Differential Expression of EZH2 and H3K27me3 in Oral Verrucous Carcinoma and Oral Verrucous Hyperplasia

Enhancer of zeste homolog 2 (EZH2), a component of the polycomb repressive complex 2 that catalyzes trimethylation of H3K27 (H3K27me3), has been shown to promote tumor development and progression. Expression of EZH2 is associated with cell cycle regulation and cell proliferation in various neoplasms. Oral verrucous hyperplasia (OVH) and Oral verrucous carcinoma (OVC) are rare entities and share several clinical and histopathologic features. Problems distinguishing these lesions are added by a lack of adjacent normal tissue of the biopsy samples and poorly oriented tissue sections. The aim of this study was to investigate the expression of EZH2 and H3K27me3 in OVH and OVC and comparing the expression with normal oral mucosa and oral squamous cell carcinoma (OSCC). Seventy-eight samples, including 25 cases of OVC, 8 cases of OVH, 35 cases of OSCC and 10 cases of normal oral mucosa, were retrieved and submitted for immunohistochemical staining. The results demonstrated that the mean labeling indices (LIs) of EZH2 and H3K27me3 expression were highest in OSCC, followed by the OVC, OVH, and normal mucosa. Statistical differences in EZH2 LI were observed among these lesions whereas H3K27me3 LI was significantly different among OSCC, OVH and normal mucosa. EZH2 LI was found to have a sensitivity of 72.00% and specificity of 87.50% in distinguishing OVH from OVC, and a sensitivity of 57.14% and specificity of 84.00% in distinguishing OVC from OSCC. A positive correlation between EZH2 and H3K27me3 expression was significantly found in OVC but not in OVH and OSCC. These findings highlight the involvement of epigenetic regulation by EZH2-mediated H3K27me3 in the pathogenesis of OVH and OVC, and EZH2 expression indicates disease progression of these verrucous lesions. Diagnostic test analysis further suggests that EZH2 may be used as an additional test for differentiating OVH from OVC in questionable cases.Electronic Supplementary MaterialThe online version of this article (10.1007/s12105-020-01209-0) contains supplementary material, which is available to authorized users.     

Assessment of the confidence interval in the multivariable normal tissue complication probability model for predicting radiation-induced liver disease in primary liver cancer

We developed a confidence interval-(CI) assessing model in multivariable normal tissue complication probability (NTCP) modeling for predicting radiation-induced liver disease (RILD) in primary liver cancer patients using clinical and dosimetric data. Both the mean NTCP and difference in the mean NTCP (ΔNTCP) between two treatment plans of different radiotherapy modalities were further evaluated and their CIs were assessed. Clinical data were retrospectively reviewed in 322 patients with hepatocellular carcinoma (n = 215) and intrahepatic cholangiocarcinoma (n = 107) treated with photon therapy. Dose–volume histograms of normal liver were reduced to mean liver dose (MLD) based on the fraction size-adjusted equivalent uniform dose. The most predictive variables were used to build the model based on multivariable logistic regression analysis with bootstrapping. Internal validation was performed using the cross-validation leave-one-out method. Both the mean NTCP and the mean ΔNTCP with 95% CIs were calculated from computationally generated multivariate random sets of NTCP model parameters using variance–covariance matrix information. RILD occurred in 108/322 patients (33.5%). The NTCP model with three clinical and one dosimetric parameter (tumor type, Child–Pugh class, hepatitis infection status and MLD) was most predictive, with an area under the receiver operative characteristics curve (AUC) of 0.79 (95% CI 0.74–0.84). In eight clinical subgroups based on the three clinical parameters, both the mean NTCP and the mean ΔNTCP with 95% CIs were able to be estimated computationally. The multivariable NTCP model with the assessment of 95% CIs has potential to improve the reliability of the NTCP model-based approach to select the appropriate radiotherapy modality for each patient.