ABSTRACT
The relationship between inflammation and cancer is well established in several tumor types, including bladder cancer. We performed an association study between 886 inflammatory-gene variants and bladder cancer risk in 1,047 cases and 988 controls from the Spanish Bladder Cancer (SBC)/EPICURO Study. A preliminary exploration with the widely used univariate logistic regression approach did not identify any significant SNP after correcting for multiple testing. We further applied two more comprehensive methods to capture the complexity of bladder cancer genetic susceptibility: Bayesian Threshold LASSO (BTL), a regularized regression method, and AUC-Random Forest, a machine-learning algorithm. Both approaches explore the joint effect of markers. BTL analysis identified a signature of 37 SNPs in 34 genes showing an association with bladder cancer. AUC-RF detected an optimal predictive subset of 56 SNPs. 13 SNPs were identified by both methods in the total population. Using resources from the Texas Bladder Cancer study we were able to replicate 30% of the SNPs assessed. The associations between inflammatory SNPs and bladder cancer were reexamined among non-smokers to eliminate the effect of tobacco, one of the strongest and most prevalent environmental risk factor for this tumor. A 9 SNP-signature was detected by BTL. Here we report, for the first time, a set of SNP in inflammatory genes jointly associated with bladder cancer risk. These results highlight the importance of the complex structure of genetic susceptibility associated with cancer risk.
Subject(s)
Bayes Theorem , Biomarkers, Tumor/genetics , Inflammation Mediators/analysis , Inflammation/genetics , Polymorphism, Single Nucleotide/genetics , Smoking/genetics , Urinary Bladder Neoplasms/etiology , Adult , Aged , Aged, 80 and over , Algorithms , Artificial Intelligence , Case-Control Studies , Female , Follow-Up Studies , Genetic Predisposition to Disease , Humans , Male , Middle Aged , Prognosis , Risk Factors , Smoking/adverse effects , Texas , Young AdultABSTRACT
Abstract DNA methylation is one of the best-known epigenetic modifications in mammals. The alteration of DNA methylation patterns has been found to be related to many diseases, including cancer. It is well-known that during carcinogenesis, a site-specific DNA hypermethylation and a global DNA hypomethylation take place. This overall loss of DNA methylation has been proposed as a valid biomarker for cancer. Given its medical utility, in recent years it has become apparent that there is a need to develop methods for the analysis of DNA methylation using different approaches: global, locus-specific, or genome-wide. Here we review some of these techniques and discuss their potential clinical utility.
