CoGA: An R Package to Identify Differentially Co-Expressed Gene Sets by Analyzing the Graph Spectra.

Gene set analysis aims to identify predefined sets of functionally related genes that are differentially expressed between two conditions. Although gene set analysis has been very successful, by incorporating biological knowledge about the gene sets and enhancing statistical power over gene-by-gene analyses, it does not take into account the correlation (association) structure among the genes. In this work, we present CoGA (Co-expression Graph Analyzer), an R package for the identification of groups of differentially associated genes between two phenotypes. The analysis is based on concepts of Information Theory applied to the spectral distributions of the gene co-expression graphs, such as the spectral entropy to measure the randomness of a graph structure and the Jensen-Shannon divergence to discriminate classes of graphs. The package also includes common measures to compare gene co-expression networks in terms of their structural properties, such as centrality, degree distribution, shortest path length, and clustering coefficient. Besides the structural analyses, CoGA also includes graphical interfaces for visual inspection of the networks, ranking of genes according to their "importance" in the network, and the standard differential expression analysis. We show by both simulation experiments and analyses of real data that the statistical tests performed by CoGA indeed control the rate of false positives and is able to identify differentially co-expressed genes that other methods failed.

Differential expression of ID4 and its association with TP53 mutation, SOX2, SOX4 and OCT-4 expression levels.

Inhibitor of DNA Binding 4 (ID4) is a member of the helix-loop-helix ID family of transcription factors, mostly present in the central nervous system during embryonic development, that has been associated with TP53 mutation and activation of SOX2. Along with other transcription factors, ID4 has been implicated in the tumorigenic process of astrocytomas, contributing to cell dedifferentiation, proliferation and chemoresistance. In this study, we aimed to characterize the ID4 expression pattern in human diffusely infiltrative astrocytomas of World Health Organization (WHO) grades II to IV of malignancy (AGII-AGIV); to correlate its expression level to that of SOX2, SOX4, OCT-4 and NANOG, along with TP53 mutational status; and to correlate the results with the clinical end-point of overall survival among glioblastoma patients. Quantitative real time PCR (qRT-PCR) was performed in 130 samples of astrocytomas for relative expression, showing up-regulation of all transcription factors in tumor cases. Positive correlation was found when comparing ID4 relative expression of infiltrative astrocytomas with SOX2 (r = 0.50; p<0.005), SOX4 (r = 0.43; p<0.005) and OCT-4 (r = 0.39; p<0.05). The results from TP53 coding exon analysis allowed comparisons between wild-type and mutated status only in AGII cases, demonstrating significantly higher levels of ID4, SOX2 and SOX4 in mutated cases (p<0.05). This pattern was maintained in secondary GBM and further confirmed by immunohistochemistry, suggesting a role for ID4, SOX2 and SOX4 in early astrocytoma tumorigenesis. Combined hyperexpression of ID4, SOX4 and OCT-4 conferred a much lower (6 months) median survival than did hypoexpression (18 months). Because both ID4 alone and a complex of SOX4 and OCT-4 activate SOX2 transcription, it is possible that multiple activation of SOX2 impair the prognosis of GBM patients. These observational results of associated expression of ID4 with SOX4 and OCT-4 may be used as a predictive factor of prognosis upon further confirmation in a larger GBM series.