Layered signaling regulatory networks analysis of gene expression involved in malignant tumorigenesis of non-resolving ulcerative colitis via integration of cross-study microarray profiles

Abstract

Background: Ulcerative colitis (UC) was the most frequently diagnosed inflammatory bowel disease (IBD) and closely linked to colorectal carcinogenesis. By far, the underlying mechanisms associated with the disease are still unclear. With the increasing accumulation of microarray gene expression profiles, it is profitable to gain a systematic perspective based on gene regulatory networks to better elucidate the roles of genes associated with disorders. However, a major challenge for microarray data analysis is the integration of multiple-studies generated by different groups.

Methodology/principal findings: In this study, firstly, we modeled a signaling regulatory network associated with colorectal cancer (CRC) initiation via integration of cross-study microarray expression data sets using Empirical Bayes (EB) algorithm. Secondly, a manually curated human cancer signaling map was established via comprehensive retrieval of the publicly available repositories. Finally, the co-differently-expressed genes were manually curated to portray the layered signaling regulatory networks.

Results: Overall, the remodeled signaling regulatory networks were separated into four major layers including extracellular, membrane, cytoplasm and nucleus, which led to the identification of five core biological processes and four signaling pathways associated with colorectal carcinogenesis. As a result, our biological interpretation highlighted the importance of EGF/EGFR signaling pathway, EPO signaling pathway, T cell signal transduction and members of the BCR signaling pathway, which were responsible for the malignant transition of CRC from the benign UC to the aggressive one.

Conclusions: The present study illustrated a standardized normalization approach for cross-study microarray expression data sets. Our model for signaling networks construction was based on the experimentally-supported interaction and microarray co-expression modeling. Pathway-based signaling regulatory networks analysis sketched a directive insight into colorectal carcinogenesis, which was of significant importance to monitor disease progression and improve therapeutic interventions.

Figure 1. Robust multi-array average (RMA) analysis results of microarray data based on WebArray.

(A) Statistical analysis result plot for GSE4183 included M-A plot, M-B plot, M histogram and B statistics histogram. (B) Statistical analysis result plot for GSE37283 included M-A plot, M-B plot, M histogram and B statistics histogram. M: the log-differential expression ratio; A: the log-intensity of spot, a measure of overall brightness of spot; B: B statistics, the log-odds of differential expression.

Figure 2. Cross-study normalization and integration results of two separate microarrays based upon ArrayMining.

Figure 3. Signaling regulatory modules of human cancer signaling network generated by AllegroMCODE based on molecular complex detection (MCODE) algorithm.

A-H represented signaling regulatory networks 1–8. Circle dots in the networks corresponded to genes. Red represented high degree connectivity, whereas green stood for low degree connectivity.

Figure 4. Layered signaling regulatory networks driven by co-differently-expressed microarray genes involved in malignant transition of colorectal cancer from the benign chronic non-solving ulcerative colitis to the more aggressive one.

In the layered signaling regulatory networks, the size of each node was proportional to the degree. In addition, red, blue and green represented nodes stemmed from signaling networks 3, 6 and 7, respectively.

Figure 5. Heatmap of the over-represented biological processes and enrichment pathways using stats package in R environment.

For each figure, columns correspond to biological processes (A) or signaling pathways (B), and rows correspond to gene cluster category and subcellular localization. Expression values are logarithm of ratio value utilizing log transform data. Red and blue in each grid represented positive, while white represented null. (A) Significantly over-represented GO biological processes in differential cluster and layers. (B) Significantly enriched signaling pathways in differential cluster and layers. E: Extracellular. M: Membrane. C: Cytoplasm. N: Nucleus.