Identification of PTPRR and JAG1 as key genes in castration-resistant prostate cancer by integrated bioinformatics methods

Abstract

To identify novel genes in castration-resistant prostate cancer (CRPC), we downloaded three microarray datasets containing CRPC and primary prostate cancer in Gene Expression Omnibus (GEO). R packages affy and limma were performed to identify differentially expressed genes (DEGs) between primary prostate cancer and CRPC. After that, we performed functional enrichment analysis including gene ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG) pathway. In addition, protein-protein interaction (PPI) analysis was used to search for hub genes. Finally, to validate the significance of these genes, we performed survival analysis. As a result, we identified 53 upregulated genes and 58 downregulated genes that changed in at least two datasets. Functional enrichment analysis showed significant changes in the positive regulation of osteoblast differentiation pathway and aldosteroneregulated sodium reabsorption pathway. PPI network identified hub genes like cortactin-binding protein 2 (CTTNBP2), Rho family guanosine triphosphatase (GTPase) 3 (RND3), protein tyrosine phosphatase receptor-type R (PTPRR), Jagged1 (JAG1), and lumican (LUM). Based on PPI network analysis and functional enrichment analysis, we identified two genes (PTPRR and JAG1) as key genes. Further survival analysis indicated a relationship between high expression of the two genes and poor prognosis of prostate cancer. In conclusion, PTPRR and JAG1 are key genes in the CRPC, which may serve as promising biomarkers of diagnosis and prognosis of CRPC.

Absence of Scaffold Protein Tks4 Disrupts Several Signaling Pathways in Colon Cancer Cells

“…Significantly upregulated genes include PLAU (urokinase-type plasminogen activator) and its inhibitor, PAI1 (plasminogen activator inhibitor 1), AKT3, PRKN, and DUSP13 ( Figure 2 C). All of these genes have been implicated in cancer progression and metastasis, their overexpression generally resulting in more aggressive phenotypes [ 35 , 36 , 37 , 38 ] and apoptosis [ 39 ].…”

“…The glucose transporter SLC2A1 and other members of the protein family (Figures 2C and 3 and Supplementary Table S2) were strongly downregulated in the Tks4 Significantly upregulated genes include PLAU (urokinase-type plasminogen activator) and its inhibitor, PAI1 (plasminogen activator inhibitor 1), AKT3, PRKN, and DUSP13 (Figure 2C). All of these genes have been implicated in cancer progression and metastasis, their overexpression generally resulting in more aggressive phenotypes [35][36][37][38] and apoptosis [39].…”

Protein Tyrosine Phosphatase Receptor Type R (PTPRR) Reduces AChR Clustering by Dephosphorylating MuSK

“…In this study, by combining the PTP substrate-trapping strategy with immunoprecipitation, we identified PTPRR as the tyrosine phosphatases responsible for dephosphorylating MuSK. PTPRR is a subfamily of classical PTPs and is considered as a tumor suppressor regulating proliferation or differentiation of cancer cells including oral squamous cell carcinoma, cervical cancer, breast tumor, and colorectal carcinomas [27][28][29][30]. We identified Tyr754 as the possible site of MuSK dephosphorylation by PTPRR through generating Tyr to Phe triple mutations for the C-terminal residues 553/750/755 of MuSK.…”

Identification of UBE2C as hub gene in driving prostate cancer by integrated bioinformatics analysis

“…GSE3325, GSE46602, GSE69223 and GSE104749 gene expression profiles have been normalized and transformed into suitable matrix using Robust Multichip Average (RMA) included in affy package of R software (version 3.6.1) consisting of background adjustment, quantile normalization and summarization pre-processing [ 6 , 18 ]. Limma package was used to identify DEGs between primary prostate cancer and noncancerous prostate samples.…”

“…In this study, the combined score represented intermolecular interactions and the protein pairs with combined score > 0.4 were selected to construct the PPI network [ 21 ]. Nodes were drawn in different sizes and colors, which represented the node degree and the regulation (up or down), respectively [ 18 ]. The hub genes were calculated by different methods with cytohubba [ 22 ].…”