A systems biology approach to the global analysis of transcription factors in colorectal cancer

Abstract

Background: Biological entities do not perform in isolation, and often, it is the nature and degree of interactions among numerous biological entities which ultimately determines any final outcome. Hence, experimental data on any single biological entity can be of limited value when considered only in isolation. To address this, we propose that augmenting individual entity data with the literature will not only better define the entity's own significance but also uncover relationships with novel biological entities.To test this notion, we developed a comprehensive text mining and computational methodology that focused on discovering new targets of one class of molecular entities, transcription factors (TF), within one particular disease, colorectal cancer (CRC).

Methods: We used 39 molecular entities known to be associated with CRC along with six colorectal cancer terms as the bait list, or list of search terms, for mining the biomedical literature to identify CRC-specific genes and proteins. Using the literature-mined data, we constructed a global TF interaction network for CRC. We then developed a multi-level, multi-parametric methodology to identify TFs to CRC.

Results: The small bait list, when augmented with literature-mined data, identified a large number of biological entities associated with CRC. The relative importance of these TF and their associated modules was identified using functional and topological features. Additional validation of these highly-ranked TF using the literature strengthened our findings. Some of the novel TF that we identified were: SLUG, RUNX1, IRF1, HIF1A, ATF-2, ABL1, ELK-1 and GATA-1. Some of these TFs are associated with functional modules in known pathways of CRC, including the Beta-catenin/development, immune response, transcription, and DNA damage pathways.

Conclusions: Our methodology of using text mining data and a multi-level, multi-parameter scoring technique was able to identify both known and novel TF that have roles in CRC. Starting with just one TF (SMAD3) in the bait list, the literature mining process identified an additional 116 CRC-associated TFs. Our network-based analysis showed that these TFs all belonged to any of 13 major functional groups that are known to play important roles in CRC. Among these identified TFs, we obtained a novel six-node module consisting of ATF2-P53-JNK1-ELK1-EPHB2-HIF1A, from which the novel JNK1-ELK1 association could potentially be a significant marker for CRC.

Figure 1

Methodology for identifying global transcription factor-interactome and important transcription factors in CRC. Depicts the overall methodology used to prioritize the TFs: (1) Data collection from peer reviews; (2) Discovery of associations using BioMAP (literature augmented data); (3) Validation of BioMAP associations using Gene Ontology distance and protein-protein interactions; (4) Construction of the global TF interaction network; (5) Ranking of TFs using multi-level, multi-parametric using: (i) weighted/un-weighted prioritization schema, (ii) hypergeometric associations, and (iii) Modules; and (6) Validation of TFs by pathway analysis.

Figure 2

Transcription Factor Interaction network. The red nodes indicate transcription factors while yellow represents the remaining proteins.

Figure 3

The novel, highly-scored functional module identified shows the association of ELK-1:JNK1 and EPHB2:HIF1A.

Figure 4

A Ranking comparison between the Bait list pathways and Literature Augmented Data pathways. B: p-value comparison between the Bait List pathway and Literature Augmented Data pathways.

Figure 5

Functional groups and associated transcription factors. The centermost transcription factors are associated with multiple functional groups. The size of the functional group represents the relative number of pathways and transcription factors associated with it.