The prognostic value of gene expressions of ERCC1 and RRM1 in non-small cell lung cancer.

Abstract

e19071 Background: Non small cell lung cancer is the most common cause of death due to cancer in the world. The role of ERCC1 and RRM1 genes has been researched in prediction of lung cancer prognosis and response of patients to chemotheraphy. We aimed to evaluate the effects of ERCC1 and RRM1 gene expressions profiles on the prediction of the prognosis and the treatment on bronchoscopy specimens of advanced stage NSCLC patients. Methods: The levels of ERCC1 and RRM1 gene expressions were studied for 76 patients which were diagnosed by bronchoscopic biopsy parafin embedded tissue samples. The RRM1 and ERCC1 gene expression profiles were examined by RT-PCR method. Forty were diagnosed with squamous cell cancer, 24 with adenocarcinoma, 12 with NOS NSCLC. Thirty patients were chemotherapy native, 33 were received gemcitabine-cisplatin and 13 were received docetaxel- cisplatin doublets chemotherapy. Results: The mean age of the patients in the study was 59.8±9.3 (years ± SD). The levels of gene expressions of ERCC1 and RRM1 were found to be 2-9 (median 4.9), and 1.3-17.7 (median 6.8). No significant difference was detected between ERCC1 and RRM1 levels and age, gender, histologic type, ECOG, weight loss when the median gene expression levels were used as cut off value. Also, no significant difference was observed for survival analysis among the patients that have low ERCC1 mRNA level (p=0.41). The level of gene expression of ERCC1 was lower in patients with advanced stage (p=0.06). In addition, no significant difference was detected for the survival analysis within the patients that have the low and high RRM1 mRNA levels (p=0.43). When the survival analyses were evaluated between the patients who had chemotherapy and the ones who did not; no significant difference was detected according to ERCC1 and RRM1 levels. Conclusions: We conclude that the analysis of ERCC1 and RRM1 gene expression profiles based on bronchoscopy obtained samples appeares feasible, but the methodology and cut off points that was used to classify expression level as ‘high’ or ‘low’ would require further optimization. These two gene signature proposed for advanced stage NSCLC may not be useful, particularly not ready for clinical application.