A new functional classification system (FGA/B) with prognostic value for glioma patients.
Ontology highlight
ABSTRACT:
SUBMITTER: Friedlein K
PROVIDER: S-EPMC4648469 | biostudies-other | 2015
REPOSITORIES: biostudies-other
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Project description:Although N6-methyladenosine (m6A) has been implicated in various biological functions in human cancers, its role in predicting the prognosis of glioma remains unclear. In this study, the transcriptome expression profiles and the clinical data of 961 patients were derived from the Chinese Glioma Genome Atlas (CGGA). We comprehensively evaluated the association between the expression of m6A regulators and the prognosis of glioma and established a 3-gene (YTHDF2, FTO, and ALKBH5) risk signature using least absolute shrinkage and selection operator (LASSO) analysis. Patients with a high-risk signature had significantly adverse prognoses. Gene set enrichment analysis (GSEA) analysis revealed that the G2M checkpoint, MTORC1 signaling, epithelial mesenchymal transition, and PI3K-AKT-mTOR signaling were significantly enriched in the high-risk group. Univariate and multivariate Cox regression analyses confirmed the independent prognostic value of this risk signature. We then constructed a nomogram for individualized prediction of overall survival (OS) by integrating clinicopathological features (age, World Health Organization [WHO] grade), treatment information (radiotherapy, temozolomide therapy), and m6A risk signature. The calibration curves showed excellent agreement between the predicted and actual probabilities for the 1-, 3-, and 5-year OS, with a C-index of 0.780 in the training cohort and 0.717 in the validation cohort. Altogether, our study elucidated the important role of m6A regulators in glioma prognosis, which is valuable for the selection of therapeutic methods and clinical management of patients with glioma.
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Project description:BACKGROUND:Previously developed classifications of glioma have provided enormous advantages for the diagnosis and treatment of glioma. Although the role of alternative splicing (AS) in cancer, especially in glioma, has been validated, a comprehensive analysis of AS in glioma has not yet been conducted. In this study, we aimed at classifying glioma based on prognostic AS. METHODS:Using the TCGA glioblastoma (GBM) and low-grade glioma (LGG) datasets, we analyzed prognostic splicing events. Consensus clustering analysis was conducted to classified glioma samples and correlation analysis was conducted to characterize regulatory network of splicing factors and splicing events. RESULTS:We analyzed prognostic splicing events and proposed novel splicing classifications across pan-glioma samples (labeled pST1-7) and across GBM samples (labeled ST1-3). Distinct splicing profiles between GBM and LGG were observed, and the primary discriminator for the pan-glioma splicing classification was tumor grade. Subtype-specific splicing events were identified; one example is AS of zinc finger proteins, which is involved in glioma prognosis. Furthermore, correlation analysis of splicing factors and splicing events identified SNRPB and CELF2 as hub splicing factors that upregulated and downregulated oncogenic AS, respectively. CONCLUSION:A comprehensive analysis of AS in glioma was conducted in this study, shedding new light on glioma heterogeneity and providing new insights into glioma diagnosis and treatment.
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Project description:The aim of the present study was to evaluate the clinical importance of melanoma-associated antigen D4 (MAGE-D4) expression in glioma, and to identify it as a valuable prognostic biomarker and therapeutic target. To achieve this, the expression of MAGE-D4 protein in 124 tumor tissues from patients with glioma was measured using reverse transcription-quantitative polymerase chain reaction (RT-qPCR) and immunohistochemistry (IHC), and the associations between MAGE-D4expression and clinicopathological factors were evaluated. The survival analysis demonstrated the significant prognostic value of MAGE-D4 in glioma using follow-up data. RT-qPCR and IHC analysis confirmed that MAGE-D4 mRNA and protein expression levels were significantly increased in glioma tissues compared with those in normal brain tissues. The present study demonstrated that the percentage of glioma tissues with high expression of MAGE-D4 mRNA was 67.74%, and the percentage positive for MAGE-D4 protein expression was 78.23%. All patients with high MAGE-D4 expression in cancerous tissues experienced significantly reduced median overall survival (OS; 18.00 vs. 33.29 months; P<0.001) and recurrence-free survival (RFS; 12.7 vs. 28.3 months; P<0.001) times compared with those with low MAGE-D4 expression. In the patients with lower grade glioma [World Health Organization (WHO), I-II], similar results were obtained for the OS (26.11 vs. 57.85 months; P=0.013) and RFS (22.7 vs. 55.3 months; P=0.010) times; however, in patients with high-grade glioma (WHO, III-IV), there were no significant differences between high and low MAGE-D4 expression levels with regard to OS and RFS times (P>0.05). Multivariate analysis indicated that high MAGE-D4 protein expression was an important independent prognostic factor for patients with glioma (hazard ratio, 2.384; P=0.005), and was significantly associated with higher grade glioma (P<0.001). These results indicated that MAGE-D4 may be a potential biomarker for glioma and an important prognostic factor for patients with new or recurring glioma.
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Project description:BackgroundThe study sought to compare Coronary Artery Disease Reporting and Data System (CAD-RADS) classification with traditional coronary artery disease (CAD) classifications and Duke Prognostic CAD Index for predicting the risk of all-cause mortality in patients with suspected CAD.Methods9625 consecutive suspected CAD patients were assessed by coronary CTA for CAD-RADS classification, traditional CAD classifications and Duke Prognostic CAD Index. Kaplan-Meier and multivariable Cox models were used to estimate all-cause mortality. Discriminatory ability of classifications was assessed using time dependent receiver-operating characteristic (ROC) curves and The Hosmer-Lemeshow goodness-of-fit test was employed to evaluate calibration.ResultsA total of 540 patients died from all causes with a median follow-up of 4.3 ± 2.1 years. Kaplan-Meier survival curves showed the cumulative events increased significantly associated with CAD-RADS, three traditional CAD classifications and Duke Prognostic CAD Index. In multivariate Cox regressions, the risk for the all-cause death increased from HR 0.861 (95% CI 0.420-1.764) for CAD-RADS 1 to HR 2.761 (95% CI 1.961-3.887) for CAD-RADS 4B&5, using CAD-RADS 0 as the reference group. The relative HRs for all-cause death increased proportionally with the grades of the three traditional CAD classifications and Duke Prognostic CAD Index. The area under the time dependent ROC curve for prediction of all-cause death was 0.7917, 0.7805, 0.7991for CAD-RADS in 1 year, 3 year, 5 year, respectively, which was non-inferior to the traditional CAD classifications and Duke Prognostic CAD Index.ConclusionsThe CAD-RADS classification provided important prognostic information for patients with suspected CAD with noninvasive evaluation, which was non-inferior than Duke Prognostic CAD Index and traditional stenosis-based grading schemes in prognostic value of all-cause mortality. Traditional and simplest CAD classification should be preferable, given the more number of groups and complexity of CAD-RADS and Duke prognostic index, without using more time consuming classification.
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Project description:INTRODUCTION:Gliomas are the most common primary brain tumors in adults and a significant cause of cancer-related mortality. A 9-gene signature was identified as a novel prognostic model reflecting survival situation obviously in gliomas. AIMS:To identify an mRNA expression signature to improve outcome prediction for patients with different glioma grades. RESULTS:We used whole-genome mRNA expression microarray data of 220 glioma samples of all grades from the Chinese Glioma Genome Atlas (CGGA) database (http://www.cgga.org.cn) as a discovery set and data from Rembrandt and GSE16011 for validation sets. Data from every single grade were analyzed by the Kaplan-Meier method with a two-sided log-rank test. Univariate Cox regression and linear risk score formula were applied to derive a gene signature with better prognostic performance. We found that patients who had high risk score according to the signature had poor overall survival compared with patients who had low risk score. Highly expressed genes in the high-risk group were analyzed by gene ontology (GO) and gene set variation analysis (GSVA). As a result, the reason for the divisibility of gliomas was likely due to cell life processes and adhesion. CONCLUSION:This 9-gene-signature prediction model provided a more accurate predictor of prognosis that denoted patients with high risk score have poor outcome. Moreover, these risk models based on defined molecular profiles showed the considerable prospect in personalized cancer management.
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Project description:BackgroundMethylation at 5 CpG sites was previously shown to classify chronic lymphocytic leukemia (CLL) into 3 prognostic subgroups. Here, we aimed to validate the marker set in an additional cohort and to evaluate its clinical utility for CLL patient stratification.MethodsWe evaluated this epigenetic marker set in 79 German patients using bisulfite treatment followed by pyrosequencing and classification using a support vector machine-learning tool.ResultsThe n-CLL, i-CLL, and m-CLL classification was detected in 28 (35%), 10 (13%), and 41 (51%) patients, respectively. Epigenetic grouping was associated with IGHV mutational status (P = 2 × 10-12), isolated del13q (P = 9 × 10-6), del17p (P = .015), complex karyotype (P = .005), VH-usage, and clinical outcome as time to first treatment (P = 1.4 × 10-12) and overall survival (P = .003). Multivariate Cox regression analysis identified n-CLL as a factor for earlier treatment hazard ratio (HR), 6.3 (95% confidence interval [CI] 2.4-16.4; P = .0002) compared to IGHV mutational status (HR 4.6, 95% CI 1.9-11.3, P = .0008). In addition, when comparing the prognostic value of the epigenetic classification system with the IGHV classification, epigenetic grouping performed better compared to IGHV mutational status using Kaplan-Meier estimation and allowed the identification of a third, intermediate (i-CLL) group. Thus, our study confirmed the prognostic value of the epigenetic marker set for patient stratification in routine clinical diagnostics.
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Project description:MS4A6A has been recognized as being associated with aging and the onset of neurodegenerative disease. However, the mechanisms of MS4A6A in glioma biology and prognosis are ill-defined. Here, we show that MS4A6A is upregulated in glioma tissues, resulting in unfavorable clinical outcomes and poor responses to adjuvant chemotherapy. Multivariate Cox regression analysis suggested that MS4A6A expression can act as a strong and independent predictor for glioma outcomes (CGGA1: HR: 1.765, p < 0.001; CGGA2: HR: 2.626, p < 0.001; TCGA: HR: 1.415, p < 0.001; Rembrandt: HR: 1.809, p < 0.001; Gravendeel: HR: 1.613, p < 0.001). A protein-protein interaction (PPI) network revealed that MS4A6A might be coexpressed with CD68, CD163, and macrophage-specific signatures. Enrichment analysis showed the innate immune response and inflammatory response to be markedly enriched in the high MS4A6A expression group. Additionally, single-cell RNA sequencing (scRNA-seq) analysis revealed distinctive expression features for MS4A6A in macrophages in the glioma immune microenvironment (GIME). Immunofluorescence staining confirmed colocalization of CD68/MS4A6A and CD163/MS4A6A in macrophages. Correlation analysis revealed that MS4A6A expression is positively related to the tumor mutation burden (TMB) of glioma, displaying the high potential of applying MS4A6A to evaluate responsiveness to immunotherapy. Altogether, our research indicates that MS4A6A upregulation may be used as a promising and effective indicator for adjuvant therapy and prognosis assessment.
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Project description:Malignant glioma is the most common solid tumor of the adult brain, with high lethality and poor prognosis. Hence, identifying novel and reliable biomarkers can be advantageous for diagnosing and treating glioma. Several galectins encoded by LGALS genes have recently been reported to participate in the development and progression of various tumors; however, their detailed role in glioma progression remains unclear. Herein, we analyzed the expression and survival curves of all LGALS across 2,217 patients with glioma using The Cancer Genome Atlas (TCGA), Chinese Glioma Genome Atlas (CGGA), and Rembrandt databases. By performing multivariate Cox analysis, we built a survival model containing LGALS1, LGALS3, LGALS3BP, LGALS8, and LGALS9 using TCGA database. The prognostic power of this panel was assessed using CGGA and Rembrandt datasets. ESTIMATE and CIBERSORT algorithms confirmed that patients in high-risk groups exhibited significant stromal and immune cell infiltration, immunosuppression, mesenchymal subtype, and isocitrate dehydrogenase 1 (IDH1) wild type. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), CancerSEA, and Gene Set Enrichment Analysis (GSEA) showed that pathways related to hypoxia, epithelial-to-mesenchymal transition (EMT), stemness, and inflammation were enriched in the high-risk group. To further elucidate the function of LGALS in glioma, we performed immunohistochemical staining of tissue microarrays (TMAs), Western blotting, and cell viability, sphere formation, and limiting dilution assays following lentiviral short hairpin RNA (shRNA)-mediated LGALS knockdown. We observed that LGALS expression was upregulated in gliomas at both protein and mRNA levels. LGALS could promote the stemness maintenance of glioma stem cells (GSCs) and positively correlate with M2-tumor-associated macrophages (TAMs) infiltration. In conclusion, we established a reliable survival model for patients with glioma based on LGALS expression and revealed the essential roles of LGALS genes in tumor growth, immunosuppression, stemness maintenance, pro-neural to mesenchymal transition, and hypoxia in glioma.
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Project description:Glioma is the most common type of primary brain tumors. After standard treatment regimen (surgical section, radiotherapy and chemotherapy), the average survival time remains merely around 14 months for glioblastoma (grade IV glioma). Recent immune therapy targeting to the immune inhibitory checkpoint axis, i.e., programmed cell death protein 1 (PD-1) and its ligand PD-L1 (i.e., CD274 or B7-H1), has achieved breakthrough in many cancers but still not in glioma. PD-L1 is considered a major prognostic biomarker for immune therapy in many cancers, with anti-PD-1 or anti-PD-L1 antibodies being used. However, the expression and subcellular distribution of PD-L1 in glioma cells exhibits great variance in different studies, severely impairing PD-L1's value as therapeutic and prognostic biomarker in glioma. The role of PD-L1 in modulating immune therapy is complicated. In addition, endogenous PD-L1 plays tumorigenic roles in glioma development. In this review, we summarize PD-L1 mRNA expression and protein levels detected by using different methods and antibodies in human glioma tissues in all literatures, and we evaluate the prognostic value of PD-L1 in glioma. We also summarize the relationships between PD-L1 and immune cell infiltration in glioma. The mechanisms regulating PD-L1 expression and the oncogenic roles of endogenous PD-L1 are discussed. Further, the therapeutic results of using anti-PD-1/PD-L1 antibodies or PD-L1 knockdown are summarized and evaluated. In summary, current results support that PD-L1 is not only a prognostic biomarker of immune therapy, but also a potential therapeutic target for glioma.
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Project description:Various histologic classification systems have been proposed as prognostic factors for gastric cancer. We assessed the prognostic value of Goseki classification as well as the TNM staging system, histological tumour grading, Lauren, WHO, Goseki and Siewert classifications in 100 patients with cardia carcinoma undergoing curative surgery. Two patients were lost at follow-up. The median time of follow-up in the remaining patients was 32.9 months after surgery (range: 0.1-142.1 months). No differences in survival rates were observed according to tumour grading, Lauren or WHO histologic or Siewert topographical classification. No differences were found according to Goseki classes, when considering either the mucin content of the carcinoma (types I and III vs II and IV) or the differentiation grade (types I and II vs III and IV). Multivariate analysis showed that the only lymph node positivity was a significant predictor of survival: 7.2% of patients with, but 41.5% of those without nodal involvement were alive after five years (P=0.0001). In conclusion, we found no prognostic role for Goseki or the traditional histological indexes, while the TNM staging system and particularly lymph node positivity were the main predictors of survival in patients with cardia adenocarcinoma.
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