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Neoplasma Vol.64, No.1, p.22-31,2017 |
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| Title: Pathway crosstalk analysis in prostate cancer based on protein-protein network data | ||
| Author: H. Y. LI, N. JIN, Y. P. HAN, X. F. JIN | ||
| Abstract: Prostate cancer (PCa) is one of the major leading cause in men and no effective biomarkers or therapy have been approved for it to date. This study aimed to explore the molecular mechanisms and identify the potential molecular biomarkers of PCa. The microarray profile GSE38241 including 18 prostate cancer metastasis and 21 normal prostate samples was retrieved from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were identified by Limma. DEGs functions were investigated by Gene Ontology (GO) and pathway enrichment analysis. Moreover, protein-protein interaction (PPI) network of DEGs was constructed, followed by functional analysis of modules. Additionally, pathway crosstalk network was constructed by integrating PPI network and Kyoto encyclopedia of genes and genomes (KEGG) pathways. Totally, 334 up – and 703 down-regulated DEGs were identified. The functions of up-regulated DEGs were significantly enriched in GO terms of cell cycle phase and cell cycle process. While down-regulated DEGs mainly participated in actin filament-based process. Among these pathways in the pathway crosstalk network, T cell receptor signaling pathway, chemokine signaling pathways, endometrial cancer and glioma were found to play critical roles during PC progression. Cell division cycle 45 (CDC45), baculoviral IAP repeat containing 5 (BIRC5) and cell division cycle associated 5 (CDCA5) may be useful markers for predicting tumor metastasis and therapeutic targets for the treatment of PCa patients. Moreover, the pathway crosstalk network provides the groundwork that targeting multiple pathways might be more effective than targeting one pathway alone. |
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| Keywords: prostate cancer, differentially expressed genes, protein-protein interaction network, pathway crosstalk | ||
| Published online: 10-Jan-2017 | ||
| Year: 2017, Volume: 64, Issue: 1 | Page From: 22, Page To: 31 | |
| doi:10.4149/neo_2017_103 |
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