MED15, transforming growth factor beta 1 (TGF-β1), FcγRIII (CD16), and HNK-1 (CD57) are prognostic biomarkers of oral squamous cell carcinoma

Abstract

Owing to the high incidence and mortality of oral squamous cell carcinoma (OSCC), knowledge of its diagnostic and prognostic factors is of significant value. The biomarkers 'CD16, CD57, transforming growth factor beta 1 (TGF-β1), and MED15' can play crucial roles in tumorigenesis, and hence might contribute to diagnosis, prognosis, and treatment. Since there was no previous study on MED15 in almost all cancers, and since the studies on diagnostic/prognostic values of the other three biomarkers were a few in OSCC (if any) and highly controversial, this study was conducted. Biomarker expressions in all OSCC tissues and their adjacent normal tissues available at the National Tumor Bank (n = 4 biomarkers × [48 cancers + 48 controls]) were estimated thrice using qRT-PCR. Diagnostic values of tumors were assessed using receiver-operator characteristic (ROC) curves. Factors contributing to patients' survival over 10 years were assessed using multiple Cox regressions. ROC curves were used to estimate cut-off points for significant prognostic variables (α = 0.05). Areas under the curve pertaining to diagnostic values of all markers were non-significant (P > 0.15). Survival was associated positively with tumoral upregulation of TGF-β1 and downregulation of CD16, CD57, and MED15. It was also associated positively with younger ages, lower histological grades, milder Jacobson clinical TNM stages (and lower pathological Ns), smaller and thinner tumors, and surgery cases not treated with incisional biopsy (Cox regression, P < 0.05). The cut-off point for clinical stage -as the only variable with a significant area under the curve- was between the stages 2 and 3. Increased TGF-β1 and reduced CD16, CD57, and MED15 expressions in the tumor might independently favor the prognosis. Clinical TNM staging might be one of the most reliable prognostic factors, and stages above 2 can predict a considerably poorer prognosis.

Figure 1

Boxplots presenting medians, quartiles, minima, and maxima for ΔCt of the four biomarkers in tumoral and benign tissues.

Figure 2

ROC curves computed based on sensitivity and specificity of cancer determination (from normal tissue) using ΔCt values.

Figure 3

ROC curves of the variables contributing to the survival.

Figure 4

Cumulative survival plots for biomarker expressions, drawn using the Kaplan-Meier function.

Figure 5

Cumulative survival plots for the TNM stage dichotomized into mild and severe stages.