Ferroptosis-Related Gene Contributes to Immunity, Stemness and Predicts Prognosis in Glioblastoma Multiforme.
Ontology highlight
ABSTRACT:
SUBMITTER: Dong J
PROVIDER: S-EPMC8960280 | biostudies-literature | 2022
REPOSITORIES: biostudies-literature
Publications
Ferroptosis-Related Gene Contributes to Immunity, Stemness and Predicts Prognosis in Glioblastoma Multiforme.
Dong Jiawei J Zhao Hongtao H Wang Fang F Jin Jiaqi J Ji Hang H Yan Xiuwei X Wang Nan N Zhang Jiheng J Hu Shaoshan S
Frontiers in neurology 20220310
Ferroptosis, a recently discovered regulated programmed cell death, is associated with tumorigenesis and progression in glioblastoma. Based on widely recognized ferroptosis-related genes (FRGs), the regulation of ferroptosis patterns and corresponding characteristics of immune infiltration of 516 GBM samples with GSE13041, TCGA-GBM, and CGGA-325 were comprehensively analyzed. Here, we revealed the expression, mutations, and CNV of FRGs in GBM. We identified three distinct regulation patterns of ...[more]
Similar Datasets
Project description:BackgroundGlioblastoma multiforme (GBM) is a common and highly aggressive brain tumor with a poor prognosis. However, the prognostic value of ferroptosis-related genes (FRGs) and their classification remains insufficiently studied.ObjectiveThis study aims to explore the significance of ferroptosis classification and its risk model in GBM using multi-omics approaches and to evaluate its potential in prognostic assessment.MethodsFerroptosis-related genes (FRGs) were retrieved from databases such as FerrDB. The TCGA-GBM and CGGA-GBM datasets were used as training and testing cohorts, respectively. Univariate Cox regression and LASSO regression analyses were performed to establish a risk model comprising five genes (OSMR, G0S2, IGFBP6, IGHG2, FMOD). A Meta-analysis of integrated TCGA and GTEx data was conducted to examine the differential expression of these genes between GBM and normal tissues. Key gene protein expression differences were analyzed using CPTAC and HPA databases. Single-cell RNA sequencing (scRNA-seq) analysis was employed to explore the cell type-specific distribution of these genes.ResultsThe five-gene risk model demonstrated significant prognostic value in GBM. Meta-analysis revealed distinct expression patterns of the identified genes between GBM and normal tissues. Protein expression analysis confirmed these differences. scRNA-seq analysis highlighted the diverse distribution of these genes across different cell types, offering insights into their biological roles.ConclusionThe ferroptosis-based risk model provides valuable prognostic insights into GBM and highlights potential therapeutic targets, emphasizing the biological significance of ferroptosis-related genes in tumor progression.
| S-EPMC11735466 | biostudies-literature
Project description:Glioblastoma multiforme (GBM) is the most malignant glioma with a high death rate. N6-methyladenosine (m6A) RNA methylation plays an increasingly important role in tumors. The current study aimed to determine the function of the regulators of m6A RNA methylation in GBM. We evaluated the difference, interaction, and correlation of these regulators with TCGA database. HNRNPC, WTAP, YTHDF2 and, YTHDF1 were significantly upregulated in GBM. To explore the expression characteristics of regulators in GBM, we defined two subgroups through consensus cluster. HNRNPC, WTAP, and YTHDF2 were significantly upregulated in the cluster2 which had a good overall survival (OS). To investigate the prognostic value of regulators, we used lasso cox regression algorithm to screen an independent prognostic risk characteristic based on the expression of HNRNPC, ZC3H13, and YTHDF2. The prognostic feature between the low and high-risk groups was significantly different (P < 0.05), which could predict significance of prognosis (area under the curve (AUC) = 0.819). Moreover, we used western blot, RT-PCR, and immunohistochemical staining to verify the expression of HNRNPC was associated with malignancy and development of gliomas. Similarly, the high expression of HNRNPC had a good prognosis. In conclusion, HNRNPC is a vital participant in the malignant progression of GBM and might be valuable for prognosis.
| S-EPMC7578363 | biostudies-literature
Project description:Osteosarcoma has been the most common malignant bone tumor in children and adolescents, while the 5-y survival of osteosarcoma patients gained no significant improvement over the past decades. This study aimed to explore the role of ferroptosis-related genes (FRGs) in the development and prognosis of osteosarcoma. The datasets of osteosarcoma patients including RNA sequencing data and clinical information were acquired from the TRGET and Gene Expression Omnibus (GEO) databases. The identification of molecular subgroups with different FRG expression patterns was achieved through nonnegative matrix factorization (NMF) clustering. The prognostic model was constructed using the least absolute shrinkage and selection operator (LASSO) algorithm and multivariate Cox regression analysis. The ESTIMATE algorithm was applied for determining the stromal score, immune score, ESTIMA score, and tumor purity of osteosarcoma patients. Functional analyses including Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses, Gene Set Enrichment Analysis (GSEA), and Gene Set Variation Analysis (GSVA) were conducted to explore the underlying mechanisms in the development and prognosis of osteosarcoma. Two molecular subgroups with different FRGs expression patterns were identified. The molecular subgroups with higher immune score and more active immune status showed better prognostic survival. On the basis of FRGs, a prognostic model and a nomogram integrating clinical characteristics were constructed and their prediction efficiency for osteosarcoma prognosis were well validated. Gene functional enrichment analysis showed that these differentially expressed FRGs were mainly enriched in immunity-related signaling pathways, indicating that FRGs may affect the development and prognosis of osteosarcoma by regulating the immune microenvironment. The expression profiles of FRGs were closely related to the immunity status and prognostic survival of osteosarcoma patients. The interaction between ferroptosis and immunity in the development of osteosarcoma could provide a new insight into the exploration of molecular mechanisms and targeted therapies of osteosarcoma patients.
| S-EPMC8586685 | biostudies-literature
Project description:AimsGlioma is a highly invasive brain tumor, which makes prognosis challenging and renders patients resistant to various treatments. Induction of cell death is promising in cancer therapy. Ferroptosis, a recently discovered regulated cell death, can be induced for killing glioma cells. However, the prognostic prediction of ferroptosis-related genes (FRGs) in glioma remains elusive.MethodsThe mRNA expression profiles and gene variation and corresponding clinical data of glioma patients and NON-TUMOR control were downloaded from public databases. Risk score based on a FRGs signature was constructed in REMBRANDT cohort and validated in other datasets including CGGA-693, CGGA-325, and TCGA.ResultsOur results demonstrated that the majority of FRGs was differentially expressed among GBM, LGG, and NON-TUMOR groups (96.6%). Furthermore, the glioma patients with low-risk score exhibited a more satisfactory clinical outcome. The better prognosis was also validated in the glioma patients with low-risk score no matter to which grade they were affiliated. Functional analysis revealed that the high-risk score group was positively correlated with the enrichment scores for immune checkpoint blockade-related positive signatures, indicating the critical role of glioma immunotherapy via risk score.ConclusionA novel FRGs-related risk score can predict prognosis and immunotherapy in glioma patients.
| S-EPMC8265949 | biostudies-literature
Project description:The prognosis of patients with cholangiocarcinoma (CCA) is closely related to both immune cell infiltration and mRNA expression. Therefore, we aimed at conducting multi-immune-related gene analyses to improve the prediction of CCA recurrence. Immune-related genes were selected from the Gene Expression Omnibus (GEO), The Cancer Genome Atlas (TCGA), and the Immunology Database and Analysis Portal (ImmPort). The least absolute shrinkage and selection operator (LASSO) regression model was used to establish the multi-gene model that was significantly correlated with the recurrence-free survival (RFS) in two test series. Furthermore, compared with single genes, clinical characteristics, tumor immune dysfunction and exclusion (TIDE), and tumor inflammation signature (TIS), the 8-immune-related differentially expressed genes (8-IRDEGs) signature had a better prediction value. Moreover, the high-risk subgroup had a lower density of B-cell, plasma, B-cell naïve, CD8+ T-cell, CD8+ T-cell naïve, and CD8+ T-cell memory infiltration, as well as more severe immunosuppression and higher mutation counts. In conclusion, the 8-IRDEGs signature was a promising biomarker for distinguishing the prognosis and the molecular and immune features of CCA, and could be beneficial to the individualized immunotherapy for CCA patients.
| S-EPMC9283581 | biostudies-literature
Project description:BACKGROUND:Papillary thyroid carcinoma (PTC) is the most common type of thyroid cancer (TC), accounting for more than 80% of all cases. Ferroptosis is a novel iron-dependent and Reactive oxygen species (ROS) reliant type of cell death which is distinct from the apoptosis, necroptosis and pyroptosis. Considerable studies have demonstrated that ferroptosis is involved in the biological process of various cancers. However, the role of ferroptosis in PTC remains unclear. This study aims at exploring the expression of ferroptosis-related genes (FRG) and their prognostic values in PTC. METHODS:A ferroptosis-related gene signature was constructed using lasso regression analysis through the PTC datasets of the Cancer Genome Atlas (TCGA). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed to investigate the bioinformatics functions of significantly different genes (SDG) of ferroptosis. Additionally, the correlations of ferroptosis and immune cells were assessed through the single-sample gene set enrichment analysis (ssGSEA) and CIBERSORT database. Finally, SDG were test in clinical PTC specimens and normal thyroid tissues. RESULTS:LASSO regression model was utilized to establish a novel FRG signature with 10 genes (ANGPTL7, CDKN2A, DPP4, DRD4, ISCU, PGD, SRXN1, TF, TFRC, TXNRD1) to predicts the prognosis of PTC, and the patients were separated into high-risk and low-risk groups by the risk score. The high-risk group had poorer survival than the low-risk group (p < 0.001). Receiver operating characteristic (ROC) curve analysis confirmed the signature's predictive capacity. Multivariate regression analysis identified the prognostic signature-based risk score was an independent prognostic indicator for PTC. The functional roles of the DEGs in the TGCA PTC cohort were explored using GO enrichment and KEGG pathway analyses. Immune related analysis demonstrated that the most types of immune cells and immunological function in the high-risk group were significant different with those in the low-risk group. Quantitative Real-Time Polymerase Chain Reaction (qRT-PCR) verified the SDG have differences in expression between tumor tissue and normal thyroid tissue. In addition, cell experiments were conducted to observe the changes in cell morphology and expression of signature's genes with the influence of ferroptosis induced by sorafenib. CONCLUSIONS:We identified differently expressed FRG that may involve in PTC. A ferroptosis-related gene signature has significant values in predicting the patients' prognoses and targeting ferroptosis may be an alternative for PTC's therapy.
| S-EPMC8670268 | biostudies-literature
Project description:Glioblastoma multiforme (GBM) is a highly malignant primary brain tumour with a poor prognosis in adults. Identifying biomarkers that can aid in the molecular classification and risk stratification of GBM is critical. Here, we conducted a transcriptional profiling analysis of T-cell immunity in the tumour microenvironment of GBM patients and identified two novel T cell exhaustion (TEX)-related GBM subtypes (termed TEX-C1 and TEX-C2) using the consensus clustering. Our multi-omics analysis revealed distinct immunological, molecular and clinical characteristics for these two subtypes. Specifically, the TEX-C1 subtype had higher infiltration levels of immune cells and expressed higher levels of immune checkpoint molecules than the TEX-C2 subtype. Functional analysis revealed that upregulated genes in the TEX-C1 subtype were significantly enriched in immune response and signal transduction pathways, and upregulated genes in the TEX-C2 subtype were predominantly associated with cell fate and nervous system development pathways. Notably, patients with activated T-cell activity status in the TEX-C1 subgroup demonstrated a significantly worse prognosis than those with severe T cell exhaustion status in the TEX-C2 subgroup. Finally, we proposed a machine-learning-derived novel gene signature comprising 12 TEX-related genes (12TexSig) to indicate tumour subtyping. In the TCGA cohort, the 12TexSig demonstrated the ability to accurately predict the prognosis of GBM patients, and this prognostic value was further confirmed in two independent external cohorts. Taken together, our results suggest that the TEX-derived subtyping and gene signature has the potential to serve as a clinically helpful biomarker for guiding the management of GBM patients, pending further prospective validation.
| S-EPMC10660619 | biostudies-literature
Project description:Ferroptosis, an iron-dependent form of selective cell death, is involved in the development of many cancers. However, the role of ferroptosis-related genes (FRGs) in kidney renal papillary cell carcinoma (KIRP) is unclear. In this study, we examined the mRNA expression profiles and clinical data of patients with KIRP from the TCGA cohort. Consequently, 41 differentially-expressed FRGs were screened using the limma package, and 17 prognostic-related FRGs were identified by survival analysis and univariate Cox regression analyses. Thereafter, a ferroptosis-related gene prognostic index (FRGPI) was constructed based on five FRGs (AKR1C3, SAT1, FANCD2, HSBP1 and SQLE), using lasso Cox and multivariate Cox regression analyses. KIRP patients with high FRGPI scores displayed worse outcomes. Furthermore, the FRGPI was shown to be a reliable independent prognostic factor in both the training and testing cohorts. Comprehensive analysis also showed that the FRGPI can distinguish gene mutation, functional enrichment of immune cells and molecular function-related pathways. Interestingly, low FRGPI score could be more benefit from immune checkpoint inhibitors (ICIs) therapy. Then, the two hub prognostic genes (AKR1C3 and FANCD2) as a risk gene for KIRP were identified based on the FRGPI module, and the expression profiles of these two genes were validated using human KIRP cells, besides, we furthermore discovered that Fancd2 is significantly up-regulated in most cancers and is associated with prognosis. In conclusion, these findings showed that FRGPI can accurately predict the prognosis of patients with KIRP, suggesting that this risk model is a promising prognostic biomarker for these patients. Moreover, targeting ferroptosis (FANCD2) could be a potential therapeutic alternative for various cancers.
| S-EPMC9582751 | biostudies-literature
Project description:BackgroundFerroptosis, an iron-dependent form of programmed cell death, significantly impacts cancer, yet its link to prostate cancer (PCa) prognosis remains underexplored. This study aims to develop and validate a ferroptosis-related gene signature to predict PCa prognosis and immune microenvironment differences, potentially identifying therapeutic targets.MethodsRNA-sequencing data of 478 PCa patients and corresponding clinical data were downloaded from The Cancer Genome Atlas (TCGA) database. We investigated the disease-free survival (DFS) rates of the high- and low-risk groups using the Kaplan-Meier method. Functional differences between the high- and low-risk groups were investigated by a gene set enrichment analysis (GSEA), and Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses. The link between ferroptosis risk score and immune status was examined using CIBERSORT. The expression levels of core prognostic genes in benign prostatic hyperplasia (BPH) and PCa were verified using quantitative real-time polymerase chain reaction (qRT-PCR), Western blot, and immunohistochemistry (IHC).ResultsA novel ferroptosis-related prognostic gene signature was established and tested in the Gene Expression Omnibus (GEO) database based on univariate and multivariate Cox regression analyses. Patients with PCa were classified into high- and low-risk groups based on this ferroptosis signature. Patients in the high-risk group had worse outcomes than those in the low-risk group. The predictive accuracy of the model was demonstrated by a receiver operating characteristic (ROC) analysis. An additional enrichment analysis of TCGA cohort revealed the immune-related pathways were significantly upregulated in the high-risk group, with areas under the curve (AUCs) of 0.85 at 1 year, 0.82 at 3 years, and 0.76 at 5 years. In the GEO cohort, the AUCs reached 0.69 at 1 year, 0.74 at 3 years, and 0.75 at 5 years. An additional enrichment analysis indicated a significant upregulation of cytokine-related pathways, immune receptor activity, and other immune-related pathways in the high-risk group. Furthermore, the analysis revealed that the proportions of mast cells and plasma cells were significantly lower in the high-risk group compared to the low-risk group of PCa patients. Conversely, the proportion of regulatory T cells (Tregs) was significantly higher in the high-risk group than in the low-risk group. According to the qRT-PCR, Western blot, and IHC results, DRD4, SRC, AKR1C2, and AIFM2 expression was significantly higher in PCa than BPH. We also showed that the ferrostatin 1-treated LNCaP cells had higher expression levels of DRD4, SRC, and AKR1C2.ConclusionsA prognostic signature of eight ferroptosis-related genes (FRGs) that may accurately predict PCa patient outcomes was constructed and validated. FRGs may contribute to anti-tumor immunity and serve as therapeutic targets in PCa.
| S-EPMC11491218 | biostudies-literature
Project description:BackgroundThis study aims to develop novel signatures for glioblastoma multiforme (GBM).MethodsGBM expression profiles from The Cancer Genome Atlas (TCGA) were downloaded and DEGs between tumor and normal samples were identified by differential expression analysis (DEA). A risk signature was developed by applying weighted gene coexpression network analysis (WGCNA) and Cox regression analysis. Patients were divided into high and low risk group, followed by evaluating the performance of the signature via Kaplan-Meier curve analysis. In addition, the prognostic significance of the signature was further validated using an independent validation dataset from Chinese Glioma Genome Atlas (CGGA). DEGs between high and low risk group were subjected to functional annotation.ResultsA total of 748 DEGs were identified between primary tumor and normal samples. Following WGCNA and Cox regression analysis, 6 DEGs were identified and used to construct a risk signature. The signature showed high performance in both training and validation dataset. Subsequently, 397 DEGs were identified between high and low risk group. These DEGs were mainly enriched in terms related to calcium signaling, cAMP-mediated signaling, and synaptic transmission.ConclusionsThe risk signature may contribute to GBM diagnosis in future clinical practice.
| S-EPMC6720050 | biostudies-literature