Date Published: March 13, 2013
Publisher: Hindawi Publishing Corporation
Author(s): Mario Flores, Tzu-Hung Hsiao, Yu-Chiao Chiu, Eric Y. Chuang, Yufei Huang, Yidong Chen.
Common microarray and next-generation sequencing data analysis concentrate on tumor subtype classification, marker detection, and transcriptional regulation discovery during biological processes by exploring the correlated gene expression patterns and their shared functions. Genetic regulatory network (GRN) based approaches have been employed in many large studies in order to scrutinize for dysregulation and potential treatment controls. In addition to gene regulation and network construction, the concept of the network modulator that has significant systemic impact has been proposed, and detection algorithms have been developed in past years. Here we provide a unified mathematic description of these methods, followed with a brief survey of these modulator identification algorithms. As an early attempt to extend the concept to new RNA regulation mechanism, competitive endogenous RNA (ceRNA), into a modulator framework, we provide two applications to illustrate the network construction, modulation effect, and the preliminary finding from these networks. Those methods we surveyed and developed are used to dissect the regulated network under different modulators. Not limit to these, the concept of “modulation” can adapt to various biological mechanisms to discover the novel gene regulation mechanisms.
With the development of microarray  and lately the next generation sequencing techniques , transcriptional profiling of biological samples, such as tumor samples [3–5] and samples from other model organisms, have been carried out in order to study sample subtypes at molecular level or transcriptional regulation during the biological processes [6–8]. While common data analysis methods employ hierarchical clustering algorithms or pattern classification to explore correlated genes and their functions, the genetic regulatory network (GRN) approaches were employed to scrutinize for dysregulation between different tumor groups or biological processes (see reviews [9–12]).
During the past years, many computational tools have been developed for regulation network construction, and then depending on the hypothesis, modulator concept can be tested and extracted. Here we will focus on modulator detection algorithms (MINDy, Mimosa, GEM, and Hermes). To introduce gene-gene interaction concept, we will also briefly discuss algorithms for regulation network construction (ARACNE) and ceRNA identification algorithm (MuTaMe).
Algorithms of utilizing modulator concept have been implemented in various software packages. Here we will discuss two new applications, MEGRA and TraceRNA, implemented in-house specifically to utilize the concept of differential correlation coefficients and ceRNAs to construct a modulated GRN with a predetermined modulator. In the case of MGERA, we chose estrogen receptor, ESR1, as the initial starting point, since it is one of the dominant and systemic factor in breast cancer; in the case of TraceRNA, we also chose gene ESR1 and its modulated gene network. Preliminary results of applications to TCGA breast cancer data are reported in the following 2 sections.
In this report, we attempt to provide a unified concept of modulation of gene regulation, encompassing earlier mRNA expression based methods and lately the ceRNA method. We expect the integration of ceRNA concept into the gene-gene interactions, and their modulator identification will further enhance our understanding in gene interaction and their systemic influence. Applications provided here also represent examples of our earlier attempt to construct modulated networks specific to breast cancer studies. Further investigation will be carried out to extend our modeling to provide a unified understanding of genetic regulation in an altered environment.