Interaction among variant vascular endothelial growth factor (VEGF) and its receptor in relation to prostate cancer risk

Abstract

Background: Prostate cancer (PCa) incidence and mortality are disproportionately high among African-American (AA) men. Its detection and perhaps its disparities could be improved through the identification of genetic susceptibility biomarkers within essential biological pathways. Interactions among highly variant genes, central to angiogenesis, may modulate susceptibility for prostate cancer, as previous demonstrated. This study evaluates the interplay among three highly variant genes (i.e., IL-10, TGFbetaR-1, VEGF), their receptors and their influence on PCa within a case-control study consisting of an under-served population.

Methods: This study evaluated single gene and joint modifying effects on PCa risk in a case-control study comprised of 859 AA men (193 cases and 666 controls) using TaqMan qPCR. Interaction among polymorphic IL-10, TGFbetaR-1 and VEGF was analyzed using conventional logistic regression analysis (LR) models, multi-dimensionality reduction (MDR) and interaction entropy graphs. Symbolic modeling allowed validation of gene-gene interaction findings identified by MDR.

Results: No significant single gene effects were demonstrated in relation to PCa risk. However, carriers of the VEGF 2482T allele had a threefold increase in the risk of developing aggressive PCa. The presence of VEGF 2482T combined with VEGFR IVS6 + 54 loci were highly significant for the risk of PCa based on MDR and symbolic modeling analyses. These findings were substantiated by 1,000-fold cross validation permutation testing (P = 0.04), respectively.

Conclusion: These findings suggest the inheritance of VEGF and VEGFR IVS6 + 54 sequence variants may jointly modify PCa susceptibility through their influence on angiogenesis. Larger sub-population studies are needed to validate these findings and evaluate whether the VEGF-VEGR axis may serve as predictors of disease prognosis and ultimately clinical response to available treatment strategies.

Fig. 1

Interaction Entropy model. This graphical model, describes the percent entropy that is explained by each Angiogenesis-Related SNP or a combination of two loci within our study population. Positive percent entropy indicates information gain or synergy. However, negative percent indicates redundancy or lack of information gain. Schematic coloration used in the visualization tools represents a continuum from synergy (i.e., non-additive) to redundancy. The colors range from red representing a high degree of synergy (positive information gain), orange a lesser degree, and gold representing independence and a midway point between synergy and redundancy. On the other hand, green represents redundancy. Notably, VEGF combined with VEGFR IVS6 + 54 provide more information in relation to prostate cancer risk relative to other individual locior SNP pairs.