Tumor suppressor candidate 3 as a novel predictor for lymph node metastasis in lung cancer patients

Tumor suppressor candidate 3 (TUSC3) was recently identified as a potential tumor suppressor gene in several cancer types. However, no data are currently available regarding the expression of TUSC3 in lung cancer. The present study investigated the expression of TUSC3 in patients with lung cancer and determined its association with the clinicopathological parameters of the disease. Cytoplasmic TUSC3 expression was evaluated by immunohistochemistry on tissue microarray slides, which included 35 small cell lung cancer (SCLC) specimens, 80 squamous cell lung cancer specimens (SCC), 80 adenocarcinoma lung cancer (ADC) specimens and 37 normal lung tissue specimens. Analysis showed significantly reduced TUSC3 expression in the SCLC patients, but not in the ADC and SCC patients, as compared with the normal controls. Additionally, TUSC3 expression in the patients with a degree of differentiation of 1–2 (well-moderately differentiated) was significantly higher than that in patients with a differentiation degree of 3–4 (poorly differentiated-undifferentiated). Further analysis showed that TUSC3 expression levels were negatively correlated with the degree of differentiation in the ADC and SCC patients. Notably, a marked decrease in TUSC3 expression was identified in the patients who were lymph node metastasis-positive (LNM(+)) compared with patients who were LNM(−). Further analysis showed that significant differences in TUSC3 expression were identified among the different N stages (LNM status) in the SCLC, ADC and SCC patients. Correlation analysis also identified a negative correlation between TUSC3 expression and LNM in all three pathological types of lung cancer tested. Overall, these results indicated that a reduction in TUSC3 may be associated with a poorly-differentiated grade of lung cancer. Importantly, TUSC3 expression may be a useful predictor of LNM in lung cancer patients. A combined analysis of TUSC3 expression and the clinical variables will aid in predicting the incidence of LNM.