Identification of Stable Regulatory Modules Using Gene Co-expression Networks
Date Issued
2012
Date
2012
Author(s)
Ho, Chia-Chuan
Abstract
To understand the mechanisms of gene regulations, we usually use microarray technology to detect levels of gene expressions and analyze these microarray data. Traditionally, statistical approaches are implemented to select genes differentially expressed in the experimental data. However, for the same research topic, researchers discover almost different genes through different datasets. These results indicate no stability across datasets to construct a general regulatory model. To overcome this difficulty, currently the analysis has been turned into the gene set based solutions: pathway analysis and network analysis. Pathway analysis interprets transcriptomic data based on prior knowledge and pre-defined gene sets with dysregulations. On the other hand, network analysis identifies the gene-gene interactions without prior knowledge and searches for modules associated with phenotypes under research.
In this study, we utilize the Weighted Gene Co-expression Network Analysis (WGCNA) to construct scale-free network for exploring highly correlated gene sets. From our lung adenocarcinoma training datasets, network analysis actually identifies regulatory modules. These selected modules could be categorized to be several functions: cytoskeletal construction, cell cycle regulation and immunodeficiency. Speaking of immune related modules, one module has been proved to be specific to lung cancer survival prediction, and it is annotated as B-cell receptor (BCR) signaling related module. The hub genes of BCR signaling related module has been predicted to be commonly regulated by transcription factor Oct-1. Based on our results and previous studies, we tried to propose a regulatory model concerning to lung carcinogenesis.
In conclusion, these data indicate network analysis could help us construct stable regulatory network across datasets without prior knowledge, and the selected gene sets are biologically functional to suggest reliable research targets.
Subjects
microarray
pathway analysis
network analysis
transcriptomic data
scale-free network
SDGs
Type
thesis
File(s)![Thumbnail Image]()
Loading...
Name
ntu-101-R99945005-1.pdf
Size
23.32 KB
Format
Adobe PDF
Checksum
(MD5):ba8151effefdcdbc63d0adbb2f8797bc