Abstract 2781: Genetic variations in angiogenesis-related genes in prostate cancer aggressiveness
Authors: Lin Hui-Yi , Park Hyun , Park Jong , Tsai Ya-Yu 
Published: June 2012

Prostate cancer is the most common cancer and the second leading cause of cancer death in American men. Clinicians usually face difficulties in distinguishing indolent and aggressive tumors at the time of prostate cancer diagnosis. Thus, there is an urgent need for finding biomarkers to predict prostate cancer prognosis in order to select a suitable treatment plan for prostate cancer patients. Angiogenesis, the development of new blood vessels, has been shown to be associated with Gleason score, tumor stage, progression, metastasis and survival in prostate cancer patients.

We used 1,151 prostate cancer cases (492 non-aggressive and 659 aggressive) obtained from the Cancer Genetic Markers of Susceptibility (CGEMS) prostate cancer genome-wide data set. This objective of this study is to evaluate single nucleotide polymorphisms (SNPs) in the angiogenesis-related genes associated with prostate cancer aggressiveness. A total of 2,653 SNPs in 161 angiogenesis-related genes in the nonsex chromosomes were evaluated after excluding two SNPs, which were not followed the Hardy-Weinberg equilibrium in the control group. A patient with a Gleason score &lt; 7 and stage &lt; III was categorized into the non-aggressive group; while a patient with a Gleason score &gt; 7 or stage &gt; III was categorized into the aggressive group. The associations between prostate cancer aggressiveness and individual SNPs with different inheritance models (dominant, recessive, log-additive or genotypic model) were evaluated using logistic regression models. For each SNP, the minimum p-value among all testing inheritance models was selected.

A total of 287 SNPs had raw-p values less than 0.05. Of the 287 SNPs, 89 SNPs had FDR q value less than 10%. Because the individual SNP analyses only considered one SNP associated with the outcome, we applied Random Forests (RF) analysis, a machine learning method, to explore important SNPs associated with prostate cancer aggressiveness though SNP-SNP interactions. RF provides the variable importance rank, which takes interactions into consideration. The top two SNPs in the univariate rank are rs1477908 (OR = 0.65, 95%CI=0.52-0.81, p=0.00017, log-additive model) and rs2616488 (OR=0.70, 95%CI=0.58-0.85, p=0.0002, log-additive model). Both SNPs were in the MMP16 gene and are also in the top one and five in the RF rank, respectively. Among top 10 SNPs in the RF ranking, 3 SNPs were in the FBLN5 gene. The RF (univariate) rank for these three SNPs (rs2498852, rs929608, and rs2052204) were 3 (8), 4 (31) and 9 (72). These data show that the impact of the SNPs in the FBLN5 gene was partially through interactions. Previous studies reported that the MMP16 and FBLN5 have different regulation in prostate cancer. This study suggests variants in angiogenesis-related genes, especially MMP16 and FBLN5, appear to influence prostate cancer aggressiveness.

Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 102nd Annual Meeting of the American Association for Cancer Research; 2011 Apr 2-6; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2011;71(8 Suppl):Abstract nr 2781. doi:10.1158/1538-7445.AM2011-2781