Downregulation of 14-3-3σ Correlates with Multistage Carcinogenesis and Poor Prognosis of Esophageal Squamous Cell Carcinoma

Aims: The asymptomatic nature of early-stage esophageal squamous cell carcinoma (ESCC) results in late presentation and consequent dismal prognosis This study characterized 14-3-3 sigma protein expression in the multi-stage development of ESCC and determined its correlation with clinical features and prognosis. Materials and Methods: Western blot was used to examine 14-3-3 sigma protein expression in normal esophageal epithelium (NEE), low grade intraepithelial neoplasia (LGIN), high grade intraepithelial neoplasia (HGIN), ESCC of TNM I to IV stage and various esophageal epithelial cell lines with different biological behavior. Immunohistochemistry was used to estimate 14-3-3 sigma protein in 110 biopsy samples of NEE, LGIN or HGIN and in 168 ESCC samples all of whom had follow-up data. Support vector machine (SVM) was used to develop a classifier for prognosis. Results: 14-3-3 sigma decreased progressively from NEE to LGIN, to HGIN, and to ESCC. Chemoresistant sub-lines of EC9706/PTX and EC9706/CDDP showed high expression of 14-3-3 sigma protein compared with non-chemoresistant ESCC cell lines and immortalized NEC. Furthermore, the downregulation of 14-3-3 sigma correlated significantly with histological grade (P = 0.000) and worse prognosis (P = 0.004). Multivariate Cox regression analysis indicated that 14-3-3 sigma protein (P = 0.016) and T stage (P = 0.000) were independent prognostic factors for ESCC. The SVM ESCC classifier comprising sex, age, T stage, histological grade, lymph node metastasis, clinical stage and 14-3-3 sigma, distinguished significantly lower-and higher-risk ESCC patients (91.67% vs. 3.62%, P = 0.000). Conclusions: Downregulation of 14-3-3 sigma arises early in the development of ESCC and predicts poor survival, suggesting that 14-3-3 sigma may be a biomarker for early detection of high-risk subjects and diagnosis of ESCC. Our seven-feature SVM classifier for ESCC prognosis may help to inform clinical decisions and tailor individual therapy.