The role of SPI1-TYROBP-FCER1G network in oncogenesis and prognosis of osteosarcoma, and its association with immune infiltration.
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ABSTRACT:
SUBMITTER: Li J
PROVIDER: S-EPMC8790913 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundTyrosine protein tyrosine kinase binding protein (TYROBP) binds non-covalently to activated receptors on the surface of various immune cells, and mediates signal transduction and cellular activation. It is dysregulated in various malignancies, although little is known regarding its role in low-grade glioma. The aim of this study is to explore the clinicopathological significance, prognostic value and immune signature of TYROBP expression in low-grade glioma (LGG).MethodsThe differentially expressed genes (DEGs) between glioma samples and normal tissues were identified from two GEO microarray datasets using the limma package. The DEGs overlapping across both datasets were functionally annotated by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses. STRING database was used to establish the protein-protein interaction (PPI) of the DEGs. The PPI network was visualized by Cytoscape and cytoHubba, and the core module and hub genes were identified. The expression profile of TYROBP and patient survival were validated in the Oncomine, GEPIA2 and CGGA databases. The correlation between TYROBP expression and the clinicopathologic characteristics were evaluated. Gene Set Enrichment Analysis (GSEA) and single-sample GSEA (ssGSEA) were performed by R based on the LGG data from TCGA. The TIMER2.0 database was used to determine the correlation between TYROBP expression and tumor immune infiltrating cells in the LGG patients. Univariate and multivariate Cox regression analyses were performed to determine the prognostic impact of clinicopathological factors via TCGA database.ResultsSixty-two overlapping DEGs were identified in the 2 datasets, and were mainly enriched in the response to wounding, focal adhesion, GTPase activity and Parkinson disease pathways. TYROBP was identified through the PPI network and cytoHubba. TYROBP expression levels were significantly higher in the LGG tissues compared to the normal tissues, and was associated with worse prognosis and poor clinicopathological parameters. In addition, GSEA showed that TYROBP was positively correlated to neutrophil chemotaxis, macrophage activation, chemokine signaling pathway, JAK-STAT signaling pathway, and negatively associated with gamma aminobutyric acid signaling pathway, neurotransmitter transport, neuroactive ligand receptor intersection etc. TIMER2.0 and ssGSEA showed that TYROBP expression was significantly associated with the infiltration of neutrophils, macrophages, myeloid dendritic cells and monocytes. The infiltration of the M2 phenotype macrophages, cancer-associated fibroblasts and myeloid dendritic cells correlated to worse prognosis in LGG patients. Finally, multivariate analysis showed that elevated TYROBP expression is an independent risk factor for LGG.ConclusionTYROBP is dysregulated in LGG and correlates with immune infiltration. It is a potential therapeutic target and prognostic marker for LGG.
| S-EPMC8220692 | biostudies-literature
Project description:We established a relationship among the immune-related genes, tumor-infiltrating immune cells (TIICs), and immune checkpoints in patients with osteosarcoma. The gene expression data for osteosarcoma were downloaded from UCSC Xena and GEO database. Immune-related differentially expressed genes (DEGs) were detected to calculate the risk score. "Estimate" was used for immune infiltrating estimation and "xCell" was used to obtain 64 immune cell subtypes. Furthermore, the relationship among the risk scores, immune cell subtypes, and immune checkpoints was evaluated. The three immune-related genes (TYROBP, TLR4, and ITGAM) were selected to establish a risk scoring system based on their integrated prognostic relevance. The GSEA results for the Hallmark and KEGG pathways revealed that the low-risk score group exhibited the most gene sets that were related to immune-related pathways. The risk score significantly correlated with the xCell score of macrophages, M1 macrophages, and M2 macrophages, which significantly affected the prognosis of osteosarcoma. Thus, patients with low-risk scores showed better results with the immune checkpoints inhibitor therapy. A three immune-related, gene-based risk model can regulate macrophage activation and predict the treatment outcomes the survival rate in osteosarcoma.
| S-EPMC8481262 | biostudies-literature
Project description: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.
| S-EPMC5715520 | biostudies-literature
Project description:Background:Inflammatory response took part in the progression of tumor and was regarded as the hallmark of cancer. However, the prognostic relationship between osteosarcoma and inflammatory response-associated genes (IRGs) was unclear. This research aimed to explore the correlations between osteosarcoma prognosis and IRG signature. Methods:The inflammatory response-associated differentially expressed messenger RNAs (DEmRNAs) were screened out through Gene Expression Omnibus (GEO) and Molecular Signature Database (MSigDB) databases. Univariate and multivariate cox regression analyses were utilized to construct the IRG signature. The prognostic value of signature was investigated through Kaplan-Meier (KM) survival curve and nomogram. DEmRNAs among high and low inflammatory response-associated risks were identified and functional enrichment analyses were conducted. ESTIMATE, CIBERSORT and single-sample gene set enrichment analyses (ssGSEA) were implied to reveal the alterations in immune infiltration. All the above results were validated in Target database. The expression of IRGs was also validated in different cell lines by quantitative real-time PCR (qRT-PCR) and osteosarcoma patient samples by immunohistochemistry. Results:The IRG signature that consisted of two genes (MYC, CLEC5A) was established. In training and validation datasets, patients with lower risk scores survived longer and the IRG signature was confirmed as the independent prognostic factor in osteosarcoma. The nomogram was constructed and the calibration curves demonstrated the reliability of this model. Functional analysis of risk score-associated DEmRNAs indicated that immune-related pathways and functions were significantly enriched. ssGSEA revealed that 14 immune cells and 11 immune functions were significantly dysregulated. The qRT-PCR results indicated IRGs were significantly differently expressed in osteosarcoma and osteoblast cell lines. The immunohistochemistry analyses of patients' samples revealed the same result. Conclusion:The novel osteosarcoma inflammatory response-associated prognostic signature was established and validated in this study. This model could serve as the biomarker and therapeutic target for osteosarcoma in the future.
| S-EPMC8668229 | biostudies-literature
Project description:Pancreatic adenocarcinoma (PAAD) is one of the deadliest malignancies. Aging is described as the degeneration of physiological function, which is complexly correlated with cancer. It is significant to explore the influences of aging-related genes (ARGs) on PAAD. Based on The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) datasets, we used univariate Cox regression analysis and acquired eight differentially expressed ARGs with prognostic values. Two molecular subtypes were identified based on these ARGs to depict PAAD patients' overall survival (OS) and immune microenvironments preliminarily. Cluster 1 had a poor OS as well as a worse immune microenvironment. Through least absolute shrinkage and selection operator (LASSO) regression analysis, we constructed a seven-ARG risk signature based on the TCGA dataset and verified it in Gene Expression Omnibus (GEO) and International Cancer Genome Consortium (ICGC) to predict the prognoses, immune microenvironments, signal pathways, tumor mutations, and drug sensitivity of PAAD patients. The high-risk group possessed an unfavorable OS compared with that of the low-risk group. We also verified the independence and clinical availability of the risk signature by Cox regression analyses and the establishment of a nomogram, respectively. The higher risk score was associated with several clinical factors such as higher grade and advanced tumor stage as well as lower immunoscore and cluster 1. The negative associations of risk scores with immune, stroma, and estimate scores proved the terrible immune microenvironment in the high-risk group. Relationships between risk score and immune checkpoint gene expression as well as signal pathways provided several therapeutic targets. PAAD patients in the low-risk group possessed lower tumor mutations as well as a higher susceptibility to axitinib and vorinostat. The high-risk group bore a higher TMB and cisplatin and dasatinib may be better options. We used immunohistochemistry and qPCR to confirm the expression of key ARGs with their influences on OS. In conclusion, we identified two ARG-mediated molecular subtypes and a novel seven-ARG risk signature to predict prognoses, immune microenvironments, signal pathways, tumor mutations, and drug sensitivity of PAAD patients.
| S-EPMC9393218 | biostudies-literature
Project description:Background: The function of olfactomedin-like 2B (OLFML2B), as a member of the olfactomedin domain-containing protein family, remains ambiguous, especially in tumors. The current study explores the possible correlation between OLFML2B, prognosis, and immune infiltration in pan-cancer. Methods: We applied a number of bioinformatics techniques to probe the prospective function of OLFML2B, consisting of its association with prognosis, clinicopathology, alteration, GSEA, tumor microenvironment (TME), immune-associated genes, immune infiltration, tumor mutational burden (TMB), microsatellite instability (MSI), and drug sensitivity in several cancer types. qPCR and immunohistochemistry were used to identify OLFML2B expression in LIHC cell lines and liver cancer tissues. Results: We discovered that OLFML2B was overexpressed in 14 cancers and positively related to several cancer type prognoses. The expression of OLFML2B was further validated in the LIHC cell lines. OLFML2B expression was bound up with TMB in 13 cancers, MSI in 10 cancers, and TME in almost all cancers. Furthermore, OLFML2B was highly co-expressed with genes encoding immune activators and immune suppressors. We further found that OLFML2B played a role in infiltrating different types of immune cells, such as macrophages and cancer-associated fibroblasts. OLFML2B may influence various cancer and immune-related pathways, such as the PI3K-Akt signaling pathway, ECM-receptor interaction, focal adhesion, and leukocyte transendothelial migration. In addition, OLFML2B may increase drug resistance of binimetinib, cobimentinib, and trametinib. Conclusion: Our outcomes reveal that OLFML2B may act as a prognostic marker and a potential target in immunotherapy for diverse tumors due to its oncogenesis function and immune infiltration.
| S-EPMC9298975 | biostudies-literature
Project description:BackgroundPapillary thyroid carcinoma (PTC) is the most common thyroid cancer. While many patients survive, a portion of PTC cases display high aggressiveness and even develop into refractory differentiated thyroid carcinoma. This may be alleviated by developing a novel model to predict the risk of recurrence. Ferroptosis is an iron-dependent form of regulated cell death (RCD) driven by lethal accumulation of lipid peroxides, is regulated by a set of genes and shows a variety of metabolic changes. To elucidate whether ferroptosis occurs in PTC, we analyse the gene expression profiles of the disease and established a new model for the correlation.MethodsThe thyroid carcinoma (THCA) datasets were downloaded from The Cancer Genome Atlas (TCGA), UCSC Xena and MisgDB, and included 502 tumour samples and 56 normal samples. A total of 60 ferroptosis related genes were summarised from MisgDB database. Gene set enrichment analysis (GSEA) and Gene set variation analysis (GSVA) were used to analyse pathways potentially involving PTC subtypes. Single sample GSEA (ssGSEA) algorithm was used to analyse the proportion of 28 types of immune cells in the tumour immune infiltration microenvironment in THCA and the hclust algorithm was used to conduct immune typing according to the proportion of immune cells. Spearman correlation analysis was performed on the ferroptosis gene expression and the correlation between immune infiltrating cells proportion. We established the WGCNA to identify genes modules that are highly correlated with the microenvironment of immune invasion. DEseq2 algorithm was further used for differential analysis of sequencing data to analyse the functions and pathways potentially involving hub genes. GO and KEGG enrichment analysis was performed using Clusterprofiler to explore the clinical efficacy of hub genes. Univariate Cox analysis was performed for hub genes combined with clinical prognostic data, and the results was included for lasso regression and constructed the risk regression model. ROC curve and survival curve were used for evaluating the model. Univariate Cox analysis and multivariate Cox analysis were performed in combination with the clinical data of THCA and the risk score value, the clinical efficacy of the model was further evaluated.ResultsWe identify two subtypes in PTC based on the expression of ferroptosis related genes, with the proportion of cluster 1 significantly higher than cluster 2 in ferroptosis signature genes that are positively associated. The mutations of Braf and Nras are detected as the major mutations of cluster 1 and 2, respectively. Subsequent analyses of TME immune cells infiltration indicated cluster 1 is remarkably richer than cluster 2. The risk score of THCA is in good performance evaluated by ROC curve and survival curve, in conjunction with univariate Cox analysis and multivariate Cox analysis results based on the clinical data shows that the risk score of the proposed model could be used as an independent prognostic indicator to predict the prognosis of patients with papillary thyroid cancer.ConclusionsOur study finds seven crucial genes, including Ac008063.2, Apoe, Bcl3, Acap3, Alox5ap, Atxn2l and B2m, and regulation of apoptosis by parathyroid hormone-related proteins significantly associated with ferroptosis and immune cells in PTC, and we construct the risk score model which can be used as an independent prognostic index to predict the prognosis of patients with PTC.
| S-EPMC8314640 | biostudies-literature
Project description:BackgroundOsteosarcoma is a highly aggressive bone tumor that most commonly affects children and adolescents. Treatment and outcomes for osteosarcoma have remained unchanged over the past 30 years. The relationship between osteosarcoma and the immune microenvironment may represent a key to its undoing.MethodsWe calculated the immune and stromal scores of osteosarcoma cases from the Target database using the ESTIMATE algorithm. Then we used the CIBERSORT algorithm to explore the tumor microenvironment and analyze immune infiltration of osteosarcoma. Differentially expressed genes (DEGs) were identified based on immune scores and stromal scores. Search Tool for the Retrieval of Interacting Genes Database (STRING) was utilized to assess protein-protein interaction (PPI) information, and Molecular Complex Detection (MCODE) plugin was used to screen hub modules of PPI network in Cytoscape. The prognostic value of the gene signature was validated in an independent GSE39058 cohort. Gene set enrichment analysis (GSEA) was performed to study the hub genes in signaling pathways.ResultsFrom 83 samples of osteosarcoma obtained from the Target dataset, 137 DEGs were identified, including 134 upregulated genes and three downregulated genes. Functional enrichment analysis and PPI networks demonstrated that these genes were mainly involved in neutrophil degranulation and neutrophil activation involved in immune response, and participated in neuroactive ligand-receptor interaction and staphylococcus aureus infection.ConclusionsOur study established an immune-related gene signature to predict outcomes of osteosarcoma, which may be important targets for individual treatment.
| S-EPMC7075043 | biostudies-literature
Project description: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.
| S-EPMC6959079 | biostudies-literature
Project description:Osteosarcoma patient survival has remained stagnant for 30 years. Novel therapeutic approaches are needed to improve outcomes. We examined the expression of Programmed Death Ligand 1 (PD-L1) and defined the tumor immune microenvironment to assess the prognostic utility in osteosarcoma. PD-L1 expression in osteosarcoma was examined in two patient cohorts using immunohistochemistry (IHC) (n?=?48, n?=?59) and expression was validated using quantitative real time PCR (n?=?21) and western blotting (n?=?9). IHC was used to determine the presence of tumor infiltrating lymphocytes and antigen-presenting cells (APCs) in the tumor. Expression of PD-L1 was correlated with immune cell infiltration and event-free-survival (EFS). The 25% of primary osteosarcoma tumors that express PD-L1 were more likely to contain cells that express PD-1 than PD-L1 negative tumors (91.7% vs 47.2%, p?=?0.002). Expression of PD-L1 was significantly associated with the presence of T cells, dendritic cells, and natural killer cells. Although all immune cell types examined were present in osteosarcoma samples, only infiltration by dendritic cells (28.3% vs. 83.9%, p?=?0.001) and macrophages (45.5% vs. 84.4%, p?=?0.031) were associated with worse five-year-EFS. PD-L1 expression was significantly associated with poorer five-year-EFS (25.0%. vs. 69.4%, p?=?0.014). Further studies in osteosarcoma are needed to determine if targeting the PD-L1:PD-1 axis improves survival.
| S-EPMC4960483 | biostudies-other