Bioinformatics analysis to reveal the key genes related to obstructive sleep apnea

Gu, Xiandong; Yang, Wei; Luo, Xuming; Wang, Xiongbiao; Tang, Jihong; Cai, ZhuYing

doi:10.1007/s11325-018-1694-7

Cited by 4 publications

(4 citation statements)

References 34 publications

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“…The hub genes were different between using biased methods and unbiased methods, because of the nature of the two approaches ( Liu et al, 2011 ). Two studies were obtained the same result that the olfactory transduction pathway plays an important role in OSA using visceral adipose tissues ( Gu et al, 2019 ) and subcutaneous adipose tissues ( Cao et al, 2021 ) respectively. Yet, the different biomarkers and enriched pathways were using the same microarray data from visceral adipose tissues (GSE38792) ( Chen et al, 2015 ; Gu et al, 2019 ), for the small sample size increased the false-positive of the results.…”

Section: Introductionmentioning

confidence: 83%

“…Two studies were obtained the same result that the olfactory transduction pathway plays an important role in OSA using visceral adipose tissues ( Gu et al, 2019 ) and subcutaneous adipose tissues ( Cao et al, 2021 ) respectively. Yet, the different biomarkers and enriched pathways were using the same microarray data from visceral adipose tissues (GSE38792) ( Chen et al, 2015 ; Gu et al, 2019 ), for the small sample size increased the false-positive of the results. Moreover, although a series of bioinformatics analyses has thoroughly investigated the potential biomarkers and functional pathways of OSA in adipose tissues, they remain to use cross analysis in various datasets to explore the possible mechanisms.…”

Section: Introductionmentioning

confidence: 83%

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Identification and Validation of Prognostic Factors of Lipid Metabolism in Obstructive Sleep Apnea

Wang

Dai

2021

Front. Genet.

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Background: Obstructive sleep apnea (OSA) is considered to be an independent factor affecting lipid metabolism. This study explored the relationship between immune genes and lipid metabolism in OSA.Methods: Immune-related Differentially Expressed Genes (DEGs) were identified by analyzing microarray data sets from the Gene Expression Omnibus (GEO) database. Subsequently, we conducted protein-protein interaction (PPI) network analysis and calculated their Gene Ontology (GO) semantic similarity. The GO, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, Disease Ontology (DO), gene set enrichment analysis (GSEA), and gene set variation analysis (GSVA) were employed for functional enrichment analyses and to determine the most significant functional terms. Combined with the results of boruta and random forest, we selected predictors to build a prognostic model, along with seeking out the potential TFs and target drugs for the predictive genes.Results: Immune-related DEGs included 64 genes upregulated and 98 genes downregulated. The enrichment analysis might closely associate with cell adhesion and T cell-mediated immunity pathways and there were many DEGs involved in lipid and atherosclerosis signaling pathways. The highest-ranking hub gene in PPI network have been reported lowly expressed in OSA. In line with the enrichment analysis, DO analysis reveal that respiratory diseases may be associated with OSA besides immune system disorders. Consistent with the result of the KEGG pathway, the analysis of GSVA revealed that the pro-inflammation pathways are associated with OSA. Monocytes and CD8 T cells were the predominant immune cells in adipose tissue. We built a prognostic model with the top six genes, and the prognostic genes were involved in the polarization of macrophage and differentiation of T lymphocyte subsets. In vivo experimental verification revealed that EPGN, LGR5, NCK1 and VIP were significantly down-regulated while PGRMC2 was significantly up-regulated in mouse model of OSA.Conclusions: Our study demonstrated strong associations between immune genes and the development of dyslipidemia in OSA. This work promoted the molecular mechanisms and potential targets for the regulation of lipid metabolism in OSA.

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Section: Introductionmentioning

confidence: 83%

Section: Introductionmentioning

confidence: 83%

Identification and Validation of Prognostic Factors of Lipid Metabolism in Obstructive Sleep Apnea

Wang

Dai

2021

Front. Genet.

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“…Hence, the results of the bioinformatic analysis are not always reliable, especially in small sample datasets, experimental validation is necessary to increase confidence. In addition, we did not further establish an immune-related diagnostic model for OSA when compared to previous studies (Li et al, 2017;Gu et al, 2019;Cao et al, 2021;Peng et al, 2021;Liu et al, 2022), because of the invasive procedures for harvesting VAT.…”

Section: Discussionmentioning

confidence: 88%

CD40LG and GZMB were correlated with adipose tissue macrophage infiltration and involved in obstructive sleep apnea related metabolic dysregulation: Evidence from bioinformatics analysis

Ming

Cai²,

Li³

et al. 2023

Front. Genet.

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Both obesity and obstructive sleep apnea (OSA) can lead to metabolic dysregulation and systemic inflammation. Similar to obesity, increasing evidence has revealed that immune infiltration in the visceral adipose tissue (VAT) is associated with obstructive sleep apnea-related morbidity. However, the pathological changes and potential molecular mechanisms in visceral adipose tissue of obstructive sleep apnea patients need to be further studied. Herein, by bioinformatics analysis and clinical validation methods, including the immune-related differentially expressed genes (IRDEGs) analysis, protein-protein interaction network (PPI), functional enrichment analysis, a devolution algorithm (CIBERSORT), spearman’s correlation analysis, polymerase chain reaction (PCR), Enzyme-linked immunosorbent assay (ELISA) and immunohistochemistry (IHC), we identified and validated 10 hub IRDEGs, the relative mRNA expression of four hub genes (CRP, CD40LG, CCL20, and GZMB), and the protein expression level of two hub genes (CD40LG and GZMB) were consistent with the bioinformatics analysis results. Immune infiltration results further revealed that obstructive sleep apnea patients contained a higher proportion of pro-inflammatory M1 macrophages and a lower proportion of M2 macrophages. Spearman’s correlation analysis showed that CD40LG was positively correlated with M1 macrophages and GZMB was negatively correlated with M2 macrophages. CD40LG and GZMB might play a vital role in the visceral adipose tissue homeostasis of obstructive sleep apnea patients. Their interaction with macrophages and involved pathways not only provides new insights for understanding molecular mechanisms but also be of great significance in discovering novel small molecules or other promising candidates as immunotherapies of OSA-associated metabolic complications.

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“…However, there is a lack of studies on OSA-related expression genes at different severity. Although Xiandong Gu et al [ 28 ] conducted functional enrichment analysis and protein-protein interaction (PPI) analysis based on DEGs of gene expression profile of visceral adipose tissue OSA patients and normal people, they also determined main functional subset in PPI network during OSA pathogenetic process. However, they only focused on genes related to OSA onset.…”

Section: Discussionmentioning

confidence: 99%

Identification of the Novel Gene Markers Based on the Gene Profile among Different Severity of Obstructive Sleep Apnea

Ren

Sui

et al. 2022

Computational and Mathematical Methods in Medicine

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Obstructive sleep apnea (OSA) is caused by repeated blockage of the upper respiratory airways during sleep. The traditional evaluation methods for OSA severity are yet limited. This study aimed to screen gene signatures to effectively evaluate OSA severity. Expression profiles of peripheral blood mononuclear cells in the different severities of OSA patients were accessed from Gene Expression Omnibus (GEO) database. A total of 446 differentially expressed genes (DEGs) were screened among the varying severities of OSA samples by analysis of variance (ANOVA) test. A total of 1,152 DEGs were screened between the pre- and post-treatment OSA samples by using t test. Overlap of the two groups of DEGs was selected (88 DEGs) for Metascape enrichment analysis. Afterwards, Mfuzz package was used to perform soft clustering analysis on these 88 genes, by which 6 clusters were obtained. It was observed that the gene expression condition of the cluster 3 was positively associated with OSA severity degree; also, the gene expression condition in cluster 4 was negatively correlated with OSA severity. A total of 10 gene markers related to OSA progression were selected from cluster 3 and cluster 4. Their expression levels and correlation were analyzed. The marker genes in cluster 3 and cluster 4 were examined, finding that most genes were significantly correlated with apnea hypopnea index (AHI). An accurate and objective assessment of the severity of OSA is of great significance for formulating follow-up treatment strategies for patients with OSA. In this paper, a set of marker genes that can detect the severity of OSA were screened by bioinformatics methods, which could be jointly used with the traditional OSA diagnostic index to achieve a more reliable OSA severity evaluation.

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Bioinformatics analysis to reveal the key genes related to obstructive sleep apnea

Abstract: IL10RB, MAPK9, MAPK10, and TXN might function in the pathogenesis of OSA.

Cited by 4 publications

References 34 publications

Identification and Validation of Prognostic Factors of Lipid Metabolism in Obstructive Sleep Apnea

Identification and Validation of Prognostic Factors of Lipid Metabolism in Obstructive Sleep Apnea

CD40LG and GZMB were correlated with adipose tissue macrophage infiltration and involved in obstructive sleep apnea related metabolic dysregulation: Evidence from bioinformatics analysis

Identification of the Novel Gene Markers Based on the Gene Profile among Different Severity of Obstructive Sleep Apnea

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