scholarly journals PRER: A patient representation with pairwise relative expression of proteins on biological networks

2021 ◽  
Vol 17 (5) ◽  
pp. e1008998
Author(s):  
Halil İbrahim Kuru ◽  
Mustafa Buyukozkan ◽  
Oznur Tastan
Keyword(s):  
Biological Networks ◽  
Expression Profiles ◽  
Predictive Performance ◽  
Survival Models ◽  
Survival Prediction ◽  
Protein Biomarkers ◽  
Expression Levels ◽  
Biased Random Walk ◽  
Aggregate Information ◽  
Ppi Networks

Changes in protein and gene expression levels are often used as features in predictive modeling such as survival prediction. A common strategy to aggregate information contained in individual proteins is to integrate the expression levels with the biological networks. In this work, we propose a novel patient representation where we integrate proteins’ expression levels with the protein-protein interaction (PPI) networks: Patient representation with PRER (Pairwise Relative Expressions with Random walks) (PRER). PRER captures the dysregulation patterns of proteins based on the neighborhood of a protein in the PPI network. Specifically, PRER computes a feature vector for a patient by comparing the source protein’s expression level with other proteins’ levels that are within its neighborhood. The neighborhood of the source protein is derived by biased random-walk strategy on the network. We test PRER’s performance in survival prediction task in 10 different cancers using random forest survival models. PRER yields a statistically significant predictive performance in 9 out of 10 cancers when compared to the same model trained with features based on individual protein expressions. Furthermore, we identified the pairs of proteins that their interactions are predictive of patient survival but their individual expression levels are not. The set of identified relations provides a valuable collection of protein biomarkers with high prognostic value. PRER can be used for other complex diseases and prediction tasks that use molecular expression profiles as input. PRER is freely available at: https://github.com/hikuru/PRER.

scholarly journals PRER: A Patient Representation with Pairwise Relative Expression of Proteins on Biological Networks

2020 ◽  
Author(s):  
Halil İbrahim Kuru ◽  
Mustafa Buyukozkan ◽  
Oznur Tastan
Keyword(s):  
Protein Expression ◽  
Biological Networks ◽  
Expression Profiles ◽  
Predictive Performance ◽  
Survival Models ◽  
Survival Prediction ◽  
Expression Levels ◽  
Biased Random Walk ◽  
Aggregate Information ◽  
Ppi Networks

AbstractChanges in protein and gene expression levels are often used as features to predictive models such as survival prediction. A common strategy to aggregate information on individual proteins is to integrate the expression information with biological networks. We propose a novel patient representation in this work where we integrate proteins’ expression levels with the protein-protein interaction (PPI) networks. Patient representation with PRER (Pairwise Relative Expressions with Random walks) uses the neighborhood of a protein to capture the dysregulation patterns in protein abundance. Specifically, PRER computes a feature vector for a patient by comparing the source protein’s protein expression level with other proteins’ levels in its neighborhood. This neighborhood of the source protein is derived using a biased random-walk strategy on the network. We test PRER’s performance through a survival prediction task in 10 different cancers using random forest survival models. PRER representation yields a statistically significant predictive performance in 9 out of 10 cancer types when compared to a representation based on individual protein expression. We also identify important proteins that are not important in the models trained with the expression values but emerge as predictive in models trained with PRER features. The set of identified relations provides a valuable collection of biomarkers with high prognostic value. PRER representation can be used for other complex diseases and prediction tasks that use molecular expression profiles as input. PRER is freely available at: https://github.com/hikuru/PRER

scholarly journals Role of miR-10b-5p in the prognosis of breast cancer

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e7728 ◽  
Author(s):  
Junmin Wang ◽  
Yanyun Yan ◽  
Zhiqi Zhang ◽  
Yali Li
Keyword(s):  
Breast Cancer ◽  
Target Genes ◽  
Expression Profiles ◽  
Expression Patterns ◽  
Cancer Development ◽  
Clinicopathological Parameters ◽  
Aberrant Expression ◽  
Expression Levels ◽  
Ppi Networks

Breast cancer is the leading cause of cancer-related death in women worldwide. Aberrant expression levels of miR-10b-5p in breast cancer has been reported while the molecular mechanism of miR-10b-5p in tumorigenesis remains elusive. Therefore, this study was aimed to investigate the role of miR-10b-5p in breast cancer and the network of its target genes using bioinformatics analysis. In this study, the expression profiles and prognostic value of miR-10b-5p in breast cancer were analyzed from public databases. Association between miR-10b-5p and clinicopathological parameters were analyzed by non-parametric test. Moreover, the optimal target genes of miR-10b-5p were obtained and their expression patterns were examined using starBase and HPA database. Additionally, the role of these target genes in cancer development were explored via Cancer Hallmarks Analytics Tool (CHAT). The protein–protein interaction (PPI) networks were constructed to further investigate the interactive relationships among these genes. Furthermore, GO, KEGG pathway and Reactome pathway analyses were carried out to decipher functions of these target genes. Results demonstrated that miR-10b-5p was down-regulated in breast cancer and low expression of miR-10b-5p was significantly correlated to worse outcome. Five genes, BIRC5, E2F2, KIF2C, FOXM1, and MCM5, were considered as potential key target genes of miR-10b-5p. As expected, higher expression levels of these genes were observed in breast cancer tissues than in normal tissues. Moreover, analysis from CHAT revealed that these genes were mainly involved in sustaining proliferative signaling in cancer development. In addition, PPI networks analysis revealed strong interactions between target genes. GO, KEGG, and Reactome pathway analysis suggested that these target genes of miR-10b-5p in breast cancer were significantly involved in cell cycle. Predicted target genes were further validated by qRT-PCR analysis in human breast cancer cell line MDA-MB-231 transfected with miR-10b mimic or antisense inhibitors. Taken together, our data suggest that miR-10b-5p functions to impede breast carcinoma progression via regulation of its key target genes and hopefully serves as a potential diagnostic and prognostic marker for breast cancer.

NEAT1 Is a Novel Oncogenic LncRNA and Correlated with miR-143 in Pediatric Oligodendrogliomas

2021 ◽  
Vol 56 (2) ◽  
pp. 133-139
Author(s):  
Secil Ak Aksoy ◽  
Melis Mutlu ◽  
Rabia Nur Balcin ◽  
Mevlut Ozgur Taskapilioglu ◽  
Cagla Tekin ◽  
...  
Keyword(s):  
Noncoding Rna ◽  
Expression Profiles ◽  
Prognostic Significance ◽  
Disease Free Survival ◽  
Pediatric Brain Tumors ◽  
Normal Brain ◽  
Diffuse Glioma ◽  
Expression Levels ◽  
Treatment Models ◽  
New Treatment

Introduction: The noncoding RNAs (ncRNAs) play a role in biological processes of various cancers including gliomas. The majority of these transcripts are uniquely expressed in differentiated tissues or specific glioma types. Pediatric oligodendroglioma (POG) is a rare subtype of diffuse glioma and accounts for <1% of pediatric brain tumors. Because histologically POG resembles adult OG, the same treatment is applied as adults. However, the significance in predicting outcomes in POG patients is unclear. In this study, we aimed to investigate the prognostic significance of expression ­profiles of microRNA (miRNA) and long noncoding RNA ­(LncRNA) in POGs. Methods: We investigated the levels of 13 known miRNAs and 6 LncRNAs in tumor samples from 9 patients with primary POG by using RT-PCR and analyzed their association with outcomes. Results: The expression levels of miR-21, miR-106a, miR-10b, and LncRNA NEAT1 were higher, and the expression level of miR-143 was lower in POG tissues compared with normal brain tissues (p = 0.006, p = 0.032, p = 0.034, p = 0.002, and p = 0.001, respectively). High levels of NEAT1 and low expression of miR-143 were associated with decreased probability of short disease-free survival (p = 0.018 and p = 0.022, respectively). Discussion: NEAT1 and miR-143 levels could serve as reciprocal prognostic predictors of disease progression in patients with POG. New treatment models to regulate the expression levels of NEAT1 and miR-143 will bring a new approach to the therapy of POG.

scholarly journals POS0859 DEEP PHENOTYPING OF DERMATOMYOSITIS BASED ON LIPID FERROPTOSIS-RELATED GENES BY MACHINE LEARNING

2021 ◽  
Vol 80 (Suppl 1) ◽  
pp. 684.1-684
Author(s):  
J. Q. Zhang ◽  
S. X. Zhang ◽  
R. Zhao ◽  
J. Qiao ◽  
M. T. Qiu ◽  
...  
Keyword(s):  
Gene Expression ◽  
T Cells ◽  
Expression Profiles ◽  
Inflammatory Myopathy ◽  
Shanxi Province ◽  
Survival Prediction ◽  
Training Set ◽  
Redox Biology ◽  
Score Model ◽  
Validation Set

Background:Dermatomyositis (DM) is an idiopathic inflammatory myopathy with heterogeneous clinical manifestation that raise challenges regarding diagnosis and therapy1. Ferroptosis is a newly discovered form of regulated cell death that is the nexus between metabolism, redox biology, and rheumatic immune diseases2. However, how ferroptosis maintains the balance of lymphocyte T cells and affect disease activity in DM is unclear.Objectives:To investigate an ferroptosis-related multiple gene expression signature for classification by assessing the global gene expression profile, and calculate the lymphocyte T cells status in the different subsets.Methods:Gene expression profiles of skeletal muscle from DM samples were acquired from GEO database. GSE143323 (30 patients and 20 HCs) was selected as the training set. The GSE3307 contained 21 DM patients and was selected as the validation set. The 60 ferroptosis genes were obtained from previous literature3. The intersection of the global gene and ferroptosis genes was considered the set of significant G-Ferroptosis genes for further analysis. The “NMF” (R-package) was applied as an unsupervised clustering method for sample classification by using G-Ferroptosis genes expression microarray data from the training datasets. An ferroptosis score model was constructed. The performance of the ferroptosis genes-based risk score model constructed by the DM training set was validated in the batch-1 and batch-2 DM sets. Normalized ferroptosis genes training data was used to compare the ssGSEA scores of gene sets between the high risk and low risk group. The statistical software package R (version 4.0.3) was used for all analyses. P value < 0.05 were considered statistically significant.Results:We selected 54 significant G-Ferroptosis genes for further analysis in training set. There were 2 distinct subtypes (high-ferroptosis-score groups and low-ferroptosis-score groups) identified in G-Ferroptosis genes cohort which were also identified in validation datasets (Fig.1A, C, D). Metallothionein 1G (MT1G) was a characteristic gene of low-ferroptosis-score group. The characteristic genes of high-ferroptosis-score group were acyl-CoA synthetase family member 2(ACSF2) and aconitase 1(ACO1) (Fig.1B). Patients in high-ferroptosis-score group had a lower level of Tregs compared with that of low-ferroptosis-score patients in both training and validation set (P <0.05, Fig.1E).Conclusion:The biological process of ferroptosis is associated with the lever of Tregs, suggesting the process of ferroptosis may be involved in the disease progression of DM. Identificating ferroptosis-related features for DM might provide a new idea for clinical treatment.References:[1]DeWane ME, Waldman R, Lu J. Dermatomyositis: Clinical features and pathogenesis. Journal of the American Academy of Dermatology 2020;82(2):267-81. doi: 10.1016/j.jaad.2019.06.1309 [published Online First: 2019/07/08].[2]Liang C, Zhang X, Yang M, et al. Recent Progress in Ferroptosis Inducers for Cancer Therapy. Advanced materials (Deerfield Beach, Fla) 2019;31(51):e1904197. doi: 10.1002/adma.201904197 [published Online First: 2019/10/09].[3]Liang JY, Wang DS, Lin HC, et al. A Novel Ferroptosis-related Gene Signature for Overall Survival Prediction in Patients with Hepatocellular Carcinoma. International journal of biological sciences 2020;16(13):2430-41. doi: 10.7150/ijbs.45050 [published Online First: 2020/08/08].Acknowledgements:This project was supported by National Science Foundation of China (82001740).Open Fund from the Key Laboratory of Cellular Physiology (Shanxi Medical University) (KLCP2019) and Innovation Plan for Postgraduate Education in Shanxi Province (2020BY078).Disclosure of Interests:None declared

scholarly journals Overexpression of β-Arrestins inhibits proliferation and motility in triple negative breast cancer cells

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Saber Yari Bostanabad ◽  
Senem Noyan ◽  
Bala Gur Dedeoglu ◽  
Hakan Gurdal
Keyword(s):  
Breast Cancer ◽  
Cell Cycle ◽  
Cancer Cells ◽  
Cancer Cell ◽  
Breast Cancer Cells ◽  
Cell Function ◽  
Expression Profiles ◽  
Tissue Samples ◽  
Expression Levels ◽  
Cell Cycle Genes

Abstractβ-Arrestins (βArrs) are intracellular signal regulating proteins. Their expression level varies in some cancers and they have a significant impact on cancer cell function. In general, the significance of βArrs in cancer research comes from studies examining GPCR signalling. Given the diversity of different GPCR signals in cancer cell regulation, contradictory results are inevitable regarding the role of βArrs. Our approach examines the direct influence of βArrs on cellular function and gene expression profiles by changing their expression levels in breast cancer cells, MDA-MB-231 and MDA-MB-468. Reducing expression of βArr1 or βArr2 tended to increase cell proliferation and invasion whereas increasing their expression levels inhibited them. The overexpression of βArrs caused cell cycle S-phase arrest and differential expression of cell cycle genes, CDC45, BUB1, CCNB1, CCNB2, CDKN2C and reduced HER3, IGF-1R, and Snail. Regarding to the clinical relevance of our results, low expression levels of βArr1 were inversely correlated with CDC45, BUB1, CCNB1, and CCNB2 genes compared to normal tissue samples while positively correlated with poorer prognosis in breast tumours. These results indicate that βArr1 and βArr2 are significantly involved in cell cycle and anticancer signalling pathways through their influence on cell cycle genes and HER3, IGF-1R, and Snail in TNBC cells.

Identification of 6 gene markers for survival prediction in osteosarcoma cases based on multi-omics analysis

2021 ◽  
pp. 153537022199201
Author(s):  
Runmin Li ◽  
Guosheng Wang ◽  
ZhouJie Wu ◽  
HuaGuang Lu ◽  
Gen Li ◽  
...  
Keyword(s):  
Feature Selection ◽  
Cox Regression ◽  
Cancer Genomics ◽  
Predictive Performance ◽  
Training Group ◽  
Gene Expression Omnibus ◽  
Survival Prediction ◽  
Screening Process ◽  
Gene Markers ◽  
Validation Set

Multiple-omics sequencing information with high-throughput has laid a solid foundation to identify genes associated with cancer prognostic process. Multiomics information study is capable of revealing the cancer occurring and developing system according to several aspects. Currently, the prognosis of osteosarcoma is still poor, so a genetic marker is needed for predicting the clinically related overall survival result. First, Office of Cancer Genomics (OCG Target) provided RNASeq, copy amount variations information, and clinically related follow-up data. Genes associated with prognostic process and genes exhibiting copy amount difference were screened in the training group, and the mentioned genes were integrated for feature selection with least absolute shrinkage and selection operator (Lasso). Eventually, effective biomarkers received the screening process. Lastly, this study built and demonstrated one gene-associated prognosis mode according to the set of the test and gene expression omnibus validation set; 512 prognosis-related genes ( P < 0.01), 336 copies of amplified genes ( P < 0.05), and 36 copies of deleted genes ( P < 0.05) were obtained, and those genes of the mentioned genomic variants display close associations with tumor occurring and developing mechanisms. This study generated 10 genes for candidates through the integration of genomic variant genes as well as prognosis-related genes. Six typical genes (i.e. MYC, CHIC2, CCDC152, LYL1, GPR142, and MMP27) were obtained by Lasso feature selection and stepwise multivariate regression study, many of which are reported to show a relationship to tumor progressing process. The authors conducted Cox regression study for building 6-gene sign, i.e. one single prognosis-related element, in terms of cases carrying osteosarcoma. In addition, the samples were able to be risk stratified in the training group, test set, and externally validating set. The AUC of five-year survival according to the training group and validation set reached over 0.85, with superior predictive performance as opposed to the existing researches. Here, 6-gene sign was built to be new prognosis-related marking elements for assessing osteosarcoma cases’ surviving state.

scholarly journals A stemness-based eleven-gene signature correlates with the clinical outcome of hepatocellular carcinoma

BMC Cancer ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Liang Hong ◽  
Yu Zhou ◽  
Xiangbang Xie ◽  
Wanrui Wu ◽  
Changsheng Shi ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Cox Regression ◽  
Molecular Subtypes ◽  
Prognostic Significance ◽  
Predictive Performance ◽  
Gene Signature ◽  
The Cancer Genome Atlas ◽  
Cumulative Distribution ◽  
Gene Module ◽  
Expression Levels

Abstract Background Cumulative evidences have been implicated cancer stem cells in the tumor environment of hepatocellular carcinoma (HCC) cells, whereas the biological functions and prognostic significance of stemness related genes (SRGs) in HCC is still unclear. Methods Molecular subtypes were identified by cumulative distribution function (CDF) clustering on 207 prognostic SRGs. The overall survival (OS) predictive gene signature was developed, internally and externally validated based on HCC datasets including The Cancer Genome Atlas (TCGA), GEO and ICGC datasets. Hub genes were identified in molecular subtypes by protein-protein interaction (PPI) network analysis, and then enrolled for determination of prognostic genes. Univariate, LASSO and multivariate Cox regression analyses were performed to assess prognostic genes and construct the prognostic gene signature. Time-dependent receiver operating characteristic (ROC) curve, Kaplan-Meier curve and nomogram were used to assess the performance of the gene signature. Results We identified four molecular subtypes, among which the C2 subtype showed the highest SRGs expression levels and proportions of immune cells, whereas the worst OS; the C1 subtype showed the lowest SRGs expression levels and was associated with most favorable OS. Next, we identified 11 prognostic genes (CDX2, PON1, ADH4, RBP2, LCAT, GAL, LPA, CYP19A1, GAST, SST and UGT1A8) and then constructed a prognostic 11-gene module and validated its robustness in all three datasets. Moreover, by univariate and multivariate Cox regression, we confirmed the independent prognostic ability of the 11-gene module for patients with HCC. In addition, calibration analysis plots indicated the excellent predictive performance of the prognostic nomogram constructed based on the 11-gene signature. Conclusions Findings in the present study shed new light on the role of stemness related genes within HCC, and the established 11-SRG signature can be utilized as a novel prognostic marker for survival prognostication in patients with HCC.

scholarly journals Identification of Novel Biomarkers Associated With the Prognosis and Potential Pathogenesis of Breast Cancer via Integrated Bioinformatics Analysis

2021 ◽  
Vol 20 ◽  
pp. 153303382199208 ◽  
Author(s):  
Meng Wu ◽  
Qingdai Li ◽  
Hongbing Wang
Keyword(s):  
Breast Cancer ◽  
Survival Data ◽  
Expression Profiles ◽  
Cancer Prognosis ◽  
Data Sets ◽  
Expression Levels ◽  
Human Protein Atlas ◽  
Novel Biomarkers ◽  
Pathway Analyses ◽  
Cancer Tissues

Background: Breast cancer is the most commonly diagnosed malignancy and a major cause of cancer-related deaths in women globally. Identification of novel prognostic and pathogenesis biomarkers play a pivotal role in the management of the disease. Methods: Three data sets from the GEO database were used to identify differentially expressed genes (DEGs) in breast cancer. Gene Ontology (GO) enrichment and Kyoto Encyclopaedia of Genes and Genomes pathway analyses were performed to elucidate the functional roles of the DEGs. Besides, we investigated the translational and protein expression levels and survival data of the DEGs in patients with breast cancer from the Gene Expression Profiling Interactive Analysis (GEPIA), Oncomine, Human Protein Atlas, and Kaplan Meier plotter tool databases. The corresponding change in the expression level of microRNAs in the DEGs was also predicted using miRWalk and TargetScan, and the expression profiles were analyzed using OncomiR. Finally, the expression of novel DEGs were validated in Chinese breast cancer tissues by RT-qPCR. Results: A total of 46 DEGs were identified, and GO analysis revealed that these genes were mainly associated with biological processes involved in fatty acid, lipid localization, and regulation of lipid metabolism. Two novel biomarkers, ADH1A and IGSF10, and 4 other genes ( APOD, KIT, RBP4, and SFRP1) that were implicated in the prognosis and pathogenesis of breast cancer, exhibited low expression levels in breast cancer tissues. Besides, 14/25 microRNAs targeting 6 genes were first predicted to be associated with breast cancer prognosis. RT-qPCR results of ADH1A and IGSF10 expression in Chinese breast cancer tissues were consistent with the database analysis and showed significant down-regulation. Conclusion: ADH1A, IGSF10, and the 14 microRNAs were found to be potential novel biomarkers for the diagnosis, treatment, and prognosis of breast cancer.

scholarly journals Identification of key biomarkers and immune infiltration in systemic lupus erythematosus by integrated bioinformatics analysis

2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Xingwang Zhao ◽  
Longlong Zhang ◽  
Juan Wang ◽  
Min Zhang ◽  
Zhiqiang Song ◽  
...  
Keyword(s):  
Systemic Lupus Erythematosus ◽  
Lupus Erythematosus ◽  
Immune Cell ◽  
Expression Profiles ◽  
Gene Set Enrichment Analysis ◽  
Cell Infiltration ◽  
Immune Cell Infiltration ◽  
Tissue Destruction ◽  
Systemic Lupus ◽  
Ppi Networks

Abstract Background Systemic lupus erythematosus (SLE) is a multisystemic, chronic inflammatory disease characterized by destructive systemic organ involvement, which could cause the decreased functional capacity, increased morbidity and mortality. Previous studies show that SLE is characterized by autoimmune, inflammatory processes, and tissue destruction. Some seriously-ill patients could develop into lupus nephritis. However, the cause and underlying molecular events of SLE needs to be further resolved. Methods The expression profiles of GSE144390, GSE4588, GSE50772 and GSE81622 were downloaded from the Gene Expression Omnibus (GEO) database to obtain differentially expressed genes (DEGs) between SLE and healthy samples. The gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichments of DEGs were performed by metascape etc. online analyses. The protein–protein interaction (PPI) networks of the DEGs were constructed by GENEMANIA software. We performed Gene Set Enrichment Analysis (GSEA) to further understand the functions of the hub gene, Weighted gene co‐expression network analysis (WGCNA) would be utilized to build a gene co‐expression network, and the most significant module and hub genes was identified. CIBERSORT tools have facilitated the analysis of immune cell infiltration patterns of diseases. The receiver operating characteristic (ROC) analyses were conducted to explore the value of DEGs for SLE diagnosis. Results In total, 6 DEGs (IFI27, IFI44, IFI44L, IFI6, EPSTI1 and OAS1) were screened, Biological functions analysis identified key related pathways, gene modules and co‐expression networks in SLE. IFI27 may be closely correlated with the occurrence of SLE. We found that an increased infiltration of moncytes, while NK cells resting infiltrated less may be related to the occurrence of SLE. Conclusion IFI27 may be closely related pathogenesis of SLE, and represents a new candidate molecular marker of the occurrence and progression of SLE. Moreover immune cell infiltration plays important role in the progession of SLE.

scholarly journals Identification of Type II Interferon Receptors in Geese: Gene Structure, Phylogenetic Analysis, and Expression Patterns

2015 ◽  
Vol 2015 ◽  
pp. 1-14
Author(s):  
Hao Zhou ◽  
Shun Chen ◽  
Yulin Qi ◽  
Qin Zhou ◽  
Mingshu Wang ◽  
...  
Keyword(s):  
Expression Profiles ◽  
Expression Patterns ◽  
Harderian Gland ◽  
Distribution Analysis ◽  
Expression Levels ◽  
Age Related ◽  
Cecal Tonsil ◽  
Immune Tissues ◽  
First Time

Interferonγreceptor 1 (IFNGR1) and IFNGR2 are two cell membrane molecules belonging to class II cytokines, which play important roles in the IFN-mediated antiviral signaling pathway. Here, goose IFNGR1 and IFNGR2 were cloned and identified for the first time. Tissue distribution analysis revealed that relatively high levels of goose IFNγmRNA transcripts were detected in immune tissues, including the harderian gland, cecal tonsil, cecum, and thymus. Relatively high expression levels of both IFNGR1 and IFNGR2 were detected in the cecal tonsil, which implicated an important role of IFNγin the secondary immune system of geese. No specific correlation between IFNγ, IFNGR1, and IFNGR2 expression levels was observed in the same tissues of healthy geese. IFNγand its cognate receptors showed different expression profiles, although they appeared to maintain a relatively balanced state. Furthermore, the agonist R848 led to the upregulation of goose IFNγbut did not affect the expression of goose IFNGR1 or IFNGR2. In summary, trends in expression of goose IFNγand its cognate receptors showed tissue specificity, as well as an age-related dependency. These findings may help us to better understand the age-related susceptibility to pathogens in birds.

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