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G-cimp status prediction of glioblastoma samples using mRNA expression data.

ABSTRACT: Glioblastoma Multiforme (GBM) is a tumor with high mortality and no known cure. The dramatic molecular and clinical heterogeneity seen in this tumor has led to attempts to define genetically similar subgroups of GBM with the hope of developing tumor specific therapies targeted to the unique biology within each of these subgroups. Recently, a subset of relatively favorable prognosis GBMs has been identified. These glioma CpG island methylator phenotype, or G-CIMP tumors, have distinct genomic copy number aberrations, DNA methylation patterns, and (mRNA) expression profiles compared to other GBMs. While the standard method for identifying G-CIMP tumors is based on genome-wide DNA methylation data, such data is often not available compared to the more widely available gene expression data. In this study, we have developed and evaluated a method to predict the G-CIMP status of GBM samples based solely on gene expression data.

SUBMITTER: Baysan M

PROVIDER: S-EPMC3490960 | biostudies-literature | 2012

REPOSITORIES: biostudies-literature

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G-cimp status prediction of glioblastoma samples using mRNA expression data.

Baysan Mehmet M Bozdag Serdar S Cam Margaret C MC Kotliarova Svetlana S Ahn Susie S Walling Jennifer J Killian Jonathan K JK Stevenson Holly H Meltzer Paul P Fine Howard A HA

PloS one 20121106 11

Glioblastoma Multiforme (GBM) is a tumor with high mortality and no known cure. The dramatic molecular and clinical heterogeneity seen in this tumor has led to attempts to define genetically similar subgroups of GBM with the hope of developing tumor specific therapies targeted to the unique biology within each of these subgroups. Recently, a subset of relatively favorable prognosis GBMs has been identified. These glioma CpG island methylator phenotype, or G-CIMP tumors, have distinct genomic cop ...[more]

PMID: 23139755

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Project description:RNA N 6-methyladenosine (m6A) has emerged as an important epigenetic modification for its role in regulating the stability, structure, processing, and translation of RNA. Instability of m6A homeostasis may result in flaws in stem cell regulation, decrease in fertility, and risk of cancer. To this day, experimental detection and quantification of RNA m6A modification are still time-consuming and labor-intensive. There is only a limited number of epitranscriptome samples in existing databases, and a matched RNA methylation profile is not often available for a biological problem of interests. As gene expression data are usually readily available for most biological problems, it could be appealing if we can estimate the RNA methylation status from gene expression data using in silico methods. In this study, we explored the possibility of computational prediction of RNA methylation status from gene expression data using classification and regression methods based on mouse RNA methylation data collected from 73 experimental conditions. Elastic Net-regularized Logistic Regression (ENLR), Support Vector Machine (SVM), and Random Forests (RF) were constructed for classification. Both SVM and RF achieved the best performance with the mean area under the curve (AUC) = 0.84 across samples; SVM had a narrower AUC spread. Gene Site Enrichment Analysis was conducted on those sites selected by ENLR as predictors to access the biological significance of the model. Three functional annotation terms were found statistically significant: phosphoprotein, SRC Homology 3 (SH3) domain, and endoplasmic reticulum. All 3 terms were found to be closely related to m6A pathway. For regression analysis, Elastic Net was implemented, which yielded a mean Pearson correlation coefficient = 0.68 and a mean Spearman correlation coefficient = 0.64. Our exploratory study suggested that gene expression data could be used to construct predictors for m6A methylation status with adequate accuracy. Our work showed for the first time that RNA methylation status may be predicted from the matched gene expression data. This finding may facilitate RNA modification research in various biological contexts when a matched RNA methylation profile is not available, especially in the very early stage of the study.

Project description:BackgroundLate HIV diagnosis is detrimental both to the individual and to society. Strategies to improve early diagnosis of HIV must be a key health care priority. We examined whether nation-wide electronic registry data could be used to predict HIV status using machine learning algorithms.MethodsWe extracted individual level data from Danish registries and used algorithms to predict HIV status. We used various algorithms to train prediction models and validated these models. We calibrated the models to mimic different clinical scenarios and created confusion matrices based on the calibrated models.FindingsA total 4,384,178 individuals, including 4,350 with incident HIV, were included in the analyses. The full model that included all variables that included demographic variables and information on past medical history had the highest area under the receiver operating characteristics curves of 88·4% (95%CI: 87·5% - 89·4%) in the validation dataset. Performance measures did not differ substantially with regards to which machine learning algorithm was used. When we calibrated the models to a specificity of 99·9% (pre-exposure prophylaxis (PrEP) scenario), we found a positive predictive value (PPV) of 8·3% in the full model. When we calibrated the models to a sensitivity of 90% (screening scenario), 384 individuals would have to be tested to find one undiagnosed person with HIV.InterpretationMachine learning algorithms can learn from electronic registry data and help to predict HIV status with a fairly high level of accuracy. Integration of prediction models into clinical software systems may complement existing strategies such as indicator condition-guided HIV testing and prove useful for identifying individuals suitable for PrEP.FundingThe study was supported by funds from the Preben and Anne Simonsens Foundation, the Novo Nordisk Foundation, Rigshospitalet, Copenhagen University, the Danish AIDS Foundation, the Augustinus Foundation and the Danish Health Foundation.

Project description:Recently it was shown that gene expression signatures generated from DNA microarray analyses have promise as biomarkers of clinical outcome and that the molecular characteristics of tumors could be elucidated. Through this study, we have determined a high-risk signature for recurrence as a prognostic biomarker using formalin-fixed HNSCC tumors and tested the results to an independent data set obtained from fresh frozen tumors as a comparison. Also, we have shown the genes that are involved in epithelial to mesenchymal transition and nuclear factor-ĸB signaling deregulation are the most prominent molecular characteristics of the high-risk tumors. Forty samples including 34 formalin-fixed tissues and 6 matched frozen tissues from 29 HNSCC patients were analyzed for gene expression. The formalin-fixed tumors were classified based on their gene expression by intrinsic analysis and the intrinsic gene list was tested on the classification of previously published 60 frozen HNSCC tumors which highly correlated. Based on the molecular classification, a 75-gene list that is predictive of high-risk for recurrence was determined by training on the formalin-fixed tumor set and tested on the independent frozen tumor set. The difference in recurrence-free survival (RFS) between the high-risk vs. low-risk groups in the training and test sets were statistically significant (Log-rank test, p=0.002 and p=0.03, respectively). Also, the gene expression data was interrogated using Gene Set Enrichment Analysis to determine functional significance. The most significant sets of genes that are enriched in the high-risk tumors were genes involving epithelial to mesenchymal transition (EMT), NF-ĸB activation and cell adhesion. In conclusion, global gene expression analysis is feasible using formalin-fixed tissue, and the data from different sample preparations and array platforms can be reliably combined for analyses. The 75-gene list can be utilized as a prognostic biomarker of recurrence and the molecular characteristics of EMT and NF-ĸB activation can be targeted as the novel therapy in the identified high-risk patients. Keywords: FFPE, survival analysis

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G-cimp status prediction of glioblastoma samples using mRNA expression data.

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G-cimp status prediction of glioblastoma samples using mRNA expression data.

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