Abstract

Background: Prostate cancer is a global health issue. Usually, men with metastatic disease will progress to castration-resistant prostate cancer (CRPC). We aimed to identify the differentially expressed genes (DEGs) in tumor samples from non-castrated and castrated men from LNCaP Orthotopic xenograft models of prostate cancer and to study the mechanisms of CRPC. Material/Methods: In this work, GSE46218 containing 4 samples from non-castrated men and 4 samples from castrated men was downloaded from Gene Expression Omnibus. We identified DEGs using limma Geoquery in R, the Robust Multi-array Average (RMA) method in Bioconductor, and Bias methods, followed by constructing an integrated regulatory network involving DEGs, miRNAs, and TFs using Cytoscape. Then, we analyzed network motifs of the integrated gene regulatory network using FANMOD. We selected regulatory modules corresponding to network motifs from the integrated regulatory network by Perl script. We preformed gene ontology (GO) and pathway enrichment analysis of DEGs in the regulatory modules using DAVID. Results: We identified total 443 DEGs. We built an integrated regulatory network, found three motifs (motif 1, motif 2 and motif 3), and got two function modules (module 1 corresponded to motif 1, and module 2 corresponded to motif 2). Several GO terms (such as regulation of cell proliferation, positive regulation of macromolecule metabolic process, phosphorylation, and phosphorus metabolic process) and two pathways (pathway in cancer and Melanoma) were enriched. Furthermore, some significant DEGs (such as CAV1, LYN, FGFR3 and FGFR3) were related to CPRC development. Conclusions: These genes might play important roles in the development and progression of CRPC.

Keywords: Algorithms, Computational Biology, Disease Progression, Gene Expression Profiling, Genes, Neoplasm, Oligonucleotide Array Sequence Analysis, Phenotype, Prognosis, Prostatic Neoplasms, Castration-Resistant - genetics