Identification of a mesenchymal-related signature associated with clinical prognosis in glioma.
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ABSTRACT:
SUBMITTER: Zhang Z
PROVIDER: S-EPMC8148476 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Now, numerous exciting findings have been yielded in the field of energy metabolism within glioma cells. In addition to aerobic glycolysis, multiple catabolic pathways are employed for energy production. However, the prognostic significance of energy metabolism in glioma remains obscure. Here, we explored the relationship between energy metabolism gene profile and outcome of diffuse glioma patients using The Cancer Genome Altas (TCGA) and Chinese Glioma Genome Altas (CGGA) datasets. Based on the gene expression profile, consensus clustering identified two robust clusters of glioma patients with distinguished prognostic and molecular features. With the Cox proportional hazards model with elastic net penalty, an energy metabolism-related signature was built to evaluate patients' prognosis. Kaplan-Meier analysis found that the acquired signature could differentiate the outcome of low and high-risk groups of patients in both cohorts. Moreover, the signature, significantly associated with the clinical and molecular features, could serve as an independent prognostic factor for glioma patients. Gene Ontology (GO) and Gene Set Enrichment Analysis (GSEA) showed that gene sets correlated with high-risk group were involved in immune and inflammatory response, with the low-risk group were mainly related to glutamate receptor signaling pathway. Our results provided new insight into energy metabolism role in diffuse glioma.
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Project description:BackgroundGlioma is the most common brain tumor in adults and is characterized by a short survival time and high resistance to chemotherapy. It is imperative to determine the prognosis and therapy-related targets for glioma. Endoplasmic reticulum stress (ERS), as an adaptive protective mechanism, indicates the unfolded protein response (UPR) to determine cell survival and affects chemotherapy sensitivity, which is related to the prognosis of glioma.MethodsOur research used the TCGA database as the training group and the CGGA database as the testing group. Lasso regression and Cox analysis were performed to construct an ERS signature-based risk score model in glioma. Three methods (time-dependent receiver operating characteristic analysis and multivariate and univariate Cox regression analysis) were applied to assess the independent prognostic effect of texture parameters. Consensus clustering was used to classify the two clusters. In addition, functional and immune analyses were performed to assess the malignant process and immune microenvironment. Immunotherapy and anticancer drug response prediction were adopted to evaluate immune checkpoint and chemotherapy sensitivity.ResultsThe results revealed that the 7-gene signature strongly predicts glioma prognosis. The two clusters have markedly distinct molecular and prognostic features. The validation group result revealed that the signature has exceptional repeatability and certainty. Functional analysis showed that the ERS-related gene signature was closely associated with the malignant process and prognosis of tumors. Immune analysis indicated that the ERS-related gene signature is strongly related to immune infiltration. Immunotherapy and anticancer drug response prediction indicated that the ERS-related gene signature is positively correlated with immune checkpoint and chemotherapy sensitivity.ConclusionsCollectively, the ERS-related risk model can provide a novel signature to predict glioma prognosis and treatment.
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Project description:A major challenge for successful immunotherapy against glioma is the identification and characterization of validated targets. We have taken a bioinformatics approach towards understanding the biological context of IL-13 receptor α2 (IL13Rα2) expression in brain tumors, and its functional significance for patient survival. Querying multiple gene expression databases, we show that IL13Rα2 expression increases with glioma malignancy grade, and expression for high-grade tumors is bimodal, with approximately 58% of WHO grade IV gliomas over-expressing this receptor. By several measures, IL13Rα2 expression in patient samples and low-passage primary glioma lines most consistently correlates with the expression of signature genes defining mesenchymal subclass tumors and negatively correlates with proneural signature genes as defined by two studies. Positive associations were also noted with proliferative signature genes, whereas no consistent associations were found with either classical or neural signature genes. Probing the potential functional consequences of this mesenchymal association through IPA analysis suggests that IL13Rα2 expression is associated with activation of proinflammatory and immune pathways characteristic of mesenchymal subclass tumors. In addition, survival analyses indicate that IL13Rα2 over-expression is associated with poor patient prognosis, a single gene correlation ranking IL13Rα2 in the top ~1% of total gene expression probes with regard to survival association with WHO IV gliomas. This study better defines the functional consequences of IL13Rα2 expression by demonstrating association with mesenchymal signature gene expression and poor patient prognosis. It thus highlights the utility of IL13Rα2 as a therapeutic target, and helps define patient populations most likely to respond to immunotherapy in present and future clinical trials.
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Project description:Background: Glioma is the most common primary intracranial tumor, accounting for the vast majority of intracranial malignant tumors. Aberrant expression of RNA:5-methylcytosine(m5C) methyltransferases have recently been the focus of research relating to the occurrence and progression of tumors. However, the prognostic value of RNA:m5C methyltransferases in glioma remains unclear. This study investigated RNA: m5C methyltransferase expression and defined its clinicopathological signature and prognostic value in gliomas. Methods: We obtained the RNA-sequence and Clinicopathological data of RNA:m5C methyltransferases underlying gliomas from the Chinese Glioma Genome Atlas (CGGA) and The Cancer Genome Atlas (TCGA) datasets. We analyzed the expression of RNA:m5C methyltransferase genes in gliomas with different clinicopathological characteristics and identified different subtypes using Consensus clustering analysis. Gene Ontology (GO) and Gene Set Enrichment Analysis (GSEA) was used to annotate the function of these genes. Univariate Cox regression and the least absolute shrinkage and selection operator (LASSO) Cox regression algorithm analyses were performed to construct the risk signature. Kaplan-Meier method and Receiver operating characteristic (ROC) curves were used to assess the overall survival of glioma patients. Additionally, Cox proportional regression model analysis was developed to address the connections between the risk scores and clinical factors. Results: We revealed the differential expression of RNA:m5C methyltransferase genes in gliomas with different clinicopathological features. Consensus clustering of RNA:m5C methyltransferases identified three clusters of gliomas with different prognostic and clinicopathological features. Meanwhile, functional annotations demonstrated that RNA:m5C methyltransferases were significantly associated with the malignant progression of gliomas. Thereafter, five RNA:m5C methyltransferase genes were screened to construct a risk signature that can be used to predict not only overall survival but also clinicopathological features in gliomas. ROC curves revealed the significant prognostic ability of this signature. In addition, Multivariate Cox regression analyses indicated that the risk score was an independent prognostic factor for glioma outcome. Conclusion: We demonstrated the prognostic role of RNA:m5C methyltransferases in the initiation and progression of glioma. We have expanded on the understanding of the molecular mechanism involved, and provided a unique approach to predictive biomarkers and targeted therapy for gliomas.
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Project description:Glioma is the most common malignant primary brain tumour. It is of great significance for the prognosis and personalized treatment of glioma patients to accurate identification of glioma based on biomarkers. Pyroptosis, a kind of programmed cell death, is closely related to tumour progression and tumour immune microenvironment. However, the role of pyroptosis in glioma remained unclear. Herein, we used glioma clinical and expression data from TCGA and CGGA to explore the relationship between pyroptosis and glioma. We first summarized the incidence of copy number variations and somatic mutations of 33 pyroptosis-related genes and explored prognostic correlation of these genes. Based on pyroptosis-related genes, three molecular subgroups of glioma related to prognosis were identified. We also found that each subgroup has unique immune and biological behaviours characteristics. Finally, based on 7 pyroptosis-related genes (CASP3, CASP4, CASP6, CASP8, CASP9, PRKACA and ELANE), we constructed a prognosis model by Lasso and Cox regression, which had a strong predictive power for the overall survival in CGGA test cohort (p < 0.05). In summary, we explored the role of pyroptosis-related genes in gliomas and the association of these genes with tumour immunity. We found the biomarkers valuable to diagnosis and prognosis, hence, provide reference to the development and treatment of tumorigenesis in glioma.
| S-EPMC8742236 | biostudies-literature
Project description:Epithelial-mesenchymal transition (EMT)-related long non-coding RNAs (lncRNAs) may be exploited as potential therapeutic targets in gliomas. However, the prognostic value of EMT-related lncRNAs in gliomas is unclear. We obtained lncRNAs from The Cancer Genome Atlas and constructed EMT-related lncRNA co-expression networks to identify EMT-related lncRNAs. The Chinese Glioma Genome Atlas (CGGA) was used for validation. Gene set enrichment and principal component analyses were used for functional annotation. The EMT-lncRNA co-expression networks were constructed. A real-time quantitative polymerase chain reaction assay was performed to validate the bioinformatics results. A nine-EMT-related lncRNAs (HAR1A, LINC00641, LINC00900, MIR210HG, MIR22HG, PVT1, SLC25A21-AS1, SNAI3-AS1, and SNHG18) signature was identified in patients with glioma. Patients in the low-risk group had a longer overall survival (OS) than those in the high-risk group (P < 0.0001). Additionally, patients in the high-risk group showed no deletion of chromosomal arms 1p and/or 19q, isocitrate dehydrogenase wild type, and higher World Health Organization grade. Moreover, the signature was identified as an independent factor and was significantly associated with OS (P = 0.041, hazard ratio = 1.806). These findings were further validated using the CGGA dataset. The low- and high-risk groups showed different EMT statuses based on principal component analysis. To study the regulatory function of lncRNAs, a lncRNA-mediated ceRNA network was constructed, which showed that complex interactions of lncRNA-miRNA-mRNA may be a potential cause of EMT progression in gliomas. This study showed that the nine-EMT-related lncRNA signature has a prognostic value in gliomas.
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Project description:(1) Background: Endometrial cancer is the most prevalent cause of gynecological malignant tumor worldwide. The prognosis of endometrial carcinoma patients with distant metastasis is poor. (2) Method: The RNA-Seq expression profile and corresponding clinical data were downloaded from the Cancer Genome Atlas database and the Gene Expression Omnibus databases. To predict patients' overall survival, a 9 EMT-related genes prognosis risk model was built by machine learning algorithm and multivariate Cox regression. Expressions of nine genes were verified by RT-qPCR. Responses to immune checkpoint blockades therapy and drug sensitivity were separately evaluated in different group of patients with the risk model. (3) Endometrial carcinoma patients were assigned to the high- and low-risk groups according to the signature, and poorer overall survival and disease-free survival were showed in the high-risk group. This EMT-related gene signature was also significantly correlated with tumor purity and immune cell infiltration. In addition, eight chemical compounds, which may benefit the high-risk group, were screened out. (4) Conclusions: We identified a novel EMT-related gene signature for predicting the prognosis of EC patients. Our findings provide potential therapeutic targets and compounds for personalized treatment. This may facilitate decision making during endometrial carcinoma treatment.
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Project description:BackgroundAs the most common primary malignant intracranial tumor, glioblastoma has a poor prognosis with limited treatment options. It has a high propensity for recurrence, invasion, and poor immune prognosis due to the complex tumor microenvironment.MethodsSix groups of samples from four datasets were included in this study. We used consensus ClusterPlus to establish two subgroups by the EMT-related gene. The difference in clinicopathological features, genomic characteristics, immune infiltration, treatment response and prognoses were evaluated by multiple algorithms. By using LASSO regression, multi-factor Cox analysis, stepAIC method, a prognostic risk model was constructed based on the final screened genes.ResultsThe consensusClusterPlus analyses revealed two subtypes of glioblastoma (C1 and C2), which were characterized by different EMT-related gene expression patterns. C2 subtype with the worse prognosis had the more malignant clinical and pathology manifestations, higher Immune infiltration and tumor-associated molecular pathways scores, and poorer response to treatment. Additionally, our EMT-related genes risk prediction model can provide valuable support for clinical evaluations of glioma.ConclusionsThe assessment system and prediction model displayed good performance in independent prognostic risk assessment and individual patient treatment response prediction. This can help with clinical treatment decisions and the development of effective treatments.
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Project description:Background Low-grade glioma (LGG) is a common malignant tumor of the central nervous system. The clinical prognosis of different patients varies greatly, so exploring appropriate markers that affect the prognosis and treatment of LGG is important. The purpose of this study was to identify the potential effect of autophagy-related DNA methylation on the prognosis and immune microenvironment in LGG. Methods The methylation profile, transcription data and corresponding clinical information of 451 patients with LGG were obtained from The Cancer Genome Atlas (TCGA). Another methylation data and clinical information of 110 patients with LGG from Chinese Glioma Genome Atlas (CGGA) were used as the validation set. Through univariate and multivariate COX regression analysis, we identified the autophagy-related genes (ARGs) associated with methylation levels and prognosis, and established a risk assessment signature. The receiver operating characteristic (ROC) and Kaplan-Meier (KM) survival curve were used to verify the model’s effectiveness in predicting prognosis. Patients were divided into low- and high-risk groups based on risk scores. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis were used to explore the differences in biological functions between the two groups. ESTIMATE and CIBERSORT algorithms were used to explore differences in immune infiltration and immunotherapy sites. Pearson correlation analysis was used to analyze the relative relationship between methylated cg sites and corresponding genes. Results A total of 6 ARGs (ARSB, CFLAR, WIPI2, RB1, ERN1, RAB24) were selected that were associated with methylation levels and prognosis. The area under the curve (AUC) =0.96, and the KM survival curve P<0.0001, which proves that the risk assessment model has a good effect in predicting the prognosis of LGG. GO and KEGG enrichment analysis showed that the model mainly involved major histocompatibility complex (MHC) II receptors, antigen processing and presentation, and immune cell differentiation. In addition, we also found differences in immune infiltration and immune checkpoints between high- and low-risk groups. Conclusions The methylation levels of these 6 ARGs have a strong predictive potential for LGG, and the methylation regulation of ARGs has an important impact on the immune microenvironment of LGGs.
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Project description:Altered metabolism of glucose, lipid and glutamine is a prominent hallmark of cancer cells. Currently, cell heterogeneity is believed to be the main cause of poor prognosis of glioblastoma (GBM) and is closely related to relapse caused by therapy resistance. However, the comprehensive model of genes related to glucose-, lipid- and glutamine-metabolism associated with the prognosis of GBM remains unclear, and the metabolic heterogeneity of GBM still needs to be further explored. Based on the expression profiles of 1,395 metabolism-related genes in three datasets of TCGA/CGGA/GSE, consistent cluster analysis revealed that GBM had three different metabolic status and prognostic clusters. Combining univariate Cox regression analysis and LASSO-penalized Cox regression machine learning methods, we identified a 17-metabolism-related genes risk signature associated with GBM prognosis. Kaplan-Meier analysis found that obtained signature could differentiate the prognosis of high- and low-risk patients in three datasets. Moreover, the multivariate Cox regression analysis and receiver operating characteristic curves indicated that the signature was an independent prognostic factor for GBM and had a strong predictive power. The above results were further validated in the CGGA and GSE13041 datasets, and consistent results were obtained. Gene set enrichment analysis (GSEA) suggested glycolysis gluconeogenesis and oxidative phosphorylation were significantly enriched in high- and low-risk GBM. Lastly Connectivity Map screened 54 potential compounds specific to different subgroups of GBM patients. Our study identified a novel metabolism-related gene signature, in addition the existence of three different metabolic status and two opposite biological processes in GBM were recognized, which revealed the metabolic heterogeneity of GBM. Robust metabolic subtypes and powerful risk prognostic models contributed a new perspective to the metabolic exploration of GBM.
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