Classification of Bladder Cancer Patients via Penalized Linear Discriminant Analysis

Abstract

<br /> <strong><span style="font-size: small;">Objectives: </span></strong><span style="font-family: Times New Roman,Times New Roman; font-size: small;"><span style="font-family: Times New Roman,Times New Roman; font-size: small;">In order to identify genes with the greatest contribution to bladder cancer, we proposed a sparse model making the best discrimination from other patients. </span></span><strong><span style="font-size: small;">Methods: </span></strong><span style="font-family: Times New Roman,Times New Roman; font-size: small;"><span style="font-family: Times New Roman,Times New Roman; font-size: small;">In a cross-sectional study, 22 genes with a key role in most cancers were considered in 21 bladder cancer patients and 14 participants of the same age (± 3 years) without </span></span><span style="font-family: Times New Roman,Times New Roman; font-size: small;"><span style="font-family: Times New Roman,Times New Roman; font-size: small;">bladder cancer in Shiraz city, Southern Iran. Real time-PCR was carried out using SYBR Green and for each of the 22 </span></span><span style="font-family: Times New Roman,Times New Roman; font-size: small;"><span style="font-family: Times New Roman,Times New Roman; font-size: small;">target genes 2</span></span>^{<span style="font-family: Times New Roman,Times New Roman; font-size: xx-small;"><span style="font-family: Times New Roman,Times New Roman; font-size: xx-small;">-Δct </span></span><span style="font-family: Times New Roman,Times New Roman; font-size: small;"><span style="font-family: Times New Roman,Times New Roman; font-size: small;">as a quantitative index of gene expression was reported. We determined the most affective genes for the discriminant vector by applying penalized linear discriminant analysis using LASSO penalties. All the analyses were performed using SPSS version 18 and the penalized LDA package in R.3.1.3 software. </span></span><strong><span style="font-size: small;">Results: </span></strong><span style="font-family: Times New Roman,Times New Roman; font-size: small;"><span style="font-family: Times New Roman,Times New Roman; font-size: small;">Using penalized linear discriminant analysis led to elimination of 13 less important genes. Considering the simultaneous effects of 22 genes </span></span><span style="font-family: Times New Roman,Times New Roman; font-size: small;"><span style="font-family: Times New Roman,Times New Roman; font-size: small;">with important influence on many cancers, it was found that TGFβ, IL12A, Her2, MDM2, CTLA-4 and IL-23 genes had the greatest contribution in classifying bladder cancer patients with the penalized linear discriminant vector. The receiver operating characteristic (ROC) curve revealed that the proposed vector had good performance with minimal (only 3) mis- classification. The area under the curve (AUC) of our proposed test was 96% (95% CI: 83%- 100%) and sensitivity, specificity, positive and negative predictive values were 90.5%, 85.7%, 90.5% and 85.7%, respectively. </span></span><strong><span style="font-size: small;">Conclusions: </span></strong><span style="font-family: Times New Roman,Times New Roman; font-size: small;"><span style="font-family: Times New Roman,Times New Roman; font-size: small;">The penalized discriminant method can be considered as appropriate for classifying bladder cancer cases and searching for important biomarkers. </span></span>}