Dataset Information

Single-cell and bulk sequencing analyses reveal the immune suppressive role of PTPN6 in glioblastoma.

ABSTRACT: Glioblastoma (GBM) is a highly malignant brain cancer with a poor prognosis despite standard treatments. This investigation aimed to explore the feasibility of PTPN6 to combat GBM with immunotherapy. Our study employed a comprehensive analysis of publicly available datasets and functional experiments to assess PTPN6 gene expression, prognostic value, and related immune characteristics in glioma. We evaluated the influence of PTPN6 expression on CD8+ T cell exhaustion, immune suppression, and tumor growth in human GBM samples and mouse models. Our findings demonstrated that PTPN6 overexpression played an oncogenic role in GBM and was associated with advanced tumor grades and unfavorable clinical outcomes. In human GBM samples, PTPN6 upregulation showed a strong association with immunosuppressive formation and CD8+ T cell dysfunction, whereas, in mice, it hindered CD8+ T cell infiltration. Moreover, PTPN6 facilitated cell cycle progression, inhibited apoptosis, and promoted glioma cell proliferation, tumor growth, and colony formation in mice. The outcomes of our study indicate that PTPN6 is a promising immunotherapeutic target for the treatment of GBM. Inhibition of PTPN6 could enhance CD8+ T cell infiltration and improve antitumor immune response, thus leading to better clinical outcomes for GBM patients.

SUBMITTER: Zhang X

PROVIDER: S-EPMC10564408 | biostudies-literature | 2023 Sep

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Single-cell and bulk sequencing analyses reveal the immune suppressive role of PTPN6 in glioblastoma.

Zhang Xiaonan X Chen Jie J Zhang Ming M Liu Saisai S Wang Tao T Wu Tianyu T Li Baiqing B Zhao Shidi S Wang Hongtao H Li Li L Wang Chun C Huang Li L

Aging 20230921 18

Glioblastoma (GBM) is a highly malignant brain cancer with a poor prognosis despite standard treatments. This investigation aimed to explore the feasibility of PTPN6 to combat GBM with immunotherapy. Our study employed a comprehensive analysis of publicly available datasets and functional experiments to assess PTPN6 gene expression, prognostic value, and related immune characteristics in glioma. We evaluated the influence of PTPN6 expression on CD8+ T cell exhaustion, immune suppression, and tum ...[more]

PMID: 37737713

Similar Datasets

Project description:BackgroundPostmenopausal osteoporosis (PMOP) represents as a significant health concern, particularly as the population ages. Currently, there is a paucity of comprehensive descriptions regarding the immunoregulatory mechanisms and early diagnostic biomarkers associated with PMOP. This study aims to examine immune-related differentially expressed genes (IR-DEGs) in the peripheral blood mononuclear cells of PMOP patients to identify immunological patterns and diagnostic biomarkers.MethodsThe GSE56815 dataset from the Gene Expression Omnibus (GEO) database was used as the training group, while the GSE2208 dataset served as the validation group. Initially, differential expression analysis was conducted after data integration to identify IR-DEGs in the peripheral blood mononuclear cells of PMOP. Subsequently, feature selection of these IR-DEGs was performed using RF, SVM-RFE, and LASSO regression models. Additionally, the expression of IR-DEGs in distinct bone marrow cell subtypes was analyzed using single-cell RNA sequencing (scRNA-seq) datasets, allowing the identification of cellular communication patterns within various cell subgroups. Finally, molecular subtypes and diagnostic models for PMOP were constructed based on these selected IR-DEGs. Furthermore, the expression levels of characteristic IR-DEGs were examined in rat osteoporosis (OP) models.ResultsUsing machine learning, six IR-DEGs (JUN, HMOX1, CYSLTR2, TNFSF8, IL1R2, and SSTR5) were identified. Subsequently, two molecular subtypes of PMOP (subtype 1 and subtype 2) were established, with subtype 1 exhibiting a higher proportion of M1 macrophage infiltration. Analysis of the scRNA-seq dataset revealed 11 distinct cell clusters. It was noted that JUN was significantly overexpressed in M1 macrophages, while HMOX1 showed a marked elevation in endothelial cells and M2 macrophages. Cell communication results suggested that the PMOP microenvironment features increased interactions among M2 macrophages, CD8+ T cells, Tregs, and fibroblasts. The diagnostic model based on these six IR-DEGs demonstrated excellent diagnostic performance (AUC = 0.927). In the OP rat model, the expression of IL1R2 and TNFSF8 were significantly elevated.ConclusionJUN, HMOX1, CYSLTR2, TNFSF8, IL1R2, and SSTR5 may serve as promising molecular targets for diagnosing and subtyping patients with PMOP. These results offer novel perspectives on the early diagnosis of PMOP and the advancement of personalized immune-based therapies.

Project description:Background: Osteosarcoma (OS) is a kind of solid tumor with high heterogeneity at tumor microenvironment (TME), genome and transcriptome level. In view of the regulatory effect of metabolism on TME, this study was based on four metabolic models to explore the intertumoral heterogeneity of OS at the RNA sequencing (RNA-seq) level and the intratumoral heterogeneity of OS at the bulk RNA-seq and single cell RNA-seq (scRNA-seq) level. Methods: The GSVA package was used for single-sample gene set enrichment analysis (ssGSEA) analysis to obtain a glycolysis, pentose phosphate pathway (PPP), fatty acid oxidation (FAO) and glutaminolysis gene sets score. ConsensusClusterPlus was employed to cluster OS samples downloaded from the Target database. The scRNA-seq and bulk RNA-seq data of immune cells from GSE162454 dataset were analyzed to identify the subsets and types of immune cells in OS. Malignant cells and non-malignant cells were distinguished by large-scale chromosomal copy number variation. The correlations of metabolic molecular subtypes and immune cell types with four metabolic patterns, hypoxia and angiogenesis were determined by Pearson correlation analysis. Results: Two metabolism-related molecular subtypes of OS, cluster 1 and cluster 2, were identified. Cluster 2 was associated with poor prognosis of OS, active glycolysis, FAO, glutaminolysis, and bad TME. The identified 28608 immune cells were divided into 15 separate clusters covering 6 types of immune cells. The enrichment scores of 5 kinds of immune cells in cluster-1 and cluster-2 were significantly different. And five kinds of immune cells were significantly correlated with four metabolic modes, hypoxia and angiogenesis. Of the 28,608 immune cells, 7617 were malignant cells. The four metabolic patterns of malignant cells were significantly positively correlated with hypoxia and negatively correlated with angiogenesis. Conclusion: We used RNA-seq to reveal two molecular subtypes of OS with prognosis, metabolic pattern and TME, and determined the composition and metabolic heterogeneity of immune cells in OS tumor by bulk RNA-seq and single-cell RNA-seq.

Dataset Information

Single-cell and bulk sequencing analyses reveal the immune suppressive role of PTPN6 in glioblastoma.

Publications

Single-cell and bulk sequencing analyses reveal the immune suppressive role of PTPN6 in glioblastoma.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets