Coexpression and expression quantitative trait loci analyses of the angiogenesis gene-gene interaction network in prostate cancer

Abstract

Background Prostate cancer (PCa) shows a substantial clinical heterogeneity. The existing risk classification for PCa prognosis based on clinical factors is not sufficient. Although some biomarkers for PCa aggressiveness have been identified, their underlying functional mechanisms are still unclear. We previously reported a gene-gene interaction network associated with PCa aggressiveness based on single nucleotide polymorphism (SNP)-SNP interactions in the angiogenesis pathway. The goal of this study is to investigate potential functional evidence of the involvement of the genes in this gene-gene interaction network. Methods A total of 11 angiogenesis genes were evaluated. The crosstalks among genes were examined through coexpression and expression quantitative trait loci (eQTL) analyses. The study population is 352 Caucasian PCa patients in the Cancer Genome Atlas (TCGA) study. The pairwise coexpressions among the genes of interest were evaluated using the Spearman coefficient. The eQTL analyses were tested using the Kruskal-Wallis test. Results Among all within gene and 55 possible pairwise gene evaluations, 12 gene pairs and one gene (MMP16) showed strong coexpression or significant eQTL evidence. There are nine gene pairs with a strong correlation (Spearman correlation ≥0.6, P<1×10−13). The top coexpressed gene pairs are EGFR-SP1 (r=0.73), ITGB3-HSPG2 (r=0.71), ITGB3-CSF1 (r=0.70), MMP16-FBLN5 (r=0.68), ITGB3-MMP16 (r=0.65), ITGB3-ROBO1 (r=0.62), CSF1-HSPG2 (r=0.61), CSF1-FBLN5 (r=0.6), and CSF1-ROBO1 (r=0.60). One cis-eQTL in MMP16 and five trans-eQTLs (MMP16-ESR1, ESR1-ROBO1, CSF1-ROBO1, HSPG2-ROBO1, and FBLN5-CSF1) are significant with a false discovery rate q value less than 0.2. Conclusions These findings provide potential biological evidence for the gene-gene interactions in this angiogenesis network. These identified interactions between the angiogenesis genes not only provide information for PCa etiology mechanism but also may serve as integrated biomarkers for building a risk prediction model for PCa aggressiveness.

Interactions ofPVT1andCASC11on Prostate Cancer Risk in African Americans

“…These three genes (CASC11, MYC, and PVT1) are physically close to each other, and their interactions associated with prostate cancer risk remain unknown, especially for AA men. It is suggested that SNP-SNP interactions have a large impact on revealing the mechanism of complex diseases (29)(30)(31)(32)(33)(34). Individual SNP effects are well known to have low prediction power despite of the fact that large numbers of GWAS SNPs have been identified.…”

Alcohol Intake and Alcohol–SNP Interactions Associated with Prostate Cancer Aggressiveness

“…ROBO1 acts like a natural inhibitor of metastasis; therefore, ROBO1 is considered as a potential cancer therapeutic target [ 58 ]. We previously reported several SNP–SNP interactions between R0BO1 and MMP16 associated PCa aggressiveness, and the biological function of the ROBO1–MMP16 interactions was supported by gene expression results [ 21 , 59 ]. ROBO1 is one gene enriched in the axon guidance pathway, significantly associated with alcoholism and alcohol addiction [ 60 ].…”

The Role of the Metzincin Superfamily in Prostate Cancer Progression: A Systematic-Like Review

“…A single study reported that MT2-MMP is downregulated in PrCa [ 38 ], but in contrast, MT3-MMP expression was increased and correlated with enhanced aggressiveness/metastatic potential [ 38 , 166 , 167 , 172 ]. Likewise, MT6-MMP expression was generally observed to be increased in PrCa [ 155 , 165 ], including one study that indicated a correlation with the PrCa grade [ 115 ].…”