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Raeisi Shahraki H, Bemani P, Jalali M. Classification of Bladder Cancer Patients via Penalized Linear Discriminant Analysis. Asian Pac J Cancer Prev. 2017;18(5):1453-1457doi: 10.22034/APJCP.2017.18.5.1453.
Raeisi Shahraki, H., Bemani, P., & Jalali, M. (2017). Classification of Bladder Cancer Patients via Penalized Linear Discriminant Analysis. Asian Pacific journal of cancer prevention : APJCP, 18(5), 1453-1457. https://doi.org/10.22034/APJCP.2017.18.5.1453
Raeisi Shahraki, Hadi, et al. "Classification of Bladder Cancer Patients via Penalized Linear Discriminant Analysis." Asian Pacific journal of cancer prevention : APJCP vol. 18,5 (2017): 1453-1457. doi: https://doi.org/10.22034/APJCP.2017.18.5.1453
Raeisi Shahraki H, Bemani P, Jalali M. Classification of Bladder Cancer Patients via Penalized Linear Discriminant Analysis. Asian Pac J Cancer Prev. 2017 May 01;18(5):1453-1457. doi: 10.22034/APJCP.2017.18.5.1453. PMID: 28612601; PMCID: PMC5555561.
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Asian Pac J Cancer Prev. 2017 May 01;18(5):1453-1457. doi: 10.22034/APJCP.2017.18.5.1453.

Classification of Bladder Cancer Patients via Penalized Linear Discriminant Analysis.

Asian Pacific journal of cancer prevention : APJCP

Hadi Raeisi Shahraki, Peyman Bemani, Maryam Jalali

Affiliations

  1. Department of Biostatistics, School of Medicine, Shiraz University of Medical Sciences, Shiraz, Iran. Email: [email protected]

PMID: 28612601 PMCID: PMC5555561 DOI: 10.22034/APJCP.2017.18.5.1453

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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Keywords: Bladder cancer; classification; gene expression; discriminant analysis; penalized method

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