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Exploring the bi-directional relationship and shared genes between depression and stroke via NHANES and bioinformatic analysis

Background: Stroke and depression are the two most common causes of disability worldwide. Growing evidence suggests a bi-directional relationship between stroke and depression, whereas the molecular mechanisms underlying stroke and depression are not well understood. The objectives of this study wer...

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Autores principales: Yang, Zhanghuan, He, Maokun, Zhang, Qian, Li, Shifu, Chen, Hua, Liao, Di
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10102600/
https://www.ncbi.nlm.nih.gov/pubmed/37065487
http://dx.doi.org/10.3389/fgene.2023.1004457
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author Yang, Zhanghuan
He, Maokun
Zhang, Qian
Li, Shifu
Chen, Hua
Liao, Di
author_facet Yang, Zhanghuan
He, Maokun
Zhang, Qian
Li, Shifu
Chen, Hua
Liao, Di
author_sort Yang, Zhanghuan
collection PubMed
description Background: Stroke and depression are the two most common causes of disability worldwide. Growing evidence suggests a bi-directional relationship between stroke and depression, whereas the molecular mechanisms underlying stroke and depression are not well understood. The objectives of this study were to identify hub genes and biological pathways related to the pathogenesis of ischemic stroke (IS) and major depressive disorder (MDD) and to evaluate the infiltration of immune cells in both disorders. Methods: Participants from the United States National Health and Nutritional Examination Survey (NHANES) 2005–2018 were included to evaluate the association between stroke and MDD. Two differentially expressed genes (DEGs) sets extracted from GSE98793 and GSE16561 datasets were intersected to generate common DEGs, which were further screened out in cytoHubba to identify hub genes. GO, KEGG, Metascape, GeneMANIA, NetworkAnalyst, and DGIdb were used for functional enrichment, pathway analysis, regulatory network analysis, and candidate drugs analysis. ssGSEA algorithm was used to analyze the immune infiltration. Results: Among the 29706 participants from NHANES 2005–2018, stroke was significantly associated with MDD (OR = 2.79,95% CI:2.26–3.43, p < 0.0001). A total of 41 common upregulated genes and eight common downregulated genes were finally identified between IS and MDD. Enrichment analysis revealed that the shared genes were mainly involved in immune response and immune-related pathways. A protein-protein interaction (PPI) was constructed, from which ten (CD163, AEG1, IRAK3, S100A12, HP, PGLYRP1, CEACAM8, MPO, LCN2, and DEFA4) were screened. In addition, gene-miRNAs, transcription factor-gene interactions, and protein-drug interactions coregulatory networks with hub genes were also identified. Finally, we observed that the innate immunity was activated while acquired immunity was suppressed in both disorders. Conclusion: We successfully identified the ten hub shared genes linking the IS and MDD and constructed the regulatory networks for them that could serve as novel targeted therapy for the comorbidities.
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spelling pubmed-101026002023-04-15 Exploring the bi-directional relationship and shared genes between depression and stroke via NHANES and bioinformatic analysis Yang, Zhanghuan He, Maokun Zhang, Qian Li, Shifu Chen, Hua Liao, Di Front Genet Genetics Background: Stroke and depression are the two most common causes of disability worldwide. Growing evidence suggests a bi-directional relationship between stroke and depression, whereas the molecular mechanisms underlying stroke and depression are not well understood. The objectives of this study were to identify hub genes and biological pathways related to the pathogenesis of ischemic stroke (IS) and major depressive disorder (MDD) and to evaluate the infiltration of immune cells in both disorders. Methods: Participants from the United States National Health and Nutritional Examination Survey (NHANES) 2005–2018 were included to evaluate the association between stroke and MDD. Two differentially expressed genes (DEGs) sets extracted from GSE98793 and GSE16561 datasets were intersected to generate common DEGs, which were further screened out in cytoHubba to identify hub genes. GO, KEGG, Metascape, GeneMANIA, NetworkAnalyst, and DGIdb were used for functional enrichment, pathway analysis, regulatory network analysis, and candidate drugs analysis. ssGSEA algorithm was used to analyze the immune infiltration. Results: Among the 29706 participants from NHANES 2005–2018, stroke was significantly associated with MDD (OR = 2.79,95% CI:2.26–3.43, p < 0.0001). A total of 41 common upregulated genes and eight common downregulated genes were finally identified between IS and MDD. Enrichment analysis revealed that the shared genes were mainly involved in immune response and immune-related pathways. A protein-protein interaction (PPI) was constructed, from which ten (CD163, AEG1, IRAK3, S100A12, HP, PGLYRP1, CEACAM8, MPO, LCN2, and DEFA4) were screened. In addition, gene-miRNAs, transcription factor-gene interactions, and protein-drug interactions coregulatory networks with hub genes were also identified. Finally, we observed that the innate immunity was activated while acquired immunity was suppressed in both disorders. Conclusion: We successfully identified the ten hub shared genes linking the IS and MDD and constructed the regulatory networks for them that could serve as novel targeted therapy for the comorbidities. Frontiers Media S.A. 2023-03-31 /pmc/articles/PMC10102600/ /pubmed/37065487 http://dx.doi.org/10.3389/fgene.2023.1004457 Text en Copyright © 2023 Yang, He, Zhang, Li, Chen and Liao. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Genetics
Yang, Zhanghuan
He, Maokun
Zhang, Qian
Li, Shifu
Chen, Hua
Liao, Di
Exploring the bi-directional relationship and shared genes between depression and stroke via NHANES and bioinformatic analysis
title Exploring the bi-directional relationship and shared genes between depression and stroke via NHANES and bioinformatic analysis
title_full Exploring the bi-directional relationship and shared genes between depression and stroke via NHANES and bioinformatic analysis
title_fullStr Exploring the bi-directional relationship and shared genes between depression and stroke via NHANES and bioinformatic analysis
title_full_unstemmed Exploring the bi-directional relationship and shared genes between depression and stroke via NHANES and bioinformatic analysis
title_short Exploring the bi-directional relationship and shared genes between depression and stroke via NHANES and bioinformatic analysis
title_sort exploring the bi-directional relationship and shared genes between depression and stroke via nhanes and bioinformatic analysis
topic Genetics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10102600/
https://www.ncbi.nlm.nih.gov/pubmed/37065487
http://dx.doi.org/10.3389/fgene.2023.1004457
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