Sp1 induced gene TIMP1 is related to immune cell infiltration in glioblastoma.
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ABSTRACT:
SUBMITTER: Liu L
PROVIDER: S-EPMC9249770 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Glioblastoma (GBM) is a malignant intracranial tumour with the highest proportion and lethality. It is characterized by invasiveness and heterogeneity. However, the currently available therapies are not curative. As an essential environmental cue that maintains glioma stem cells, hypoxia is considered the cause of tumour resistance to chemotherapy and radiation. Growing evidence shows that immunotherapy focusing on the tumour microenvironment is an effective treatment for GBM; however, the current clinicopathological features cannot predict the response to immunotherapy and provide accurate guidance for immunotherapy. Based on the ESTIMATE algorithm, GBM cases of The Cancer Genome Atlas (TCGA) data set were classified into high- and low-immune/stromal score groups, and a four-gene tumour environment-related model was constructed. This model exhibited good efficiency at forecasting short- and long-term prognosis and could also act as an independent prognostic biomarker. Additionally, this model and four of its genes (CLECL5A, SERPING1, CHI3L1 and C1R) were found to be associated with immune cell infiltration, and further study demonstrated that these four genes might drive the hypoxic phenotype of perinecrotic GBM, which affects hypoxia-induced glioma stemness. Therefore, these might be important candidates for immunotherapy of GBM and deserve further exploration.
| S-EPMC7701576 | biostudies-literature
Project description:Immune cell infiltration varies widely between different glioblastomas (GBMs). The underlying mechanism, however, remains unknown. Here we show that TGF-beta regulates proliferation, migration, and tumorigenicity of mesenchymal GBM cancer stem cells (CSCs) in vivo and in vitro. In contrast, proneural GBM CSCs resisted TGF-beta due to TGFR2 deficiency. In vivo, a substantially increased infiltration of immune cells was observed in mesenchymal GBMs, while immune infiltrates were rare in proneural GBMs. On a functional level, proneural CSC lines caused a significantly stronger TGF-beta-dependent suppression of NKG2D expression on CD8(+) T and NK cells in vitro providing a mechanistic explanation for the reduced immune infiltration of proneural GBMs. Thus, the molecular subtype of CSCs TGF-beta-dependently contributes to the degree of immune infiltration.
| S-EPMC3464079 | biostudies-other
Project description:BackgroundGBM, also known as glioblastoma multiforme, is the most prevalent and lethal type of brain cancer. The cell proliferation, invasion, angiogenesis, and treatment of gliomas are significantly influenced by oxidative stress. Nevertheless, the connection between ORGs and GBM remains poorly comprehended. The objective of this research is to investigate the predictive significance of ORGs in GBM and their potential as targets for therapy.MethodsWe identified differentially expressed genes in glioma and ORGs from public databases. A risk model was established using LASSO regression and Cox analysis, and its performance was evaluated with ROC curves. We then performed consistent cluster analysis on the model, examining its correlation with immunity and drug response. Additionally, PCR, WB and IHC were employed to validate key genes within the prognostic model.Results9 ORGs (H6PD, BMP2, SPP1, HADHA, SLC25A20, TXNIP, ACTA1, CCND1, EEF1A1) were selected via differential expression analysis, LASSO and Cox analysis, and incorporated into the risk model with high predictive accuracy. Enrichment analyses using GSVA and GSEA focused predominantly on malignancy-associated pathways. Subtype C of GBM had the best prognosis with the lowest risk score. Furthermore, the model exhibited a strong correlation with the infiltration of immune cells and had the capability to pinpoint potential targeted therapeutic medications for GBM. Ultimately, we selected HADHA for in vitro validation. The findings indicated that GBM exhibits a significant upregulation of HADHA. Knockdown of HADHA inhibited glioma cell proliferation and diminished their migration and invasion capacities and influenced the tumor growth in vivo.ConclusionThe risk model, built upon 9 ORGs and the identification of GBM subtypes, suggests that ORGs have a broad application prospect in the clinical immunotherapy and targeted drug treatment of GBM. HADHA significantly influences the development of gliomas, both in vivo and in vitro.
| S-EPMC10986133 | biostudies-literature
Project description:To explore novel therapeutic targets, develop a gene signature and construct a prognostic nomogram of bladder cancer (BCa). Transcriptome data and clinical traits of BCa were downloaded from UCSC Xena database and Gene Expression Omnibus (GEO) database. We then used the method of Single sample Gene Set Enrichment analysis (ssGSEA) to calculate the infiltration abundances of 24 immune cells in eligible BCa samples. By weighted correlation network analysis (WGCNA), we identified turquoise module with strong and significant association with the infiltration abundance of immune cells which were associated with overall survival of BCa patients. Subsequently, we developed an immune cell infiltration-related gene signature based on the module genes (MGs) and immune-related genes (IRGs) from the Immunology Database and Analysis Portal (ImmPort). Then, we tested the prognostic power and performance of the signature in both discovery and external validation datasets. A nomogram integrated with signature and clinical features were ultimately constructed and tested. Five prognostic immune cell infiltration-related module genes (PIRMGs), namely FPR1, CIITA, KLRC1, TNFRSF6B, and WFIKKN1, were identified and used for gene signature development. And the signature showed independent and stable prognosis predictive power. Ultimately, a nomogram consisting of signature, age and tumor stage was constructed, and it showed good and stable predictive ability on prognosis. Our prognostic signature and nomogram provided prognostic indicators and potential immunotherapeutic targets for BCa. Further researches are needed to verify the clinical effectiveness of this nomogram and these biomarkers.
| S-EPMC8370985 | biostudies-literature
Project description:Glioblastoma (GBM) is the most malignant and prevalent primary brain tumor. In this study, weighted gene coexpression network analysis (WGCNA) was performed to analyze RNA binding protein (RBP) expression data from The Cancer Genome Atlas (TCGA) for the IDH-wild type GBM cohort. The CIBERSORT algorithm quantified the cellular composition of immune cells and was used to identify key modules associated with CD8+ T cell infiltration. Coexpression networks analysis and protein-protein interaction (PPI) network analysis was used to filter out central RBP genes. Eleven RBP genes, including MYEF2, MAPT, NOVA1, MAP2, TUBB2B, CDH10, TTYH1, PTPRZ1, SOX2, NOVA2 and SCG3, were identified as candidate CD8+ T cell infiltration-associated central genes. A Cox proportional hazards regression model and Kaplan-Meier analysis were applied to identify candidate biomarkers. MYEF2 was selected as a prognostic biomarker based on the results of prognostic analysis. Flow Cytometric Analysis indicated that MYEF2 expression was negatively correlated with dysfunctional CD8+ T cell markers. Kaplan-Meier survival analysis (based on IHC staining) revealed that GBM patients with elevated MYEF2 expression have a better prognosis. Knockdown of MYEF2 in GBM cells via in vitro assays was observed to promote cell proliferation and migration. Our study suggests that MYEF2 expression negatively correlates with T cell exhaustion and tumor progression, rendering it a potentially valuable prognostic biomarker for GBM.
| S-EPMC10457068 | biostudies-literature
Project description:Clear cell renal cell carcinoma (ccRCC) is one of the most common tumors in the urinary system. Ferroptosis plays a vital role in ccRCC development and progression. We did an update of ferroptosis-related multigene expression signature for individualized prognosis prediction in patients with ccRCC. Differentially expressed ferroptosis-related genes in ccRCC and normal samples were screened using The Cancer Genome Atlas. Univariate and multivariate Cox regression analyses and machine learning methods were employed to identify optimal prognosis-related genes. CARS1, CD44, FANCD2, HMGCR, NCOA4, SLC7A11, and ACACA were selected to establish a prognostic risk score model. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway analyses revealed that these genes were mainly enriched in immune-related pathways; single-sample Gene Set Enrichment Analysis revealed several immune cells potentially related to ferroptosis. Kaplan-Meier survival analysis demonstrated that patients with high-risk scores had significantly poor overall survival (log-rank P = 7.815 × 10-11). The ferroptosis signature was identified as an independent prognostic factor. Finally, a prognostic nomogram, including the ferroptosis signature, age, histological grade, and stage status, was constructed. Analysis of The Cancer Genome Atlas-based calibration plots, C-index, and decision curve indicated the excellent predictive performance of the nomogram. The ferroptosis-related seven-gene risk score model is useful as a prognostic biomarker and suggests therapeutic targets for ccRCC. The prognostic nomogram may assist in individualized survival prediction and improve treatment strategies.
| S-EPMC8220217 | biostudies-literature
Project description:BackgroundEfferocytosis is a biological process in which phagocytes remove apoptotic cells and vesicles from tissues. This process is initiated by the release of inflammatory mediators from apoptotic cells and plays a crucial role in resolving inflammation. The signals associated with efferocytosis have been found to regulate the inflammatory response and the tumor microenvironment (TME), which promotes the immune escape of tumor cells. However, the role of efferocytosis in glioblastoma multiforme (GBM) is not well understood and requires further investigation.MethodsIn this study, we conducted a comprehensive analysis of 22 efferocytosis-related genes (ERGs) by searching for studies related to efferocytosis. Using bulk RNA-Seq and single-cell sequencing data, we analyzed the expression and mutational characteristics of these ERGs. By using an unsupervised clustering algorithm, we obtained ERG clusters from 549 GBM patients and evaluated the immune infiltration characteristics of each cluster. We then identified differential genes (DEGs) in the two ERG clusters and classified GBM patients into different gene clusters using univariate cox analysis and unsupervised clustering algorithms. Finally, we utilized the Boruta algorithm to screen for prognostic genes and reduce dimensionality, and the PCA algorithm was applied to create a novel efferocytosis-related scoring system.ResultsDifferential expression of ERGs in glioma cell lines and normal cells was analyzed by rt-PCR. Cell function experiments, on the other hand, validated TIMD4 as a tumor risk factor in GBM. We found that different ERG clusters and gene clusters have distinct prognostic and immune infiltration profiles. The ERG signature we developed provides insight into the tumor microenvironment of GBM. Patients with lower ERG scores have a better survival rate and a higher likelihood of benefiting from immunotherapy.ConclusionsOur novel efferocytosis-related signature has the potential to be used in clinical practice for risk stratification of GBM patients and for selecting individuals who are likely to respond to immunotherapy. This can help clinicians design appropriate targeted therapies before initiating clinical treatment.
| S-EPMC10583324 | biostudies-literature
Project description:Melanoma is a life-threatening form of skin cancer with an elevated risk of metastasis and high mortality rates. The prognosis and clinical outcomes of cancer immunotherapy in melanoma patients are influenced by immune cell infiltration in the tumor microenvironment (TME) and the expression of genetic factors. Despite reports suggesting that immune-classification may have a better prediction of prognosis compared to the American Joint Committee on Cancer/Union for International Cancer Control (AJCC/UICC) TNM-classification, the definition of Immunoscore in melanoma is becoming a difficult challenge. In this study, we established and verified a 7-gene prognostic signature. Melanoma patients from the Cancer Genome Atlas (TCGA) were separated into a low-risk group and a high-risk group using the median risk score. Receiver operating characteristic (ROC) analysis for overall survival (OS) showed that the area under the curve (AUC) was 0.701 for 1 year, 0.726 for 3 years, and 0.745 for 5 years, respectively. Moreover, a nomogram was constructed as a practical prognostic tool, and the AUC was 0.829 for 3 years, and 0.803 for 5 years, respectively. Furthermore, we validated the above results in two datasets from the Gene Expression Omnibus (GEO) database and the relationship between 7-gene prognostic signature and immune infiltration estimated.
| S-EPMC7906183 | biostudies-literature
Project description:Heterotopic ossification (HO) is one of the most intractable disorders following musculoskeletal injury and is characterized by the ectopic presence of bone tissue in the soft tissue leading to severe loss of function in the extremities. Recent studies have indicated that immune cell infiltration and inflammation are involved in aberrant bone formation. In this study, we found increased monocyte/macrophage and mast cell accumulation during early HO progression. Macrophage depletion by clodronate liposomes and mast cell stabilization by cromolyn sodium significantly impeded HO formation. Therefore, we proposed that the dietary phytochemical quercetin could also suppress immune cell recruitment and related inflammatory responses to prevent HO. As expected, quercetin inhibited the monocyte-to-macrophage transition, macrophage polarization, and mast cell activation in vitro in a dose-dependent manner. Using a murine burn/tenotomy model, we also demonstrated that quercetin attenuated inflammatory responses and HO in vivo. Furthermore, elevated SIRT1 and decreased acetylated NFκB p65 expression were responsible for the mechanism of quercetin, and the beneficial effects of quercetin were reversed by the SIRT1 antagonist EX527 and mimicked by the SIRT agonist SRT1720. The findings in this study suggest that targeting monocyte/macrophage and mast cell activities may represent an attractive approach for therapeutic intervention of HO and that quercetin may serve as a promising therapeutic candidate for the treatment of trauma-induced HO by modulating SIRT1/NFκB signaling.
| S-EPMC8173182 | biostudies-literature
Project description:BackgroundTo investigate the potential role of immune-related genes (IRGs) and immune cells in myocardial infarction (MI) and establish a nomogram model for diagnosing myocardial infarction.MethodsRaw and processed gene expression profiling datasets were archived from the Gene Expression Omnibus (GEO) database. Differentially expressed immune-related genes (DIRGs), which were screened out by four machine learning algorithms-partial least squares (PLS), random forest model (RF), k-nearest neighbor (KNN), and support vector machine model (SVM) were used in the diagnosis of MI.ResultsThe six key DIRGs (PTGER2, LGR6, IL17B, IL13RA1, CCL4, and ADM) were identified by the intersection of the minimal root mean square error (RMSE) of four machine learning algorithms, which were screened out to establish the nomogram model to predict the incidence of MI by using the rms package. The nomogram model exhibited the highest predictive accuracy and better potential clinical utility. The relative distribution of 22 types of immune cells was evaluated using cell type identification, which was done by estimating relative subsets of RNA transcripts (CIBERSORT) algorithm. The distribution of four types of immune cells, such as plasma cells, T cells follicular helper, Mast cells resting, and neutrophils, was significantly upregulated in MI, while five types of immune cell dispersion, T cells CD4 naive, macrophages M1, macrophages M2, dendritic cells resting, and mast cells activated in MI patients, were significantly downregulated in MI.ConclusionThis study demonstrated that IRGs were correlated with MI, suggesting that immune cells may be potential therapeutic targets of immunotherapy in MI.
| S-EPMC10052851 | biostudies-literature