The Role of 22 Genes Expression in Bladder Cancer by Adaptive LASSO

Abstract

Background: Genetic expression has been frequently considered as an efficient method for early diagnosis of cancer. In this study, we examined the simultaneous effect of 22 genes on contribution to bladder cancer. Objectives: Since these 22 genes are known as the most important risk factors in many cancers, we aimed to investigate them as potential effective genes in bladder cancer. Methods: The data consist of 25 patients with bladder cancer (the case group) and 23 matched healthy individuals as a control group. Univariate analysis was performed and differences between two groups were analyzed through the independent T-test. A multivariate gene expression model was implemented using the least absolute shrinkage and selection operator (LASSO) and Adaptive LASSO regression. Standard error of coefficients was obtained using the bootstrap method. We used two methods for classification and compared areas under the curve (AUC), using receiver operating characteristic (ROC) curve. Results: Independent T-test showed that 11 genes had a significant difference between the two groups. Also multivariate analysis using the LASSO revealed that 12 genes have a significant effect on bladder cancer and adaptive lasso regression represented SDF1, CTLA-4, Her2 and IL-23 genes as the most effective genes. The AUC for LASSO and Adaptive LASSO were 0.71 and 0.89, respectively which was statistically significant (P = 0.009). Our multivariable results for SDF1, CTLA-4 and IL-23 confirm the findings of many studies in this field. Conclusions: Among all genes were examined, SDF1, CTLA-4, Her2 and IL-23 which were selected by the two methods has the greatest contribution to bladder cancer.