B Cell–Extrinsic <i>Myd88</i> and <i>Fcer1g</i> Negatively Regulate Autoreactive and Normal B Cell Immune Responses

Sweet, Rebecca A.; Nickerson, Kevin M.; Cullen, Jaime L.; Wang, Yujuan; Shlomchik, Mark J.

doi:10.4049/jimmunol.1600861

Cited by 36 publications

(30 citation statements)

References 71 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Early studies found that FCER1G transduced activation signals from various immunoreceptors [10,29]. It was functionally linked to mediate neutrophil activation and was also involved in platelet activation.…”

Section: Discussionmentioning

confidence: 99%

“…Specifically, FCER1G is a constitutive component of the high-affinity immunoglobulin E (IgE) receptor and interleukin-3 receptor complex. It is mainly involved in mediating the allergic inflammatory signaling of mast cells, selectively mediating the production of interleukin 4 (IL4) by basophils, and initiating the transfer from T-cells to the effector T-helper 2 subset [10,11]. It also forms a functional signaling complex together with the pattern recognition receptors CLEC4D and CLEC4E in myeloid cells.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Enhanced expression of FCER1G predicts positive prognosis in multiple myeloma

Fu¹,

Cheng²,

Dong³

et al. 2020

J. Cancer

View full text Add to dashboard Cite

Background: Multiple myeloma (MM) is the second most common hematologic malignancy worldwide and does not have sufficient prognostic indicators. FCER1G (Fc fragment Of IgE receptor Ig) is located on chromosome 1q23.3 and is involved in the innate immunity. Early studies have shown that FCER1G participates in many immune-related pathways encompassing multiple cell types. Meanwhile, it is associated with many malignancies. However, the relationship between MM and FCER1G has not been studied. Methods: In this study, we integrated nine independent gene expression omnibus (GEO) datasets and analyzed the associations of FCER1G expression and myeloma progression, ISS stage, 1q21 amplification and survival in 2296 myeloma patients and 48 healthy donors. Results: The expression of FCER1G showed a decreasing trend with the advance of myeloma. As ISS stage and 1q21 amplification level increased, the expression of FCER1G decreased (P = 0.0012 and 0.0036, respectively). MM patients with high FCER1G expression consistently had longer EFS and OS across three large sample datasets (EFS: P = 0.0057, 0.0049, OS: P = 0.0014, 0.00065, 0.0019 and 0.0029, respectively). Meanwhile, univariate and multivariate analysis indicated that high FCER1G expression was an independent favorable prognostic factor for EFS and OS in MM patients (EFS: P = 0.006, 0.027, OS: P =0.002,0.025, respectively). Conclusions:The expression level of FCER1G negatively correlated with myeloma progression, and high FCER1G expression may be applied as a favorable biomarker in MM patients.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Enhanced expression of FCER1G predicts positive prognosis in multiple myeloma

Fu¹,

Cheng²,

Dong³

et al. 2020

J. Cancer

View full text Add to dashboard Cite

show abstract

“…FCER1G is a Protein Coding gene that interacts with other factors and participates in various nuclear pathways [28]. Speci cally, FCER1G is a constitutive component of the high-a nity immunoglobulin E receptor and interleukin-3 receptor complex and is mainly involved in mediating the allergic in ammatory signaling of mast cells, selectively mediating the production of interleukin 4 by basophils, and initiating the transfer from T cells to the effector T-helper 2 subset [29]. Additionally, FCER1G is associated with the progression of clear cell renal cell carcinoma and may improve prognosis by affecting immune-related pathways.…”

Section: Discussionmentioning

confidence: 99%

Bioinformatics Analysis Reveals Novel Hub Gene Pathways Associated With IgA Nephropathy

Jiang

et al. 2020

Preprint

View full text Add to dashboard Cite

Background:Immunoglobulin A nephropathy (IgAN) is the most common primary glomerulopathy worldwide. However, the molecular events underlying IgAN remain to be fully elucidated. This study aimed to identify novel biomarkers of IgAN through bioinformatics analysis and elucidate the possible molecular mechanism. Methods:Based on the microarray datasets GSE93798 and GSE37460 downloaded from the Gene Expression Omnibus database, the diﬀerentially expressed genes (DEGs) between IgAN samples and normal controls were identified. Using the DEGs, we further performed a series of functional enrichment analyses. Protein-protein interaction (PPI) networks of the DEGs were constructed using the STRING online search tool and were visualized using Cytoscape. Next, hub genes were identified and the most important module among the DEGs, Biological Networks Gene Ontology tool (BiNGO), was used to elucidate the molecular mechanism of IgAN.Results:In total, 148 DEGs were identified, comprising 53 upregulated genes and 95 downregulated genes. Gene Oncology (GO) analysis indicated that the DEGs for IgAN were mainly enriched in extracellular exosome, region and space, fibroblast growth factor stimulus, inflammatory response, and innate immunity. Module analysis showed that genes in the top 1 significant module of the PPI network were mainly associated with innate immune response, integrin-mediated signaling pathway and inflammatory response. The top 10 hub genes were constructed in the PPI network, which could well distinguish the IgAN and control group in monocyte and tissue samples. We finally identified the integrin subunit beta 2 (ITGB2) and Fc fragment of IgE receptor Ig (FCER1G) genes that may play important roles in the development of IgAN.Conclusions: We identified key genes along with the pathways that were most closely related to IgAN initiation and progression. Our results provide a more detailed molecular mechanism for the development of IgAN and novel candidate gene targets of IgAN.

show abstract

“…Through a review of documents about lupus and related genes [34][35][36][37][38][39][40][41][42][43][44][45][46][47][48], 15 core genes related to clinical manifestation were found to be associated in autoimmune disease (Table 1). FCER1G, CLEC7A, MARCO, CLEC7A, PSMB9, and PSMB8 showed apparent correlation with clinical manifestation.…”

Section: Discussionmentioning

confidence: 99%

Immune cell infiltration characteristics and related core genes in lupus nephritis: results from bioinformatic analysis

Cao

Tang

2019

Preprint

View full text Add to dashboard Cite

Background Lupus nephritis (LN) is a common complication of systemic lupus erythematosus that presents a high risk of end-stage renal disease. In the present study, we used CIBERSORT and gene set enrichment analysis (GSEA) of gene expression profiles to identify immune cell infiltration characteristics and related core genes in LN. Results Datasets from the Gene Expression Omnibus, GSE32591 and GSE113342, were downloaded for further analysis. The GSE32591 dataset, which included 32 LN glomerular biopsy tissues and 14 glomerular tissues from living donors, was analyzed by CIBERSORT. Different immune cell types in LN were analyzed by the Limma software. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis based on GSEA were performed by clusterProfiler software. Lists of core genes were derived from Spearman correlation between the most significant GO term and differentially expressed immune cell gene from CIBERSORT. GSE113342 was employed to validate the association between selected core genes and clinical manifestation. Five types of immune cells revealed important associations with LN, and monocytes emerged as having the most prominent differences. GO and KEGG analyses indicated that immune response pathways are significantly enriched in LN. The Spearman correlation indicated that 15 genes, including FCER1G, CLEC7A, MARCO, CLEC7A, PSMB9, and PSMB8 , were closely related to clinical features. Conclusions This study is the first to identify immune cell infiltration with microarray data of glomeruli in LN by using CIBERSORT analysis and provides novel evidence and clues for further research of the molecular mechanisms of LN.

show abstract

B Cell–Extrinsic Myd88 and Fcer1g Negatively Regulate Autoreactive and Normal B Cell Immune Responses

Cited by 36 publications

References 71 publications

Enhanced expression of FCER1G predicts positive prognosis in multiple myeloma

Enhanced expression of FCER1G predicts positive prognosis in multiple myeloma

Bioinformatics Analysis Reveals Novel Hub Gene Pathways Associated With IgA Nephropathy

Immune cell infiltration characteristics and related core genes in lupus nephritis: results from bioinformatic analysis

Contact Info

Product

Resources

About