying biomarkers related to insomnia |
| title_full_unstemmed | Experimental verification and identifying biomarkers related to insomnia |
| title_short | Experimental verification and identifying biomarkers related to insomnia |
| title_sort | experimental verification and identifying biomarkers related to insomnia |
| topic | insomnia sleep deprivation gene pathways RNA immune infiltration |
| url | https://www.frontiersin.org/articles/10.3389/fneur.2023.1189076/full |
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