[Clinicopathological features and prognostic implications of EGFR/c-Met gene copy number and protein expression in non-small cell lung cancer].

OBJECTIVE: To investigate the gene copy number and protein expression of EGFR/c-Met, so as to explore the relationship between them and the clinicopathological features and prognosis in non-small cell lung cancer (NSCLC) patients.
METHODS: The expression of EGFR/c-Met protein was detected by immunohistochemical staining in NSCLC tissues of 61 cases. The level of EGFR/c-Met gene copy number was detected by fluorescent RT-PCR.
RESULTS: The positive rates of EGFR and c-Met protein expression were 77.5% and 57.4%, respectively. These rates in well/moderately differentiated NSCLC tissues were 61.5% and 38.5%, all significantly lower than the data in poorly differentiated tissues (P < 0.05). The relative EGFR and c-Met gene copy number was 0.22 ± 0.22 and 0.20 ± 0.21 in smokers, respectively, all significantly higher than that in non-smokers (P < 0.05). The gene copy number of EGFR and c-Met in adenocarcinoma was 0.24 ± 0.26 and 0.23 ± 0.25, respectively, all higher than the data in squamous cell carcinoma (P < 0.05). There was a significant correlation in regard to the EGFR and c-Met protein expression, EGFR and c-Met gene copy number, and the protein expression vs. the gene copy number only in EGFR (P < 0.05). But there was no significant correlation between the c-Met protein expression and gene copy number (P = 0.259). There was a statistical significance between the postoperational median survival times (MST) of low EGFR gene copy number (48 months) and the high EGFR gene copy number (36 months) patients (P = 0.039). Similarly, there was also a significant difference between the MST of the low and high c-Met gene copy number patients (44 and 31 months, P = 0.022).
CONCLUSION: There is a correlation between the EGFR and c-Met protein expression and the differentiation of NSCLC. The relative gene copy number is correlated with pathologic types and smoking of NSCLC patients, and it can be used in the prediction of prognosis.