Transcriptional Networks Identify BRPF1 as a Potential Drug Target Based on Inflammatory Signature in Primary Lower-Grade Gliomas.
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ABSTRACT:
SUBMITTER: Xia M
PROVIDER: S-EPMC8674185 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:We studied if combination genetic signature potentially stratifies lower-grade gliomas better than histology by investigating 214 lower-grade gliomas for IDH1/2 and TERTp mutations, 1p/19q codeletion and EGFR amplification as to their impact on prognostication. Prognostic association of grading was independent of other prognostic variables including age, histological type, IDH1/2, 1p/19q and TERTp status. No single marker, including IDH1/2, superseded grading in prognostication, indicating grading was still a very important tool. Prognosis was most favorable in 31.7% of patients with IDH1/2 mutation and either 1p/19q codeletion or TERTp mutation (IDHmut-OT), intermediate in 45.8% of patients with IDH1/2 mutation only (IDHmut) and 16.9% of patients without any of the alterations (IDHwt), and poorest in 5.6% of patients with wild-type IDH1/2 and either TERTp mutation or EGFR amplification (IDHwt-ET). Our results suggested not all IDH1/2 wild-type lower-grade gliomas are aggressive and additional biomarkers are required to identify glioblastoma-equivalent tumors. Multivariate analysis revealed independent prognostic values of grading and genetic signature. Grade II IDHwt-ET gliomas exhibited shorter survival than IDH1/2 mutated grade III gliomas, suggesting combination genetic signature potentially superseded grading in prognostication. In summary, biomarker-based stratification is useful in the diagnosis and prognostication of lower-grade gliomas, and should be used together with grading.
| S-EPMC4673237 | biostudies-literature
Project description:Unravelling the heterogeneity is the central challenge for glioma precession oncology. In this study, we extracted quantitative image features from T2-weighted MR images and revealed that the isocitrate dehydrogenase (IDH) wild type and mutant lower grade gliomas (LGGs) differed in their expression of 146 radiomic descriptors. The logistic regression model algorithm further reduced these to 86 features. The classification model could discriminate the two types in both the training and validation sets with area under the curve values of 1.0000 and 0.9932, respectively. The transcriptome-radiomic analysis revealed that these features were associated with the immune response, biological adhesion, and several malignant behaviors, all of which are consistent with biological processes that are differentially expressed in IDH wild type and IDH mutant LGGs. Finally, a prognostic signature showed an ability to stratify IDH mutant LGGs into high and low risk groups with distinctive outcomes. By extracting a large number of radiomic features, we identified an IDH mutation-specific radiomic signature with prognostic implications. This radiomic signature may provide a way to non-invasively discriminate lower-grade gliomas as with or without the IDH mutation.
| S-EPMC6366985 | biostudies-literature
Project description:Purpose of reviewLow-grade gliomas (LGG) are a group of primary brain tumors that arise from supporting glial cells. They are characterized by a mutation in the isocitrate dehydrogenase (IDH) enzyme and include astrocytomas and oligodendrogliomas. They usually affect young adults, and their main treatment consists of surgical resection, followed by radiation and chemotherapy in selected patients. This article reviews recent research on the clinical and molecular aspects of the disease and innovative therapeutic modalities in the process.Recent findingsNewly identified clinical and molecular features are currently used in the classification of LGG and applied in treatment-planning decisions. Advanced studies on the cellular level have an advanced understanding of the metabolic effects induced by IDH mutations, offering opportunities for specific targeted therapies that may improve patient outcomes. Such findings may lead to a paradigm shift in the treatment of these tumors. Although LGG are sensitive to radiation and chemotherapy, these therapies are not curative, and patient survival remains limited, raising the need for more creative and effective interventions.
| S-EPMC7244462 | biostudies-literature
Project description:BackgroundLower-grade gliomas (LGGs) have more favorable outcomes than glioblastomas; however, LGGs often progress to process glioblastomas within a few years. Numerous studies have proven that the tumor microenvironment (TME) is correlated with the prognosis of glioma.MethodsLGG RNA-Sequencing (RNA-seq) data from The Cancer Genome Atlas (TCGA) and the Chinese Glioma Genome Atlas (CGGA) were extracted and then divided into training and testing cohorts, respectively. Immune-related differentially expressed genes (DEGs) were screened to establish a prognostic signature by a multivariate Cox proportional hazards regression model. The immune-related risk score and clinical information, such as age, sex, World Health Organization (WHO) grade, and isocitrate dehydrogenase 1 (IDH1) mutation, were used to independently validate and develop a prognostic nomogram. GO and KEGG pathway analyses to DEGs between immune-related high-risk and low-risk groups were performed.ResultsSixteen immune-related genes were screened for establishing a prognostic signature. The risk score had a negative correlation with prognosis, with an area under the receiver operating characteristic (ROC) curve of 0.941. The risk score, age, grade, and IDH1 mutation were identified as independent prognostic factors in patients with LGGs. The hazard ratios (HRs) of the high-risk score were 5.247 [95% confidence interval (CI) = 3.060-8.996] in the multivariate analysis. A prognostic nomogram of 1-, 3-, and 5-year survival was established and validated internally and externally. Go and KEGG pathway analyses implied that immune-related biological function and pathways were involved in the TME.ConclusionThe immune-related prognostic signature and the prognostic nomogram could accurately predict survival.
| S-EPMC7753319 | biostudies-literature
Project description:IntroductionGlioma is the most common malignant primary brain tumor with survival outcome for patients with lower-grade gliomas (LGGs) being quite variable. Epigenetic modifications in LGGs appear tightly linked to patient clinical outcomes but are not commonly used as clinical tools.AimsWe aimed to derive an epigenetic enzyme gene signature for LGGs that would allow for improved clinical risk stratification.ResultsThe study employed transcriptomic data of 711 lower-grade gliomas from three publically available data sets. Based on least absolute shrinkage and selection operator (LASSO) Cox regression analysis, we discovered a 13-gene epigenetic signature that strongly predicts poor overall survival in LGGs. The robust prediction ability for survival was further verified in two independent validation cohorts. The signature was also significantly associated with malignant molecular signatures including wild-type IDH, unmethylated MGMT promoter, and non-codeletion of 1p19q together with linkage to multiple oncogenic pathways. Interestingly, our results showed that immune infiltration of MDSCs together with mRNA expression of immune inhibition biomarkers was also positively correlated with the epigenetic signature. Lastly, we confirmed the oncogenic role of SMYD2 in glioma tumor cells in functional assays.ConclusionsWe report a novel epigenetic gene signature that harbors robust survival prediction value for LGG patients that is tightly linked to activation of multiple oncogenic pathways.
| S-EPMC7941239 | biostudies-literature
Project description:Background: Mutations in isocitrate dehydrogenase (IDH) affect the development and prognosis of gliomas. We investigated the role of IDH mutations in the regulation of immune phenotype in lower-grade gliomas (LGGs).Method and patients: A total of 1,008 cases with clinical and IDH mutation data from five cohorts were enrolled. Samples with RNA sequencing data from the Chinese Glioma Genome Atlas (CGGA) were used as training set, whereas RNA data from the Cancer Genome Atlas, Repository for Molecular Brain Neoplasia, GSE16011, and CGGA microarray databases were used for validation. R language tools and bioinformatics analysis were used for gene signature construction and biological function annotation.Results: We found that IDH mutations caused down-regulation of local immune response as among 332 immune system-related genes, 196(59.0%) were differentially expressed according to IDH mutation status. Nearly 70% of those differentially expressed genes exhibited prognostic value in LGGs. An immune response-based gene signature was constructed that distinguished cases with high- or low-risk of unfavorable prognosis and remained an independent prognostic factor in multivariate analyses in both training and validation cohorts. Samples from high-risk cases exhibited elevated expression of genes involved in immune response and NF-?B pathway activation. Furthermore, we found a strong correlation between the risk score and T cells, macrophage-related immune response, and expression of several prominent immune checkpoints.Conclusion: Our results indicated that mutant IDH is highly associated with the regulation of the immune microenvironment in LGGs. The observed immune system gene signature, which was sensitive to IDH mutation status, efficiently predicted patient survival.
| S-EPMC5980422 | biostudies-literature
Project description:Background: Lower-grade glioma (LGG) is the most common histology identified in gliomas, a heterogeneous tumor that may develop into high-grade malignant glioma that seriously shortens patient survival time. Recent studies reported that glutamatergic synapses might play an essential role in the progress of gliomas. However, the role of glutamatergic synapse-related biomarkers in LGG has not been systemically researched yet. Methods: The mRNA expression data of glioma and normal brain tissue were obtained from The Cancer Genome Atlas database and Genotype-Tissue Expression, respectively, and the Chinese Glioma Genome Atlas database was used as a validation set. Difference analysis was performed to evaluate the expression pattern of glutamatergic synapse-related genes (GSRGs) in LGG. The least absolute shrinkage and selection operator (LASSO) Cox regression was applied to construct the glutamatergic synapse-related risk signature (GSRS), and the risk score of each LGG sample was calculated based on the coefficients and expression value of selected GSRGs. Univariate and multivariate Cox regression analyses were used to investigate the prognostic value of risk score. Immunity profile and single-sample gene set enrichment analysis (ssGSEA) were performed to explore the association between risk score and the characters of tumor microenvironment in LGG. Gene set variation analysis (GSVA) was performed to investigate the potential pathways related to GSRS. The HPA database and real-time PCR were used to identify the expression of hub genes identified in GSRS. Results: A total of 22 genes of 39 GSRGs were found differentially expressed among normal and LGG samples. Through the LASSO algorithm, 14-genes GSRS constructed were associated with the prognosis and clinicopathological features of patients with LGG. Furthermore, the risk score level was significantly positively correlated with the infiltrating level of immunosuppressive cells, including M2 macrophages and regulatory T cells. GSVA identified a series of cancer-related pathways related to GSRS, such as P13K-AKT and P53 pathways. Moreover, ATAD1, NLGN2, OXTR, and TNR, hub genes identified in GSRS, were considered as potential prognostic biomarkers in LGG. Conclusion: A 14-genes GSRS was constructed and verified in this study. We provided a novel insight into the role of GSRS in LGG through a series of bioinformatics methods.
| S-EPMC8581158 | biostudies-literature
Project description:Temozolomide (TMZ) has been used as a first-line therapy against lower-grade gliomas (LGGs) combined with other chemotherapy drugs. However, there has been no reliable index predicting TMZ response of patients with LGGs. In this study, we aim to investigate the relationship between gene expressions and the prognosis of TMZ therapy in LGGs. We integrated transcriptome and clinical data of 171 LGGs from the Chinese Glioma Genome Atlas (CGGA). Consensus LASSO Cox regression was used to identify 14 key genes related to different clinical outcomes under TMZ chemotherapy. We constructed and evaluated a risk score based on the 14 genes. Patients with LGGs of lower risk scores (low-risk group) generally had better survival than those LGGs of higher risk scores (high-risk group), which is independent of clinicopathological factors. High-risk patients showed activation of innate and humoral-type immunity. The prognostic contribution of the risk score was validated in an independent validation cohort of 65 patients. Besides, combined with three independent predictors (grade, IDH1 mutation status, and chr1p19q co-deletion status), we further developed a nomogram to predict the benefit of TMZ treatment in LGGs. Our results indicate that a transcriptome-based index can optimize the treatment strategy for patients with LGGs under TMZ therapy.
| S-EPMC10643648 | biostudies-literature
Project description:Glioma is the most malignant and aggressive type of brain tumour with high heterogeneity and mortality. Although some clinicopathological factors have been identified as prognostic biomarkers, the individual variants and risk stratification in patients with lower grade glioma (LGG) have not been fully elucidated. The primary aim of this study was to identify an efficient DNA methylation combination biomarker for risk stratification and prognosis in LGG. We conducted a retrospective cohort study by analysing whole genome DNA methylation data of 646 patients with LGG from the TCGA and GEO database. Cox proportional hazard analysis was carried out to screen and construct biomarker model that predicted overall survival (OS). The Kaplan-Meier survival curves and time-dependent ROC were constructed to prove the efficiency of the signature. Then, another independent cohort was used to further validate the finding. A two-CpG site DNA methylation signature was identified by multivariate Cox proportional hazard analysis. Further analysis indicated that the signature was an independent survival predictor from other clinical factors and exhibited higher predictive accuracy compared with known biomarkers. This signature was significantly correlated with immune-checkpoint blockade, immunotherapy-related signatures and ferroptosis regulator genes. The expression pattern and functional analysis showed that these two genes corresponding with two methylation sites contained in the model were correlated with immune infiltration level, and involved in MAPK and Rap1 signalling pathway. The signature may contribute to improve the risk stratification of patients and provide a more accurate assessment for precision medicine in the clinic.
| S-EPMC8743658 | biostudies-literature
Project description:ObjectiveWe aimed to identify a radiomic signature to be used as a noninvasive biomarker of prognosis in patients with lower-grade gliomas (LGGs) and to reveal underlying biological processes through comprehensive radiogenomic investigation.MethodsWe extracted 55 radiomic features from T2-weighted images of 233 patients with LGGs (training cohort: n = 85; validation cohort: n = 148). Univariate Cox regression and linear risk score formula were applied to generate a radiomic-based signature. Gene ontology analysis of highly expressed genes in the high-risk score group was conducted to establish a radiogenomic map. A nomogram was constructed for individualized survival prediction.ResultsThe six-feature radiomic signature stratified patients in the training cohort into low- or high-risk groups for overall survival (P = 0.0018). This result was successfully verified in the validation cohort (P = 0.0396). Radiogenomic analysis revealed that the prognostic radiomic signature was associated with hypoxia, angiogenesis, apoptosis, and cell proliferation. The nomogram resulted in high prognostic accuracy (C-index: 0.92, C-index: 0.70) and favorable calibration for individualized survival prediction in the training and validation cohorts.ConclusionsOur results suggest a great potential for the use of radiomic signature as a biological surrogate in providing prognostic information for patients with LGGs.
| S-EPMC6224242 | biostudies-literature