STUDY DESIGN: We determined the expression of molecular markers gamma glutamyl hydrolase (GGH), cyclin-dependent kinase inhibitor-3 (CDKN3), and chromobox homolog-7 (CBX7) using immunohistochemistry in OSCC clinical samples (n = 35). The intensity of staining was scored using a semiquantitative index (HSCORE). The association between clinicopathologic parameters and expression of molecular markers with ENE status was analyzed using chi-square test.
RESULTS: The number of positive nodes and the highest anatomic level of nodal involvement significantly correlated with ENE (P < .05). High GGH expression was significantly associated with ENE (P < .05), with an increased risk for ENE (odds ratio [OR] 9.9, 95% CI 1.08-91.47, P = .04), whereas no significant association was seen for CDKN3 and CBX7 expression with ENE. However, a trend toward significance was observed with a high level of CDKN3 and a low level of CBX7 expression with ENE.
CONCLUSIONS: Gamma glutamyl hydrolase offers potential as a predictor for ENE in OSCC, whereas the role of CDKN3 and CBX7 need to be validated in a larger sample.
METHODS: Sixteen computed tomography scan of SC patients (8 months-6 years old) were imported to Materialise Interactive Medical Image Control System (MIMICS) and Materialise 3-matics software. Three-dimensional (3D) OC models were fabricated, and linear measurements were obtained. Mathematical formulas were used for calculation of OC volume and surface area from the 3D model. The same measurements were obtained from the software and used as ground truth. Data normality was investigated before statistical analyses were performed. Wilcoxon test was used to validate differences of OC volume and surface area between 3D model and software.
RESULTS: The mean values for OC surface area for 3D model and MIMICS software were 103.19 mm2 and 31.27 mm2, respectively, whereas the mean for OC volume for 3D model and MIMICS software were 184.37 mm2 and 147.07 mm2, respectively. Significant difference was found between OC volume (P = 0.0681) and surface area (P = 0.0002) between 3D model and software.
CONCLUSION: Optic canal in SC is not a perfect conical frustum thus making 3D model measurement and mathematical formula for surface area and volume estimation not ideal. Computer software remains the best modality to gauge dimensional parameter and is useful to elucidates the relationship of OC and eye function as well as aiding intervention in SC patients.
Methods: Comparative proteomics profiling of serum samples from OSCC patients, oral potentially malignant disorder (OPMD) patients, and healthy individuals were performed using two-dimensional gel electrophoresis (2-DE) coupled with mass spectrometry (MS) (n = 60) and bioinformatics analysis. The enzyme-linked immunosorbent assay (ELISA) (n = 120) and immunohistochemistry (IHC) (n = 70) were used to confirm our findings.
Results: The 2-DE analysis revealed that 20 differentially expressed proteins were detected in OPMD and OSCC (p
Methods: The effect of palbociclib was evaluated in a panel of well-characterized OSCC cell lines by cell proliferation assays and further confirmed by in vivo evaluation in xenograft models. PIK3CA-mutant isogenic cell lines were used to investigate the effect of PIK3CA mutation towards palbociclib response.
Results: We demonstrated that 80% of OSCC cell lines are sensitive to palbociclib at sub-micromolar concentrations. Consistently, palbociclib was effective in controlling tumor growth in mice. We identified that palbociclib-resistant cells harbored mutations in PIK3CA. Using isogenic cell lines, we showed that PIK3CA mutant cells are less responsive to palbociclib as compared to wild-type cells with concurrent upregulation of CDK2 and cyclin E1 protein levels. We further demonstrated that the combination of a PI3K/mTOR inhibitor (PF-04691502) and palbociclib completely controlled tumor growth in mice.
Conclusions: This study demonstrated the potency of palbociclib in OSCC models and provides a rationale for the inclusion of PIK3CA testing in the clinical evaluation of CDK4/6 inhibitors and suggests combination approaches for further clinical studies.
METHODS: This retrospective case-control study involves 790 cases of cancers of the oral cavity and 450 controls presenting with non-malignant oral diseases, recruited from seven hospital-based centres nationwide. Data on risk habits (smoking, drinking, chewing) were obtained using a structured questionnaire via face-to-face interviews. Multiple logistic regression was used to determine association between risk habits and oral cancer risk; chi-square test was used to assess association between risk habits and ethnicity. Population attributable risks were calculated for all habits.
RESULTS: Except for alcohol consumption, increased risk was observed for all habits; the highest risk was for smoking + chewing + drinking (aOR 22.37 95% CI 5.06, 98.95). Significant ethnic differences were observed in the practice of habits. The most common habit among Malays was smoking (24.2%); smoking + drinking were most common among Chinese (16.8%), whereas chewing was the most prevalent among Indians (45.2%) and Indigenous people (24.8%). Cessation of chewing, smoking and drinking is estimated to reduce cancer incidence by 22.6%, 8.5% and 6.9%, respectively.
CONCLUSION: Ethnic variations in the practice of oral cancer risk habits are evident. Betel quid chewing is the biggest attributable factor for this population.