Construction of Severe Eosinophilic Asthma Related Competing Endogenous RNA Network by Weighted Gene Co-Expression Network Analysis

Wang, Haixia; Zhang, Zeyi; Ma, Yu; Jia, Yuanmin; Ma, Bin; Gu, Junlian; Chen, Ou; Yue, Shouwei

doi:10.3389/fphar.2022.852536

Cited by 3 publications

(2 citation statements)

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“…Bioinformatics ( 9 ) is used for gene expression profiling to further investigate the molecular mechanisms and signaling pathways of disease through differentially expressed gene (DEG) screening ( 10 ). Analysis of key genes ( 11 ) and signaling pathways ( 12 ) in the development of disease can help lead to better cures ( 13 ).…”

Section: Introductionmentioning

confidence: 99%

Combining bioinformatics, network pharmacology and artificial intelligence to predict the mechanism of celastrol in the treatment of type 2 diabetes

Zhang

2022

Front. Endocrinol.

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BackgroundType 2 diabetes (T2D) is a common chronic disease with many serious complications. Celastrol can prevent and treat type 2 diabetes by reversing insulin resistance in a number of ways. However, the specific mechanisms by which celastrol prevents and treats T2D are not well understood. The aim of this study was to explore the key gene targets and potential signaling pathway mechanisms of celastrol for the treatment of T2D.MethodsGSE184050 was downloaded from the Gene Expression Omnibus online database. Blood samples from patients and healthy individuals with T2D were analyzed to identify differentially expressed genes (DEGs), and a protein−protein interaction network (PPI) was constructed. Key gene analysis of DEGs was performed using the MCODE plugin in Cystoscope as well as the Hubba plugin, and intersections were taken to obtain hub genes, which were displayed using a Venn diagram. Enrichment analysis was then performed via the ClueGo plugin in Cytoscape and validated using Gene Set Enrichment Analysis. The therapeutic targets of celastrol were then analyzed by pharmacophore network pharmacology, intersected to identify the therapeutic targets of celastrol, enriched for all targets, and intersected to obtain the signaling pathways for celastrol treatment. The protein structures of the therapeutic targets were predicted using the artificial intelligence AlphaFold2. Finally, molecular docking was used to verify whether celastrol could be successfully docked to the predicted targets.Results618 DEGs were obtained, and 9 hub genes for T2D were identified by the MCODE and Hubba plug-ins, including ADAMTS15, ADAMTS7, ADAMTSL1, SEMA5B, ADAMTS8, THBS2, HBB, HBD and HBG2. The DEG-enriched signaling pathways mainly included the ferroptosis and TGF-beta signaling pathways. A total of 228 target genes were annotated by pharmacophore target analysis, and the therapeutic targets were identified, including S100A11, RBP3, HBB, BMP7 and IQUB, and 9 therapeutic signaling pathways were obtained by an intersectional set. The protein structures of the therapeutic targets were successfully predicted by AlphaFold2, and docking was validated using molecular docking.ConclusionCelastrol may prevent and treat T2D through key target genes, such as HBB, as well as signaling pathways, such as the TGF-beta signaling pathway and type II diabetes mellitus.

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Section: Introductionmentioning

confidence: 99%

Combining bioinformatics, network pharmacology and artificial intelligence to predict the mechanism of celastrol in the treatment of type 2 diabetes

Zhang

2022

Front. Endocrinol.

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“…Since immune disorders play a key role in both SALI and NSCLC, we wondered whether ICIs for NSCLC could be used in SALI prevention and treatment. Although their shared signatures based on gene regulation mechanisms are still unknown, the rise of sequencing technology and bioinformatics in recent years has made it possible to explore the link between the two diseases at the genetic level [ 7 , 8 ]. The Gene Expression Omnibus (GEO) database was employed to obtain the RNA sequence and patient information regarding NSCLC and sepsis.…”

Section: Introductionmentioning

confidence: 99%

Inhibition of EZH2 Ameliorates Sepsis Acute Lung Injury (SALI) and Non-Small-Cell Lung Cancer (NSCLC) Proliferation through the PD-L1 Pathway

Wang

Guo

Wang

et al. 2022

Cells

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(1) Background: Both sepsis acute lung injury (SALI) and non-small-cell lung cancer (NSCLC) are life-threatening diseases caused by immune response disorders and inflammation, but the underlining linking mechanisms are still not clear. This study aimed to detect the shared gene signature and potential molecular process between SALI and NSCLC. (2) Methods: RNA sequences and patient information on sepsis and NSCLC were acquired from the Gene Expression Omnibus (GEO) database. Weighted gene co-expression network analysis (WGCNA) was used to build a co-expression network associated with sepsis and NSCLC. Protein–protein interaction (PPI) analysis of shared genes was intuitively performed by the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database. The involvement of EZH2 in the tumor immune microenvironment (TIME) and sepsis immune microenvironment (IME) was assessed by R software. Western blot, flow cytometry, and other in vitro assays were performed to further confirm the function and mechanism of EZH2 in NSCLC and SALI. (3) Results: WGCNA recognized three major modules for sepsis and two major modules for NSCLC, and there were seven shared genes identified for the two diseases. Additionally, the hub gene EZH2 was screened out. It was shown that EZH2 was closely related to the IME in the two diseases. In the validation assay, our data showed that EZH2 was expressed at a higher level in peripheral blood mononuclear cells (PBMCs) of septic patients than those of healthy donors (HDs), and EZH2 was also expressed at a higher level in lipopolysaccharide (LPS)-induced PBMCs and non-small cell lung cancer (A549) cells. EZH2 inhibitor (GSK343) downregulated the proliferation ability of A549 cells in a concentration-dependent manner, parallel with the decreased expression level of PD-L1. Similarly, GSK343 inhibited PD-L1 protein expression and downregulated the level of proinflammatory factors in LPS-induced PBMCs. In the co-culture system of PBMCs and human type II alveolar epithelial cells (ATIIs), the addition of GSK343 to PBMCs significantly downregulated the apoptosis of LPS-induced ATIIs. (4) Conclusions: This study illustrated that EZH2 inhibition could ameliorate A549 cell proliferation and LPS-induced ATII apoptosis in parallel with downregulation of PD-L1 protein expression, which provided new insights into molecular signaling networks involved in the pathogenetics of SALI and NSCLC.

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Ferroptosis-related genes are involved in asthma and regulate the immune microenvironment

Wang

Jia²,

Gu³

et al. 2023

Front. Pharmacol.

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Background: Asthma was a chronic inflammatory illness driven by complicated genetic regulation and environmental exposure. The complex pathophysiology of asthma has not been fully understood. Ferroptosis was involved in inflammation and infection. However, the effect of ferroptosis on asthma was still unclear. The study was designed to identify ferroptosis-related genes in asthma, providing potential therapeutic targets.Methods: We conducted a comprehensive analysis combined with WGCNA, PPI, GO, KEGG, and CIBERSORT methods to identify ferroptosis-related genes that were associated with asthma and regulated the immune microenvironment in GSE147878 from the GEO. The results of this study were validated in GSE143303 and GSE27066, and the hub genes related to ferroptosis were further verified by immunofluorescence and RT-qPCR in the OVA asthma model.Results: 60 asthmatics and 13 healthy controls were extracted for WGCNA. We found that genes in the black module (r = −0.47, p < 0.05) and magenta module (r = 0.51, p < 0.05) were associated with asthma. CAMKK2 and CISD1 were discovered to be ferroptosis-related hub genes in the black and magenta module, separately. We found that CAMKK2 and CISD1 were mainly involved in the CAMKK-AMPK signaling cascade, the adipocytokine signaling pathway, the metal cluster binding, iron-sulfur cluster binding, and 2 iron, 2 sulfur cluster binding in the enrichment analysis, which was strongly correlated with the development of ferroptosis. We found more infiltration of M2 macrophages and less Tregs infiltration in the asthma group compared to healthy controls. In addition, the expression levels of CISD1 and Tregs were negatively correlated. Through validation, we found that CAMKK2 and CISD1 expression were upregulated in the asthma group compared to the control group and would inhibit the occurrence of ferroptosis.Conclusion: CAMKK2 and CISD1 might inhibit ferroptosis and specifically regulate asthma. Moreover, CISD1 might be tied to the immunological microenvironment. Our results could be useful to provide potential immunotherapy targets and prognostic markers for asthma.

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Construction of Severe Eosinophilic Asthma Related Competing Endogenous RNA Network by Weighted Gene Co-Expression Network Analysis

Cited by 3 publications

References 40 publications

Combining bioinformatics, network pharmacology and artificial intelligence to predict the mechanism of celastrol in the treatment of type 2 diabetes

Combining bioinformatics, network pharmacology and artificial intelligence to predict the mechanism of celastrol in the treatment of type 2 diabetes

Inhibition of EZH2 Ameliorates Sepsis Acute Lung Injury (SALI) and Non-Small-Cell Lung Cancer (NSCLC) Proliferation through the PD-L1 Pathway

Ferroptosis-related genes are involved in asthma and regulate the immune microenvironment

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