Nomogram for risk prediction of malignant transformation in oral leukoplakia patients using combined biomarkers

Xianglan Zhang, Ki Yeol Kim, Zhenlong Zheng, Shadavlonjid Bazarsad, Jin Kim

Research output: Contribution to journalArticle

11 Citations (Scopus)

Abstract

Objective Squamous cell carcinomas (SCC) are the most common malignancies in the oral mucosa; these carcinomas have been preceded by potentially malignant oral disorders (PMODs), mostly oral leukoplakia (OL). No specific biomarker has been widely accepted for predicting the risk of malignant transformation of PMODs. The aim of this study was to develop an accurate prediction model for the malignant transformation of OL using clinical variables and candidate biomarkers. Materials and methods To achieve this goal, 10 candidate biomarkers that had previously been reported as useful molecules were investigated: P53, Ki-67, P16, β-catenin, c-jun, c-met, insulin like growth factor II mRNA-binding protein (IMP-3), cyclooxygenase (COX-2), podoplanin (PDPN) and carbonic anhydrase 9 (CA9). For this study, malignant transformed (n = 22, median interval of malignant conversion: 3.3 years) and untransformed (n = 138) OL specimens with median follow-up period of 11.3 years (range: 4.6–23.2 years) were immunohistochemically stained. Results Using univariate Cox regression analysis, all biomarkers were proven to be significant for predicting malignant transformation in OL. To reach the highest prediction accuracy, the repeated simulation was performed, revealing that the combination of P53 and CA9 with the clinical factors including age and degree of dysplasia achieved the highest prediction accuracy. We constructed a nomogram with the identified prognostic factors for predicting the 5-, 10-, and 15-year progression free survival of OL. Conclusions The proposed nomogram may be useful for the accurate and individual prediction of the transformation to SCC in OL patients and may help clinicians offer appropriate treatments and follow up.

Original languageEnglish
Pages (from-to)132-139
Number of pages8
JournalOral Oncology
Volume72
DOIs
Publication statusPublished - 2017 Sep

All Science Journal Classification (ASJC) codes

  • Oral Surgery
  • Oncology
  • Cancer Research

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