Dataset Information

A 5-gene prognostic nomogram predicting survival probability of glioblastoma patients.

ABSTRACT: BACKGROUND:Glioblastoma (GBM) remains the most biologically aggressive subtype of gliomas with an average survival of 10 to 12 months. Considering that the overall survival (OS) of each GBM patient is a key factor in the treatment of individuals, it is meaningful to predict the survival probability for GBM patients newly diagnosed in clinical practice. MATERIAL AND METHODS:Using the TCGA dataset and two independent GEO datasets, we identified genes that are associated with the OS and differentially expressed between GBM tissues and the adjacent normal tissues. A robust likelihood-based survival modeling approach was applied to select the best genes for modeling. After the prognostic nomogram was generated, an independent dataset on different platform was used to evaluate its effectiveness. RESULTS:We identified 168 differentially expressed genes associated with the OS. Five of these genes were selected to generate a gene prognostic nomogram. The external validation demonstrated that 5-gene prognostic nomogram has the capability of predicting the OS of GBM patients. CONCLUSION:We developed a novel and convenient prognostic tool based on five genes that exhibited clinical value in predicting the survival probability for newly diagnosed GBM patients, and all of these five genes could represent potential target genes for the treatment of GBM. The development of this model will provide a good reference for cancer researchers.

SUBMITTER: Wang L

PROVIDER: S-EPMC6456771 | biostudies-literature | 2019 Apr

REPOSITORIES: biostudies-literature

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A 5-gene prognostic nomogram predicting survival probability of glioblastoma patients.

Wang Lingchen L Yan Zhengwei Z He Xiaona X Zhang Cheng C Yu Huiqiang H Lu Quqin Q

Brain and behavior 20190311 4

<h4>Background</h4>Glioblastoma (GBM) remains the most biologically aggressive subtype of gliomas with an average survival of 10 to 12 months. Considering that the overall survival (OS) of each GBM patient is a key factor in the treatment of individuals, it is meaningful to predict the survival probability for GBM patients newly diagnosed in clinical practice.<h4>Material and methods</h4>Using the TCGA dataset and two independent GEO datasets, we identified genes that are associated with the OS ...[more]

PMID: 30859746

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Project description:Background:Medulloblastoma is the common pediatric malignant tumor with poor prognosis in cerebellum. However, MB is always with clinical heterogeneity. To provide patients with more clinically beneficial treatment strategies, there is a pressing need to develop a new prognostic prediction model as a supplement to the prediction outcomes of clinical judgment. Materials and Methods:Four datasets of mRNA expression and clinical data were downloaded from gene expression omnibus (GEO) database. Differentially expressed genes (DEGs) were identified and functionally enriched among GSE50161, GSE74195, GSE86574. Then we used STRING and Cytoscape to constructed and analyze protein-protein interaction network (PPI) and hub genes. Univariate cox regression analysis was performed to identify overall survival-related hub genes in an unique dataset from GSE85217 as train cohort. Lasso Cox regression model was used to construct the prognostic gene signature. Time-dependent receiver operating characteristic (ROC), Kaplan-Meier curve, univariate and multivariate Cox regression analysis were used to assess the prognostic capacity of the twelve-gene signature. A unique dataset from GSE85217 was downloaded to further validate the results. Finally, we established the nomogram by using the gene signature and validated it with ROC curve. Gene set enrichment analysis (GSEA) was carried out to further investigate its potential molecular mechanism. Besides, the twelve genes expression at the mRNA and protein levels was validated using external database such as Oncomine, cBioportal and HPA, respectively. Results:A twelve-gene signature comprising FOXM1, NEK2, CCT2, ACTL6A, EIF4A3, CCND2, ABL1, SYNCRIP, ITGB1, NRXN2, ENAH, and UMPS was established to predict overall survival of medulloblastoma. The ROC curve showed good performance in survival prediction in both the train cohort and the validation cohort. The twelve-gene signature could stratify patients into a high risk and low risk group which had significantly different survival. Univariate and multivariate Cox regression revealed that the twelve-gene signature was an independent prognostic factor in medulloblastoma. Nomogram, which included twelve-gene signatures, was established and showed some clinical benefit. Conclusion:Our study identified a twelve-gene signature and established a prognostic nomogram that reliably predicts overall survival in medulloblastoma. The above results will help us to better analyze the pathogenesis and treatment of medulloblastoma in the future.

Project description:Background: Pancreatic cancer is highly lethal and aggressive with increasing trend of mortality in both genders. An effective prediction model is needed to assess prognosis of patients for optimization of treatment. Materials and Methods: Seven datasets of mRNA expression and clinical data were obtained from gene expression omnibus (GEO) database. Level 3 mRNA expression and clinicopathological data were obtained from The Cancer Genome Atlas pancreatic ductal adenocarcinoma (TCGA-PAAD) dataset. Differentially expressed genes (DEGs) between pancreatic tumor and normal tissue were identified by integrated analysis of multiple GEO datasets. Univariate and Lasso Cox regression analyses were applied to identify overall survival-related DEGs and establish a prognostic gene signature whose performance was evaluated by Kaplan-Meier curve, receiver operating characteristic (ROC), Harrell's concordance index (C-index) and calibration curve. GSE62452 and GSE57495 were used for external validation. Gene set enrichment analysis (GSEA) and tumor immunity analysis were applied to elucidate the molecular mechanisms and immune relevance. Multivariate Cox regression analysis was used to identify independent prognostic factors in pancreatic cancer. Finally, a prognostic nomogram was established based on the TCGA PAAD dataset. Results: A nine-gene signature comprising MET, KLK10, COL17A1, CEP55, ANKRD22, ITGB6, ARNTL2, MCOLN3, and SLC25A45 was established to predict overall survival of pancreatic cancer. The ROC curve and C-index indicated good performance of the nine-gene signature at predicting overall survival in the TCGA dataset and external validation datasets relative to classic AJCC staging. The nine-gene signature could classify patients into high- and low-risk groups with distinct overall survival and differentiate tumor from normal tissue. Univariate Cox regression revealed that the nine-gene signature was an independent prognostic factor in pancreatic cancer. The nomogram incorporating the gene signature and clinical prognostic factors was superior to AJCC staging in predicting overall survival. The high-risk group was enriched with multiple oncological signatures and aggressiveness-related pathways and associated with significantly lower levels of CD4+ T cell infiltration. Conclusion: Our study identified a nine-gene signature and established a prognostic nomogram that reliably predict overall survival in pancreatic cancer. The findings may be beneficial to therapeutic customization and medical decision-making.

Project description:ObjectivesTo develop a nomogram that could predict spontaneous stone passage (SSP) in patients presenting with acute ureteric colic who are suitable for conservative management.Subject/patientsA 2517 patient dataset was utilised from an international multi-centre cohort study (MIMIC, A Multi-centre Cohort Study Evaluating the role of Inflammatory Markers In Patients Presenting with Acute Ureteric Colic) of patients presenting with acute ureteric colic across 71 secondary care hospitals in the United Kingdom, Ireland, Australia, and New Zealand. Inclusion criteria mandated a non-contrast CT-KUB.MethodsSSP was defined as the 'absence of the need for intervention'. The model was developed using logistic regression and backwards selection (to achieve lowest AIC) in a subset from 2009-2015 (n=1728) and temporally validated on a subset from 2016-2017 (n=789).ResultsOf the 2517 patients, 1874 had SSP (74.5%). Mean age (±[SD]) was 47 (±14.7) years and 1892 were male (75.2%). At the end of the modelling process, gender: male (OR 0.8, 95%CI 0.64-1.01, p=0.07), neutrophil count (OR 1.03, 95%CI 1.00-1.06, p = 0.08), hydronephrosis (OR 0.79, 95%CI 0.59-1.05, p=0.1), hydroureter (OR 1.3, 95%CI 0.97-1.75, p =0.08), stone size >5-7mm (OR 0.2, 95%CI 0.16-0.25, p<0.0001), stone size >7mm (OR 0.11, 95%CI 0.08-0.15, p<0.001), middle ureter stone position (OR 0.59, 95%CI 0.43-0.81, p=0.001), upper ureter stone position (OR 0.31, 95%CI 0.25-0.39, p<0.001) ), medical expulsive therapy use (OR 1.36, 95%CI 1.1 - 1.67, p = 0.001), oral NSAID use (OR 1.3, 95%CI 0.99 - 1.71, p=0.06), and rectal NSAID use (OR1.17, 95%CI 0.9 - 1.53, p=0.24) remained. Concordance-statistic (C-statistic) was 0.77 (95%CI 0.75 - 0.80) and a nomogram was developed based on these.ConclusionThe presented nomogram is available to use as an online calculator via www.BURSTurology.com and could allow clinicians and patients to make a more informed decision on pursuing conservative management versus early intervention.

Project description:BackgroundDexamethasone is reported to induce both tumor-suppressive and tumor-promoting effects. The purpose of this study was to identify the genomic impact of dexamethasone in glioblastoma stem cell (GSC) lines and its prognostic value; furthermore, to identify drugs that can counter these side effects of dexamethasone exposure.MethodsWe utilized 3 independent GSC lines with tumorigenic potential for this study. Whole-genome expression profiling and pathway analyses were done with dexamethasone-exposed and control cells. GSCs were also co-exposed to dexamethasone and temozolomide. Risk scores were calculated for most affected genes, and their associations with survival in The Cancer Genome Atlas and Repository of Molecular Brain Neoplasia Data databases. In silico Connectivity Map analysis identified camptothecin as antagonist to dexamethasone-induced negative effects.ResultsPathway analyses predicted an activation of dexamethasone network (z-score: 2.908). Top activated canonical pathways included "role of breast cancer 1 in DNA damage response" (P=1.07E-04). GSCs were protected against temozolomide-induced apoptosis when coincubated with dexamethasone. Altered cellular functions included cell movement, cell survival, and apoptosis with z-scores of 2.815, 5.137, and -3.122, respectively. CCAAT/enhancer binding protein beta (CEBPB) was activated in a dose dependent manner specifically in slow-dividing "stem-like" cells. CEBPB was activated in dexamethasone-treated orthotopic tumors. Patients with high risk scores had significantly shorter survival. Camptothecin was validated as potential partial neutralizer of dexamethasone-induced oncogenic effects.ConclusionsDexamethasone exposure induces a genetic program and CEBPB expression in GSCs that adversely affects key cellular functions and response to therapeutics. High risk scores associated with these genes have negative prognostic value in patients. Our findings further suggest camptothecin as a potential neutralizer of adverse dexamethasone-mediated effects.

Dataset Information

A 5-gene prognostic nomogram predicting survival probability of glioblastoma patients.

Publications

A 5-gene prognostic nomogram predicting survival probability of glioblastoma patients.

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