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.

Role of ZNF143 and Its Association with Gene Expression Patterns, Noncoding Mutations, and the Immune System in Human Breast Cancer

“…Thus, it may suggest that ZNF143 could be one of the most significant genes that may control the breast cancer-related signaling pathways. There are a number of works where the research has concentrated on ZNF143; however, our work is distinctive in that it gives fundamentals related to ZNF143 in a straightforward manner [ 44 , 45 , 46 , 47 , 48 ]. Most biological functions in the MCF7 network are controlled by the PIK3R1 gene, and many other pathways, including apoptosis, ErbB, JAK–STAT, HIF-1, the control of the actin cytoskeleton, TNF, cancer-related pathways, TCR, BCR, and TLR signaling pathways, may also be related to breast cancer.…”

“…Once the connectivities are fetched, we then import the file with genes and connectivities into Cytoscape, where the nodes’ and edges’ color and styles are selected as per our interest. As metioned above, MATLAB was used for coding purpose, FunCoup to predict PPI networks, and Cytoscape for visualization and for more details these references could also be seen [ 20 , 24 , 25 , 40 , 41 , 42 , 43 , 44 ].…”

Impact of curcumin on ferroptosis‐related genes in colorectal cancer: Insights from in‐silico and in‐vitro studies

“…Additionally, KEGG was employed to further enhance knowledge by interpreting genomic, disease‐related, pathway, chemical, and systemic functional data. p Values below .05 were considered statistically significant 34 …”

Development and evaluation of a risk score model based on a WNT score gene-associated signature for predicting the clinical outcome and the tumour microenvironment of hepatocellular carcinoma

“…The WebGestaltR package in R (https://www.rdocumentation.org/packages/WebGestaltR/versions/0.4.6) was used in the biological functional enrichment analysis of the 2817 PCGs. 14,15…”

“…org/packages/WebGestaltR/versions/0.4.6) was used in the biological functional enrichment analysis of the 2817 PCGs. 14,15 Gene Set Enrichment Analysis (GSEA) GSEA (https://www.broadlnstitute.org/gsea/) was applied to identify the biological states and gene expression patterns according to all candidate gene sets in the Hallmark database. 16 The coxph function in R (https://www.…”