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Sepsis, characterized by a widespread and dysregulated immune response to infection leading to organ dysfunction, presents significant challenges in diagnosis and treatment. In this study, we investigated 203 coagulation-related genes in sepsis patients to explore their roles in the disease. Through differential gene expression analysis, we identified 20 genes with altered expression patterns. Subsequent correlation analysis, visualized through circos plots and heatmaps, revealed significant relationships among these genes. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses indicated that these genes are involved in immune response activation, coagulation, and immune receptor activity. Disease Ontology (DO) enrichment analysis further linked these genes to autoimmune hemolytic anemia and tumor-related signaling pathways. Additionally, the CIBERSORT analysis highlighted differences in immune cell composition in sepsis patients, revealing an increase in neutrophils and monocytes and a decrease in inactive NK cells, CD8 T cells, and B cells. We employed machine learning techniques, including random forest and SVM, to construct a diagnostic model, identifying FCER1G and FYN as key biomarkers. These biomarkers were validated through their expression levels and ROC curve analysis in an independent validation cohort, demonstrating strong diagnostic potential. Single-cell analysis from the GSE167363 dataset further confirmed the distinct expression profiles of these genes across various cell types, with FCER1G predominantly expressed in monocytes, NK cells, and platelets, and FYN in CD4+ T cells and NK cells. Enrichment analysis via GSEA and ssGSEA revealed that these genes are involved in critical pathways, including intestinal immune networks, fatty acid synthesis, and antigen processing. In conclusion, our comprehensive analysis identifies FCER1G and FYN as promising biomarkers for sepsis, providing valuable insights into the molecular mechanisms of this complex condition. These findings offer new avenues for the development of targeted diagnostic and therapeutic strategies in sepsis management. [ABSTRACT FROM AUTHOR]

The spleen tyrosine kinase (SYK) and high affinity immunoglobulin epsilon receptor subunit gamma (FCER1G) interaction has a major role in the normal innate and adaptive immune responses, but dysregulation of this interaction is implicated in several human diseases, including autoimmune disorders, hematological malignancies, and Alzheimer's Disease. Development of small molecule chemical probes could aid in studying this pathway both in normal and aberrant contexts. Herein, we describe the miniaturization of a time-resolved fluorescence resonance energy transfer (TR-FRET) assay to measure the interaction between SYK and FCER1G in a 1536-well ultrahigh throughput screening (uHTS) format. The assay utilizes the His-SH2 domains of SYK, which are indirectly labeled with anti-His-terbium to serve as a TR-FRET donor and a FITC-conjugated phosphorylated ITAM domain peptide of FCER1G to serve as an acceptor. We have optimized the assay into a 384-well HTS format and further miniaturized the assay into a 1536-well uHTS format. Robust assay performance has been achieved with a Z’ factor > 0.8 and signal-to-background (S/B) ratio > 15. The utilization of this uHTS TR-FRET assay for compound screening has been validated by a pilot screening of 2,036 FDA-approved and bioactive compounds library. Several primary hits have been identified from the pilot uHTS. One compound, hematoxylin, was confirmed to disrupt the SYK/FECR1G interaction in an orthogonal protein–protein interaction assay. Thus, our optimized and miniaturized uHTS assay could be applied to future scaling up of a screening campaign to identify small molecule inhibitors targeting the SYK and FCER1G interaction.

Although recent studies have revealed the relationship between Fc Fragment of IgE Receptor Ig (FCER1G) and human tumours, there is still a lack of a more comprehensive pan‐cancer analysis of FCER1G as an immune‐related gene. In this study, we investigated the expression pattern and prognostic value of FCER1G based on multiple databases. Subsequently, we further explored the role of FCER1G in tumour proliferation and metastasis, as well as its genomic alterations and DNA methylation levels, we next assessed the association between FCER1G and the immune infiltrating cells of the tumour microenvironment in different cancers and verified it by immunohistochemical staining. The correlation between FCER1G and immune checkpoint genes expression and its predictive power in the immune checkpoint blockade treatment cohorts were used to evaluate the importance of FCER1G in immunotherapy. Enrichment analysis of FCER1G‐associated partners was also performed. In addition, we substantiated the expression of FCER1G in specific cell types of different tumours using single‐cell RNA sequencing data from different databases. Our research results showed that FCER1G is up‐regulated in most tumour. Positive associations were found between FCER1G expression and tumour prognosis, proliferation, and metastasis, we also found that FCER1G is closely related to the tumour microenvironment and tumour immunity. Moreover, FCER1G‐associated partners were enriched in pathways associated with neutrophils activation. Finally, we confirmed that FCER1G was mainly expressed in monocyte/macrophages of the tumour microenvironment. In conclusion, our findings provided a comprehensive understanding of FCER1G in oncogenesis and tumour immunology among various tumours and demonstrated its potential value in prognosis prediction and tumour immunotherapy. [ABSTRACT FROM AUTHOR]

Abstract Background Tumor-associated macrophages (TAMs) are closely related to unfavorable prognosis of patients with clear cell renal cell carcinoma (ccRCC). However, the important molecules in the interaction between ccRCC and TAMs are unclear. Methods TCGA-KIRC gene expression data of tumor tissues and normal tissues adjacent to tumor were compared to identify differentially expressed genes in ccRCC. TAMs related genes were discovered by analyzing the correlation between these differentially expressed genes and common macrophage biomarkers. Gene set enrichment analysis was performed to predict functions of TAMs related gene. The findings were further validated using RNA sequencing data obtained from the CheckMate 025 study and immunohistochemical analysis of samples from 350 patients with ccRCC. Kaplan–Meier survival curve, Cox regression analysis and Harrell’s concordance index analysis were used to determine the prognostic significance. Results In this study, we applied bioinformatic analysis to explore TAMs related differentially expressed genes in ccRCC and identified 5 genes strongly correlated with all selected macrophage biomarkers: STAC3, LGALS9, TREM2, FCER1G, and PILRA. Among them, FCER1G was abundantly expressed in tumor tissues and showed prognostic importance in patients with ccRCC who received treatment with Nivolumab; however, it did not exhibit prognostic value in those treated with Everolimus. We also discovered that high expression levels of FCER1G are related to T cell suppression. Moreover, combination of FCER1G and macrophage biomarker CD68 can improve the prognostic stratification of patients with ccRCC from TCGA-KIRC. Based on the immunohistochemical analysis of samples from patients with ccRCC, we further validated that FCER1G and CD68 are both highly expressed in tumor tissue and correlate with each other. Higher expression of CD68 or FCER1G in ccRCC tissue indicates shorter overall survival and progression-free survival; patients with high expression of both CD68 and FCER1G have the worst outcome. Combining CD68 and FCER1G facilitates the screening of patients with a worse prognosis from the same TNM stage group. Conclusions High expression of FCER1G in ccRCC is closely related to TAMs infiltration and suppression of T cell activation and proliferation. Combining the expression levels of FCER1G and macrophage biomarker CD68 may be a promising postoperative prognostic index for patients with ccRCC.

Sepsis, characterized by a widespread and dysregulated immune response to infection leading to organ dysfunction, presents significant challenges in diagnosis and treatment. In this study, we investigated 203 coagulation-related genes in sepsis patients to explore their roles in the disease. Through differential gene expression analysis, we identified 20 genes with altered expression patterns. Subsequent correlation analysis, visualized through circos plots and heatmaps, revealed significant relationships among these genes. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses indicated that these genes are involved in immune response activation, coagulation, and immune receptor activity. Disease Ontology (DO) enrichment analysis further linked these genes to autoimmune hemolytic anemia and tumor-related signaling pathways. Additionally, the CIBERSORT analysis highlighted differences in immune cell composition in sepsis patients, revealing an increase in neutrophils and monocytes and a decrease in inactive NK cells, CD8 T cells, and B cells. We employed machine learning techniques, including random forest and SVM, to construct a diagnostic model, identifying FCER1G and FYN as key biomarkers. These biomarkers were validated through their expression levels and ROC curve analysis in an independent validation cohort, demonstrating strong diagnostic potential. Single-cell analysis from the GSE167363 dataset further confirmed the distinct expression profiles of these genes across various cell types, with FCER1G predominantly expressed in monocytes, NK cells, and platelets, and FYN in CD4+ T cells and NK cells. Enrichment analysis via GSEA and ssGSEA revealed that these genes are involved in critical pathways, including intestinal immune networks, fatty acid synthesis, and antigen processing. In conclusion, our comprehensive analysis identifies FCER1G and FYN as promising biomarkers for sepsis, providing valuable insights into the molecular mechanisms of this complex condition. These findings offer new avenues for the development of targeted diagnostic and therapeutic strategies in sepsis management., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Ge, Deng, Zhou, Hu, Zhang and Zheng.)

The spleen tyrosine kinase (SYK) and high affinity immunoglobulin epsilon receptor subunit gamma (FCER1G) interaction has a major role in the normal innate and adaptive immune responses, but dysregulation of this interaction is implicated in several human diseases, including autoimmune disorders, hematological malignancies, and Alzheimer's Disease. Development of small molecule chemical probes could aid in studying this pathway both in normal and aberrant contexts. Herein, we describe the miniaturization of a time-resolved fluorescence resonance energy transfer (TR-FRET) assay to measure the interaction between SYK and FCER1G in a 1536-well ultrahigh throughput screening (uHTS) format. The assay utilizes the His-SH2 domains of SYK, which are indirectly labeled with anti-His-terbium to serve as a TR-FRET donor and a FITC-conjugated phosphorylated ITAM domain peptide of FCER1G to serve as an acceptor. We have optimized the assay into a 384-well HTS format and further miniaturized the assay into a 1536-well uHTS format. Robust assay performance has been achieved with a Z' factor > 0.8 and signal-to-background (S/B) ratio > 15. The utilization of this uHTS TR-FRET assay for compound screening has been validated by a pilot screening of 2,036 FDA-approved and bioactive compounds library. Several primary hits have been identified from the pilot uHTS. One compound, hematoxylin, was confirmed to disrupt the SYK/FECR1G interaction in an orthogonal protein-protein interaction assay. Thus, our optimized and miniaturized uHTS assay could be applied to future scaling up of a screening campaign to identify small molecule inhibitors targeting the SYK and FCER1G interaction., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

Rheumatoid arthritis (RA) is a chronic autoimmune disease that, when improperly treated, leads to disability in patients. Various factors that may cause the development and activity of RA are being considered. Epigenetic factors are also receiving increasing attention. In our study, we analyzed the association between FCER1G gene methylation and RA activity. We conducted our study in 50 RA patients and 24 controls. The patients were divided into two groups in terms of high disease activity and remission. Quantitative real-time methylation-specific PCR was used to analyze the methylation status of the investigated genes. We observed that RA patients have lower levels of methylation of the FCER1G gene compared to controls, but we did not find any difference in the methylation status of this gene between patients with high disease activity and remission. The results of this study suggest that FCER1G gene methylation may be a new potential epigenetic marker of RA that is independent of disease activity. [ABSTRACT FROM AUTHOR]

Diffuse large B cell lymphoma (DLBCL) is a group of biologically heterogeneous tumors with different prognoses. The tumor microenvironment plays a vital role in the tumorigenesis and development of DLBCL, and activated memory CD4+ T cells are an essential component of immunological cells in the lymphoma microenvironment. So far, there are few reports about activated memory CD4+T cells infiltration and related genes in the DLBCL tumor microenvironment. This study obtained the mRNA expression profile information of the testing GSE87371 dataset and another six validation datasets (GSE53786, GSE181063, GSE10846, GSE32918, GSE32018, GSE9327, GSE3892, TCGA-DLBC) from the GEO and TCGA databases. Weighted Gene Co-expression Network Analysis (WGCNA) screened gene module associated with activated memory CD4+ T cells infiltration. CIBERSORT and TIMER (immune cells infiltrating estimation analysis tools) were used to identify the relationship between activated memory CD4+ T cells and genes associated with immune infiltrating cells in the tumor microenvironment. The least absolute shrinkage and selection operator (LASSO) built the risk prediction model and verified it using nomogram and Kaplan-Meier analysis. Further functional characterization includes Gene Ontology, KEGG pathway analysis and Gene Set Enrichment Analysis (GSEA) to investigate the role and underlying mechanisms of these genes. These results suggest that the expression of FCER1G can reflect the invasion of activated memory CD4+ T cells in DLBCL, which provides a new idea for studying the tumor microenvironment and may become a potential predictive biomarker for the assessment of DLBCL. [ABSTRACT FROM AUTHOR]

Abstract Purpose Glioma is the most prevalent malignant form of brain tumors, with a dismal prognosis. Currently, cancer immunotherapy has emerged as a revolutionary treatment for patients with advanced highly aggressive therapy-resistant tumors. However, there is no effective biomarker to reflect the response to immunotherapy in glioma patient so far. So we aim to assess the clinical predictive value of FCER1G in patients with glioma. Methods The expression level and correlation between clinical prognosis and FER1G levels were analyzed with the data from CGGA, TCGA, and GEO database. Univariate and multivariate cox regression model was built to predict the prognosis of glioma patients with multiple factors. Then the correlation between FCER1G with immune cell infiltration and activation was analyzed. At last, we predict the immunotherapeutic response in both high and low FCER1G expression subgroups. Results FCER1G was significantly higher in glioma with greater malignancy and predicted poor prognosis. In multivariate analysis, the hazard ratio of FCER1G expression (Low versus High) was 0.66 and 95 % CI is 0.54 to 0.79 (P

Diffuse large B cell lymphoma (DLBCL) is a group of biologically heterogeneous tumors with different prognoses. The tumor microenvironment plays a vital role in the tumorigenesis and development of DLBCL, and activated memory CD4+ T cells are an essential component of immunological cells in the lymphoma microenvironment. So far, there are few reports about activated memory CD4+T cells infiltration and related genes in the DLBCL tumor microenvironment. This study obtained the mRNA expression profile information of the testing GSE87371 dataset and another six validation datasets (GSE53786, GSE181063, GSE10846, GSE32918, GSE32018, GSE9327, GSE3892, TCGA-DLBC) from the GEO and TCGA databases. Weighted Gene Co-expression Network Analysis (WGCNA) screened gene module associated with activated memory CD4+ T cells infiltration. CIBERSORT and TIMER (immune cells infiltrating estimation analysis tools) were used to identify the relationship between activated memory CD4+ T cells and genes associated with immune infiltrating cells in the tumor microenvironment. The least absolute shrinkage and selection operator (LASSO) built the risk prediction model and verified it using nomogram and Kaplan-Meier analysis. Further functional characterization includes Gene Ontology, KEGG pathway analysis and Gene Set Enrichment Analysis (GSEA) to investigate the role and underlying mechanisms of these genes. These results suggest that the expression of FCER1G can reflect the invasion of activated memory CD4+ T cells in DLBCL, which provides a new idea for studying the tumor microenvironment and may become a potential predictive biomarker for the assessment of DLBCL.

Background: Tumor-associated macrophages (TAMs) are closely related to unfavorable prognosis of patients with clear cell renal cell carcinoma (ccRCC). However, the important molecules in the interaction between ccRCC and TAMs are unclear.Methods: TCGA-KIRC gene expression data of tumor tissues and normal tissues adjacent to tumor were compared to identify differentially expressed genes in ccRCC. TAMs related genes were discovered by analyzing the correlation between these differentially expressed genes and common macrophage biomarkers. Gene set enrichment analysis was performed to predict functions of TAMs related gene. The findings were further validated using RNA sequencing data obtained from the CheckMate 025 study and immunohistochemical analysis of samples from 350 patients with ccRCC. Kaplan-Meier survival curve, Cox regression analysis and Harrell's concordance index analysis were used to determine the prognostic significance.Results: In this study, we applied bioinformatic analysis to explore TAMs related differentially expressed genes in ccRCC and identified 5 genes strongly correlated with all selected macrophage biomarkers: STAC3, LGALS9, TREM2, FCER1G, and PILRA. Among them, FCER1G was abundantly expressed in tumor tissues and showed prognostic importance in patients with ccRCC who received treatment with Nivolumab; however, it did not exhibit prognostic value in those treated with Everolimus. We also discovered that high expression levels of FCER1G are related to T cell suppression. Moreover, combination of FCER1G and macrophage biomarker CD68 can improve the prognostic stratification of patients with ccRCC from TCGA-KIRC. Based on the immunohistochemical analysis of samples from patients with ccRCC, we further validated that FCER1G and CD68 are both highly expressed in tumor tissue and correlate with each other. Higher expression of CD68 or FCER1G in ccRCC tissue indicates shorter overall survival and progression-free survival; patients with high expression of both CD68 and FCER1G have the worst outcome. Combining CD68 and FCER1G facilitates the screening of patients with a worse prognosis from the same TNM stage group.Conclusions: High expression of FCER1G in ccRCC is closely related to TAMs infiltration and suppression of T cell activation and proliferation. Combining the expression levels of FCER1G and macrophage biomarker CD68 may be a promising postoperative prognostic index for patients with ccRCC. [ABSTRACT FROM AUTHOR]

Background: Cardioembolic stroke (CE) exhibits the highest recurrence rate and mortality rate among all subtypes of cerebral ischemic stroke (CIS), yet its pathogenesis remains uncertain. The immune system plays a pivotal role in the progression of CE. Growing evidence indicates that several immune-associated blood biomarkers may inform the causes of stroke. The study aimed to identify new immune-associated blood biomarkers in patients with CE and create an online predictive tool in distinguishing CE from noncardioembolic stroke (non-CE) in CIS., Methods: Gene expression profiles that were publicly available were obtained from the Gene Expression Omnibus (GEO). The identification of differentially expressed genes (DEGs) was conducted using the Limma package. The hub module and hub genes were identified through the application of weighted gene coexpression network analysis (WGCNA). In order to identify potential diagnostic biomarkers for CE, both the random forest (RF) model and least absolute shrinkage and selection operator (LASSO) regression analysis were employed. Concurrently, the CIBERSORT algorithm was employed to evaluate the infiltration of immune cells in CE samples and examine the correlation between the biomarkers and the infiltrating immune cells. The diagnostic gene expression in blood samples was confirmed using qRT-PCR in a self-constructed dataset. Univariate and multiple logistic regression analyses were used to identify the risk factors for CE. Subsequently, the mathematical model of the nomogram was employed via Java's "Spring Boot" framework to develop the corresponding online tool, which was then deployed on a cloud server utilizing "nginx"., Results: Eleven differentially expressed genes (DEGs) that were upregulated and seven DEGs that were downregulated were identified. Through bioinformatics analysis and clinical sample verification, it was discovered that Fc Fragment of IgE Receptor Ig ( FCER1G ) could serve as a novel potential blood biomarker for CE. FCER1G , along with other risk factors associated with CE, were utilized to develop a nomogram. The training and validation sets, which consisted of 65 CIS patients, yielded areas under the curve (AUCs) of 0.9722 and 0.9689, respectively. These results indicate a high level of precision in risk delineation by the nomogram. Furthermore, the associated online predictive platform has the potential to serve as a more efficacious and appropriate predictive instrument (https://www.origingenetic.com/CardiogenicStroke-FCER1G) for distinguishing between CE and non-CE., Conclusion: Blood biomarker FCER1G has the potential to identify patients who are at a higher risk of cardioembolism and direct the search for occult AF.The utilization of this online tool is anticipated to yield significant implications in terms of distinguishing between CE and non-CE, as well as enhancing the optimization of treatment decision support., Competing Interests: The authors declare that they have no competing interests., (© 2024 The Authors. Published by Elsevier Ltd.)

The spleen tyrosine kinase (SYK) and high affinity immunoglobulin epsilon receptor subunit gamma (FCER1G) interaction has a major role in the normal innate and adaptive immune responses, but dysregulation of this interaction is implicated in several human diseases, including autoimmune disorders, hematological malignancies, and Alzheimer's Disease. Development of small molecule chemical probes could aid in studying this pathway both in normal and aberrant contexts. Herein, we describe the miniaturization of a time-resolved fluorescence resonance energy transfer (TR-FRET) assay to measure the interaction between SYK and FCER1G in a 1536-well ultrahigh throughput screening (uHTS) format. The assay utilizes the His-SH2 domains of SYK, which are indirectly labeled with anti-His-terbium to serve as TR-FRET donor and a FITC-conjugated phosphorylated ITAM domain peptide of FCER1G to serve as acceptor. We have optimized the assay into 384-well HTS format and further miniaturized the assay into a 1536-well uHTS format. Robust assay performance has been achieved with a Z' factor > 0.8 and signal-to-background (S/B) ratio > 15. The utilization of this uHTS TR-FRET assay for compound screening has been validated by a pilot screening of 2,036 FDA-approved and bioactive compounds library. Several primary hits have been identified from the pilot uHTS. One compound, hematoxylin, was confirmed to disrupt the SYK/FECR1G interaction in an orthogonal protein-protein interaction assay. Thus, our optimized and miniaturized uHTS assay could be applied to future scaling up of a screening campaign to identify small molecule inhibitors targeting the SYK and FCER1G interaction.

The involvement of aberrantly expressed genes in the pathogenesis and progression of various human malignancies has been widely reported, including clear cell renal cell carcinoma (ccRCC). This study aimed to identify potential crucial genes in ccRCC and further investigate the role of these genes in ccRCC prognosis. Three gene expression profiles (GSE3, GSE6344 and GSE53000) were downloaded from GEO database. GEO2R was performed to identify the differentially expressed genes (DEGs) between ccRCC and normal samples. GO analysis and KEGG pathway enrichment analysis were applied for the function analysis. The DEGs were mapped into the PPI network, then the hub genes were identified and verified using the ONCOMINE database. Kaplan–Meier plotter was used to evaluate of the prognostic value of the identified hub genes. A total of 113 DEGs were identified from the three gene expression profiles, including 64 up-regulated genes and 69 down-regulated genes. DEGs were observed to be enriched in biological processes related to the progress and pathogenesis of human cancers. According to PPI network, 5 hub genes were collected, including TYROBP, C1QB, ITGB2, CD53 and FCER1G. Among them, CD53 was newly identified, and Kaplan–Meier survival curves suggested that high expression of CD53 was significantly associated with poor survival in ccRCC patients (log-rank P < 0.01). The present results may provide new insight into the understanding of molecular mechanisms and the clinical prognosis of ccRCC. [ABSTRACT FROM AUTHOR]

Activating killer cell immunoglobulin-like receptors (KIR) in macaques are thought to be derived by genetic recombination of the region encoding the transmembrane and intracellular part of KIR2DL4 and a KIR3D gene. As a result, all macaque activating KIR possess a positively charged arginine residue in the transmembrane region. As human KIR2DL4 associates with the FCER1G (also called Fc receptor-gamma, FcRγ) adaptor, we hypothesized that in contrast to human and great ape the activating KIRs of macaques associate with FcRγ instead of DAP12. By applying co-immunoprecipitation of transfected as well as primary cells, we demonstrate that rhesus macaque KIR3DS05 indeed associates with FcRγ and not with DAP12. This association with FcRγ results in increased and substantially stabilized surface expression of KIR3DS05. In addition, we demonstrate that binding of specific ligands of KIR3DS05, Mamu-A1*001 and A1*011, resulted in signal transduction in the presence of FcRγ in contrast to DAP12.

Activating killer cell immunoglobulin-like receptors (KIR) in macaques are thought to be derived by genetic recombination of the region encoding the transmembrane and intracellular part of KIR2DL4 and a KIR3D gene. As a result, all macaque activating KIR possess a positively charged arginine residue in the transmembrane region. As human KIR2DL4 associates with the FCER1G (also called Fc receptor-gamma, FcRγ) adaptor, we hypothesized that in contrast to human and great ape the activating KIRs of macaques associate with FcRγ instead of DAP12. By applying co-immunoprecipitation of transfected as well as primary cells, we demonstrate that rhesus macaque KIR3DS05 indeed associates with FcRγ and not with DAP12. This association with FcRγ results in increased and substantially stabilized surface expression of KIR3DS05. In addition, we demonstrate that binding of specific ligands of KIR3DS05, Mamu-A1*001 and A1*011, resulted in signal transduction in the presence of FcRγ in contrast to DAP12. [ABSTRACT FROM AUTHOR]

Purpose: Glioma is the most prevalent malignant form of brain tumors, with a dismal prognosis. Currently, cancer immunotherapy has emerged as a revolutionary treatment for patients with advanced highly aggressive therapy-resistant tumors. However, there is no effective biomarker to reflect the response to immunotherapy in glioma patient so far. So we aim to assess the clinical predictive value of FCER1G in patients with glioma. Methods: The expression level and correlation between clinical prognosis and FER1G levels were analyzed with the data from CGGA, TCGA, and GEO database. Univariate and multivariate cox regression model was built to predict the prognosis of glioma patients with multiple factors. Then the correlation between FCER1G with immune cell infiltration and activation was analyzed. At last, we predict the immunotherapeutic response in both high and low FCER1G expression subgroups. Results: FCER1G was significantly higher in glioma with greater malignancy and predicted poor prognosis. In multivariate analysis, the hazard ratio of FCER1G expression (Low versus High) was 0.66 and 95 % CI is 0.54 to 0.79 (P < 0.001), whereas age (HR = 1.26, 95 % CI 1.04–1.52), grade (HR = 2.75, 95 % CI 2.06–3.68), tumor recurrence (HR = 2.17, 95 % CI 1.81–2.62), IDH mutant (HR = 2.46, 95 % CI 1.97–3.01) and chemotherapeutic status (HR = 1.4, 95 % CI 1.20–1.80) are also included. Furthermore, we illustrated that gene FCER1G stratified glioma cases into high and low FCER1G expression subgroups that demonstrated with distinct clinical outcomes and T cell activation. At last, we demonstrated that high FCER1G levels presented great immunotherapeutic response in glioma patients. Conclusions: This study demonstrated FCER1G as a novel predictor for clinical diagnosis, prognosis, and response to immunotherapy in glioma patient. Assess expression of FCER1G is a promising method to discover patients that may benefit from immunotherapy. [ABSTRACT FROM AUTHOR]

The impact of germline variants on the regulation of the expression of tumor microenvironment (TME)-based immune response genes remains unclear. Expression quantitative trait loci (eQTL) provide insight into the effect of downstream target genes (eGenes) regulated by germline-associated variants (eVariants). Through eQTL analyses, we illustrated the relationships between germline eVariants, TME-based immune response eGenes, and clinical outcomes. In this study, both RNA sequencing data from primary tumor and germline whole-genome sequencing data were collected from patients with stage III colorectal cancer (CRC). Ninety-nine high-risk subjects were subjected to immune response gene expression analyses. Seventy-seven subjects remained for further analysis after quality control, of which twenty-two patients (28.5%) experienced tumor recurrence. We found that 65 eQTL, including 60 germline eVariants and 22 TME-based eGenes, impacted the survival of cancer patients. For the recurrence prediction model, 41 differentially expressed genes (DEGs) achieved the best area under the receiver operating characteristic curve of 0.93. In total, 19 survival-associated eGenes were identified among the DEGs. Most of these genes were related to the regulation of lymphocytes and cytokines. A high expression of HGF, CCR5, IL18, FCER1G, TDO2, IFITM2, and LAPTM5 was significantly associated with a poor prognosis. In addition, the FCER1G eGene was associated with tumor invasion, tumor nodal stage, and tumor site. The eVariants that regulate the TME-based expression of FCER1G, including rs2118867 and rs12124509, were determined to influence survival and chromatin binding preferences. We also demonstrated that FCER1G and co-expressed genes in TME were related to the aggregation of leukocytes via pathway analysis. By analyzing the eQTL from the cancer genome using germline variants and TME-based RNA sequencing, we identified the eQTL in immune response genes that impact colorectal cancer characteristics and survival.

The resistance development of the combination regimen of corticosteroids (CS) with cyclosporin A (CsA) leads to therapeutic failure of some patients with autoimmune diseases. In the male patients with Vogt-Koyanagi-Harada (VKH) disease, we have identified RPS4Y1 as an important resistance gene of the regimen and a functional mediator of chlorambucil (CLB). However, it remains unclear what is responsible for the resistance in female patients. In the present study, we performed RNA sequencing, tandem mass tag (TMT) proteomics, gain- and loss-of-function assays and rescue assays to screen and validate potential resistant mediators. The results showed that only Fc epsilon receptor Ig (FCER1G) exhibited significantly differential expression in CD4+ T cells among female CsA & CS resistant, sensitive and CLB & CsA & CS treated patients at transcription and protein levels. Inhibition of FCER1G was demonstrated to modulate CD4+ T cell resistance to CsA & CS in female patients. Importantly, the inhibition was mediated by elevated DNA methylation in the promoter region of the FCER1G gene. Moreover, we found that the salvage effect of CLB on CsA & CS resistance was mediated by an increased FCER1G expression via DNA demethylation in female patients. Taken together, the downregulation of FCER1G due to DNA hypermethylation is responsible for the resistance to CsA & CS and CLB reverses this resistance by inducing FCER1G expression via DNA demethylation in female patients. Modulation of FCER1G would be a promising sensitization strategy in female patients with resistance to CsA & CS. [ABSTRACT FROM AUTHOR]

The high-affinity IgE receptor gamma subunit gene (FCER1G) plays a pivotal role in allergic inflammatory signaling, which is closely associated with allergic reactions. Previous studies have shown that FCER1G is an innate immunity gene and is involved in the development of many diseases. However, the role of FCER1G in pan-cancer has not been fully studied. This study aimed to explore the prognostic value of FCER1G in pan-cancer and investigate the relationship between FCER1G expression and immune infiltration. We analyzed FCER1G mRNA expression in the Oncomine, TIMER, and GEPIA databases. We used GEPIA, PrognoScan, and Kaplan-Meier Plotter to analyze the prognostic value of FCER1G. We explored the correlations between FCER1G expression and immune infiltration in TIMER databases. Cytoscape was used to perform pathway enrichment and functional enrichment analyses. Spearman method was used to determine the statistical significance.FCER1G can act as a prognostic marker for pan-cancer, FCER1G expression can affect patients' prognosis and correlate with immune cells infiltration. In kidney renal clear cell carcinoma and thyroid carcinoma, FCER1G is closely associated with different immune cells infiltration states and tumor purity, and immune infiltration can interact with FCER1G-mediated activities both in tumor cells and immune cells. FCER1G can be used to predict the efficacy of immunological checkpoint therapy among these types of tumors patients. Our study also provides an important basis for the clinical use of FCER1G to assess the patient immune status and the selection of individualized immunotherapy options. [ABSTRACT FROM AUTHOR]

The impact of germline variants on the regulation of the expression of tumor microenvironment (TME)-based immune response genes remains unclear. Expression quantitative trait loci (eQTL) provide insight into the effect of downstream target genes (eGenes) regulated by germline-associated variants (eVariants). Through eQTL analyses, we illustrated the relationships between germline eVariants, TME-based immune response eGenes, and clinical outcomes. In this study, both RNA sequencing data from primary tumor and germline whole-genome sequencing data were collected from patients with stage III colorectal cancer (CRC). Ninety-nine high-risk subjects were subjected to immune response gene expression analyses. Seventy-seven subjects remained for further analysis after quality control, of which twenty-two patients (28.5%) experienced tumor recurrence. We found that 65 eQTL, including 60 germline eVariants and 22 TME-based eGenes, impacted the survival of cancer patients. For the recurrence prediction model, 41 differentially expressed genes (DEGs) achieved the best area under the receiver operating characteristic curve of 0.93. In total, 19 survival-associated eGenes were identified among the DEGs. Most of these genes were related to the regulation of lymphocytes and cytokines. A high expression of HGF, CCR5, IL18, FCER1G, TDO2, IFITM2, and LAPTM5 was significantly associated with a poor prognosis. In addition, the FCER1G eGene was associated with tumor invasion, tumor nodal stage, and tumor site. The eVariants that regulate the TME-based expression of FCER1G, including rs2118867 and rs12124509, were determined to influence survival and chromatin binding preferences. We also demonstrated that FCER1G and co-expressed genes in TME were related to the aggregation of leukocytes via pathway analysis. By analyzing the eQTL from the cancer genome using germline variants and TME-based RNA sequencing, we identified the eQTL in immune response genes that impact colorectal cancer characteristics and survival. [ABSTRACT FROM AUTHOR]

Summary: Background and objective: The high-affinity IgE receptor comprises a tetramer of the ligand-binding alpha chain, a signal-augmenting beta chain, and a signal-transducing gamma chain dimer on mast cells. We hypothesized that the three subsets of the FCER1 gene may play a role in the development of the aspirin-intolerant asthma (AIA) phenotype and analyzed these genetic polymorphisms in association with clinical parameters in AIA patients. Subjects and methods: Six polymorphisms of FCER1 (FCERIA-344C>T, FCER1A-95T>C, MS4A2-109T>C, MS4A2 E237G, FCER1G-237A>G, FCER1G-54G>T) were genotyped in 126 AIA patients compared to 177 patients with aspirin-tolerant asthma (ATA) and 222 normal health controls (NC). Results: A significant difference in the genotype frequencies of FCER1G-237A>G was detected between AIA and ATA patients (p <0.05) both in co-dominant and recessive analysis models, whereas no significant relationships were identified between the frequencies of the other five single-nucleotide polymorphisms and AIA, ATA, and NC subjects. In addition, AIA patients carrying the homozygous AA genotype of FCER1G-237A>G exhibited significantly higher total serum IgE levels than did those with the GG/AG genotype (p =0.012). AIA patients expressing the CT/TT genotype at FCERIA-344C>T showed a higher prevalence of serum IgE specific to Staphylococcal enterotoxin A than did those with the CC genotype (p =0.008). Conclusion: The FCER1G-237A>G and FCERIA-344C>T polymorphisms may contribute to the development of AIA in a Korean population. [Copyright &y& Elsevier]

Diffuse large B cell lymphoma (DLBCL) is a group of biologically heterogeneous tumors with different prognoses. The tumor microenvironment plays a vital role in the tumorigenesis and development of DLBCL, and activated memory CD4 + T cells are an essential component of immunological cells in the lymphoma microenvironment. So far, there are few reports about activated memory CD4+T cells infiltration and related genes in the DLBCL tumor microenvironment. This study obtained the mRNA expression profile information of the testing GSE87371 dataset and another six validation datasets (GSE53786, GSE181063, GSE10846, GSE32918, GSE32018, GSE9327, GSE3892, TCGA-DLBC) from the GEO and TCGA databases. Weighted Gene Co-expression Network Analysis (WGCNA) screened gene module associated with activated memory CD4 + T cells infiltration. CIBERSORT and TIMER (immune cells infiltrating estimation analysis tools) were used to identify the relationship between activated memory CD4 + T cells and genes associated with immune infiltrating cells in the tumor microenvironment. The least absolute shrinkage and selection operator (LASSO) built the risk prediction model and verified it using nomogram and Kaplan-Meier analysis. Further functional characterization includes Gene Ontology, KEGG pathway analysis and Gene Set Enrichment Analysis (GSEA) to investigate the role and underlying mechanisms of these genes. These results suggest that the expression of FCER1G can reflect the invasion of activated memory CD4 + T cells in DLBCL, which provides a new idea for studying the tumor microenvironment and may become a potential predictive biomarker for the assessment of DLBCL., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Xiang, Gao, Zhang, Tang, Zhao, Liu, Ye and Zhang.)

Background: Diabetic kidney disease (DKD) is the primary cause of end-stage renal disease. However, the pathogenesis of DKD remains unclarified, and there is an urgent need for improved treatments. Recently, many crucial genes closely linked to the molecular mechanism underlying various diseases were discovered using weighted gene co-expression network analysis., Methods: We used a gene expression omnibus series dataset GSE104948 with 12 renal glomerular DKD tissue samples and 18 control samples obtained from the gene expression omnibus database and performed weighted gene co-expression network analysis. After obtaining the trait-related modules, gene ontology and Kyoto encyclopedia of genes and genomes enrichment analyses of the modules were conducted and the key gene associated with DKD was selected from the top two most significant gene ontology terms using the maximal clique centrality method. Finally, we verified the key gene using protein-protein interaction analysis, additional datasets, and explored the relationship between the key gene and DKD renal function using the Nephroseq v5 online database., Results: Among the 10 gene co-expression modules identified, the darkorange2 and red modules were highly related to DKD and the normal biological process, respectively. Majority of the genes in the darkorange2 module were related to immune and inflammatory responses, and potentially related to the progression of DKD due to their abnormal up-regulation. After performing sub-network analysis of the genes extracted from the top two most significant gene ontology terms and calculating the maximal clique centrality values of each gene, FCER1G , located at the center of the protein-protein interaction network, was identified as a key gene related to DKD. Furthermore, gene expression omnibus validation in additional datasets also showed that FCER1G was overexpressed in DKD compared with normal tissues. Finally, Pearson's correlation analysis between the expression of FCER1G and DKD renal function revealed that the abnormal upregulation of FCER1G was related to diabetic glomerular lesions., Conclusions: Our study demonstrated for the first time that FCER1G is a crucial gene associated with the pathogenesis of DKD., Competing Interests: Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at http://dx.doi.org/10.21037/atm-20-1087). The authors have no conflicts of interest to declare., (2020 Annals of Translational Medicine. All rights reserved.)

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., Competing Interests: Competing Interests: The authors have declared that no competing interest exists., (© The author(s).)

Clear cell renal cell carcinoma (ccRCC) is the most common solid lesion within kidney, and its prognostic is influenced by the progression covering a complex network of gene interactions. In current study, the microarray data GSE66272 containing ccRCC and adjacent normal tissues was analyzed to identify 4042 differentially expressed genes, on which weighted gene co-expression network analysis was performed. Then 12 co-expressed gene modules were identified. The highest association was found between blue module and pathological stage (r = -0.77) by Pearson's correlation analysis. Functional enrichment analysis revealed that biological processes of blue module focused on inflammatory response, immune response, chemotaxis (all p < 1e-10). In the significant module, a total of 38 network hub genes were identified, FCER1G exhibited the highest correlation (r = 0.95) with ccRCC progression. In addition, FCER1G was hub node in the protein-protein interaction network of the genes in blue module as well. Thus, FCER1G was subsequently selected for validation. In the test set GSE53757 and RNA-sequencing data, FCER1G expression was also positively correlated with four stages of ccRCC progression (p < 0.001). Receiver operating characteristic (ROC) curve indicated that FCER1G could distinguish localized (pathological stage I, II) from non-localized (pathological stage III, IV) ccRCC (AUC=0.74, p < 0.001). Besides, FCER1G could be a prognostic gene in clinical practice as well, revealed by survival analysis based on RNA-sequencing data (p < 0.05). In conclusion, using weighted gene co-expression analysis, FCER1G was identified and validated in association with ccRCC progression and prognosis, which might improve the prognosis by influencing immune-related pathways., Competing Interests: Competing Interests: The authors have declared that no competing interest exists.