Background/Aim: Understanding of the molecular events associated with progression and survival differences in patients with lower-grade gliomas (LGGs) is still unclear. The comparison of findings across studies using different datasets and methods is essential for a new molecular-based classification system. The aim of the study was to identify biomarkers for prognostic classification of patients with LGGs, and furthermore to lay a foundation for future development of targeted therapies for LGGs. Patients and Methods: Using information-theoretic and statistical approaches, we analyzed mRNA expression data for 18,413 genes from LGG samples in order to identify candidate biomarkers for survival. The candidate genes were then evaluated for their potential as prognostic biomarkers using multivariable Cox regression analyses that adjusted for the effects of age and grade. Results: WEE1, EMP3, E2F7, CD58 and NSUN7 genes were identified as candidate biomarkers of LGGs and their high expression was associated with significantly shorter survival. The hazard ratios for mortality were 5.02 (95% CI=3.40-7.40) for WEE1, for EMP3, for E2F7, for ) for NSUN7. In addition, the expression pattern of these genes, associated with shorter survival in LGGs, was also observed in glioblastoma multiforme. Conclusion: Identification of genes associated with poor outcomes will provide insights into novel biological mechanisms that may lead to improvement in progression and survival for patients with LGGs.

Generalized marginal homogeneity, Kurtosis, Marginal homogeneity, Moment, Ordered category, Skewness, Square contingency table, 62H17, C39,

Likelihood ratio statistic, Marginal point-symmetry, Quasi point-symmetry, Separability, Square contingency table,

Square contingency tables are a special case commonly used in various fields to analyze categorical data. Although several analysis methods have been developed to examine marginal homogeneity (MH) in these tables, existing measures are single-summary ones. To date, a visualization approach has yet to be proposed to intuitively depict the results of MH analysis. Current measures used to assess the degree of departure from MH are based on entropy such as the Kullback-Leibler divergence and do not satisfy distance postulates. Hence, the current measures are not conducive to visualization. Herein we present a measure utilizing the Matusita distance and introduce a visualization technique that employs sub-measures of categorical data. Through multiple examples, we demonstrate the meaningfulness of our visualization approach and validate its usefulness to provide insightful interpretations.

No abstract