An Approach for Systems-Level Understanding of Prostate Cancer from High-Throughput Data Integration to Pathway Modeling and Simulation

Abstract

To understand complex diseases, high-throughput data are generated at large and multiple levels. However, extracting meaningful information from large datasets for comprehensive understanding of cell phenotypes and disease pathophysiology remains a major challenge. Despite tremendous advances in understanding molecular mechanisms of cancer and its progression, current knowledge appears discrete and fragmented. In order to render this wealth of data more integrated and thus informative, we have developed a GECIP toolbox to investigate the crosstalk and the responsible genes'/proteins' connectivity of enriched pathways from gene expression data. To implement this toolbox, we used mainly gene expression datasets of prostate cancer, and the three datasets were GSE17951, GSE8218, and GSE1431. The raw samples were processed for normalization, prediction of differentially expressed genes, and the prediction of enriched pathways for the differentially expressed genes. The enriched pathways have been processed for crosstalk degree calculations for which number connections per gene, the frequency of genes in the pathways, sharing frequency, and the connectivity have been used. For network prediction, protein-protein interaction network database FunCoup2.0 was used, and cytoscape software was used for the network visualization. In our results, we found that there were enriched pathways 27, 45, and 22 for GSE17951, GSE8218, and GSE1431, respectively, and 11 pathways in common between all of them. From the crosstalk results, we observe that focal adhesion and PI3K pathways, both experimentally proven central for cellular output upon perturbation of numerous individual/distinct signaling pathways, displayed highest crosstalk degree. Moreover, we also observe that there were more critical pathways which appear to be highly significant, and these pathways are HIF1a, hippo, AMPK, and Ras. In terms of the pathways' components, GSK3B, YWHAE, HIF1A, ATP1A3, and PRKCA are shared between the aforementioned pathways and have higher connectivity with the pathways and the other pathway components. Finally, we conclude that the focal adhesion and PI3K pathways are the most critical pathways, and since for many other pathways, high-rank enrichment did not translate to high crosstalk degree, the global impact of one pathway on others appears distinct from enrichment.

Keywords: crosstalk; docking; enriched pathways; gene expression; high-throughput data; mathematical modeling and simulation; systems-level understanding.

Figure 1

GECIP (Gene set, Enrichment, and Crosstalk between the Inferred Pathways) workflow for pathway enrichment analysis and crosstalk degree calculation for the enriched pathways.

Figure 2

Pseudocode for carry out the entire work presented in this study.

Figure 3

GECIP pathway analysis: (a) total number of DEGs for three different datasets of prostate cancer, (b) pathways selected from different KEGG pathway classes, (c) total number of enriched KEGG pathways for three different datasets of prostate cancer, (d) p-values (the heat map has been plotted by converting p-values into −10 ∗ log10 of p-values (red: lowest p-value and gray: higher p-values and less than 0.05)) of 11 common enriched pathways.

Figure 4

$C{T}_{degree}$ analysis for the enriched pathways: Degree of crosstalk between the pathways for (a) GSE8218, (b) GSE1431, and (c) Grasso dataset. Different thickness of the edges represents the degree of crosstalk between the pathways (higher the higher the $C{T}_{degree}$, and lower thickness means lower $C{T}_{degree}$). Different edges colors are for clarity only.

Figure 5

Crosstalk between the common enriched pathways: (a) genes belonging (in yellow color) to the crosstalk (pathway–pathway interaction) network and total connectivities for each gene (bigger circle means higher connectivities), (b) heatmap to represent the corresponding crosstalk degree between the pathways, (c) pathway–pathway interaction network for common enriched pathways among all the three datasets and the pathway components (gene–gene associations), and (d) genes shared by the 11 pathways present in pathway–pathway interaction network and their degree of association within the crosstalk network for the corresponding genes.

Figure 6

Crosstalk between the common enriched pathways: Pathway–pathway interactions together with the corresponding genes. The hexagonal nodes with number 1–11 represent the pathways, and the genes with more than one color mean that they are shared by more than one pathway. The higher the size of circular nodes (genes), the higher the degree of connectivity.

Figure 7

Systems-level application, validation, and integration of high-throughput data: (a) A sketch for AKT signaling pathway components in case of prostate cancer, (b) kinetics of ppERK calculated by using mass-action kinetics and ODE approach in case of normal and prostate cancer signaling, (c) the quantification for immunohistochemistry data, (d) ppERK expression in normal human and cancer prostate biopsies, and (e) delta G representing the herbal drugs’ interactions with GSK3B, HIF1A, and YWHAE, which appear highly dominant based on high-throughput databased crosstalk result.