Classification of Bladder Cancer Patients via Penalized LinearDiscriminant Analysis
Asian Pacific journal of cancer prevention : APJCP(2017)
Abstract
Objectives: In order to identify genes with the greatest contribution to bladder cancer, we proposed a sparse model
making the best discrimination from other patients. Methods: 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
bladder cancer in Shiraz city, Southern Iran. Real time-PCR was carried out using SYBR Green and for each of the 22
target genes 2-Δct 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. Results: Using penalized linear
discriminant analysis led to elimination of 13 less important genes. Considering the simultaneous effects of 22 genes
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.
Conclusions: The penalized discriminant method can be considered as appropriate for classifying bladder cancer cases
and searching for important biomarkers.
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Key words
Bladder cancer,classification,gene expression,discriminant analysis,penalized method
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