PURPOSE: This study was conducted to characterize gene expression profile of survival in patients with surgically curable gastric cancer by using an in-house membrane microarray and developing a survival prediction model. MATERIALS AND METHODS: Data of cDNA microarrays were obtained from 18 pairs of cancerous and noncancerous gastric tissues. Nine patients who survived > 30 months were identified as good survival, and the other nine, who survived < 12 months, were identified as poor survival. Supervised analysis was performed to identify a gene expression profile by good and poor survival. Semiquantitative reverse transcriptase polymerase chain reaction (RT-PCR) was used to confirm the microarray data in 10 patients with sufficient RNA. Using these 10 patients and another 10 patients selected randomly from 40 newly enrolled patients as the training group, the RT-PCR status of the confirmed genes was used for predicting good versus poor survival. Finally, the prediction model was tested in the remaining 30 newly enrolled gastric cancer patients. RESULTS: A survival prediction model consisting of three genes (CD36, SLAM, PIM-1) was developed. This model could correctly predict poor or good survival in 23 (76.7%) of 30 newly enrolled patients, and yielded a specificity of 80% and a sensitivity of 73.3%. The survival rate of the patients predicted to have good survival was significantly higher than that of those predicted to have poor survival in the test group as a whole (N = 30; P = .00531) and in stage III patients (n = 16; P = .04467). CONCLUSION: The semiquantitative RT-PCR gene expression profiling of three genes extracted from microarray study can accurately predict surgery-related outcome in gastric cancer patients.
PURPOSE: This study was conducted to characterize gene expression profile of survival in patients with surgically curable gastric cancer by using an in-house membrane microarray and developing a survival prediction model. MATERIALS AND METHODS: Data of cDNA microarrays were obtained from 18 pairs of cancerous and noncancerous gastric tissues. Nine patients who survived > 30 months were identified as good survival, and the other nine, who survived < 12 months, were identified as poor survival. Supervised analysis was performed to identify a gene expression profile by good and poor survival. Semiquantitative reverse transcriptase polymerase chain reaction (RT-PCR) was used to confirm the microarray data in 10 patients with sufficient RNA. Using these 10 patients and another 10 patients selected randomly from 40 newly enrolled patients as the training group, the RT-PCR status of the confirmed genes was used for predicting good versus poor survival. Finally, the prediction model was tested in the remaining 30 newly enrolled gastric cancerpatients. RESULTS: A survival prediction model consisting of three genes (CD36, SLAM, PIM-1) was developed. This model could correctly predict poor or good survival in 23 (76.7%) of 30 newly enrolled patients, and yielded a specificity of 80% and a sensitivity of 73.3%. The survival rate of the patients predicted to have good survival was significantly higher than that of those predicted to have poor survival in the test group as a whole (N = 30; P = .00531) and in stage III patients (n = 16; P = .04467). CONCLUSION: The semiquantitative RT-PCR gene expression profiling of three genes extracted from microarray study can accurately predict surgery-related outcome in gastric cancerpatients.
Authors: Benjamin Balluff; Sandra Rauser; Stephan Meding; Mareike Elsner; Cedrik Schöne; Annette Feuchtinger; Christoph Schuhmacher; Alexander Novotny; Uta Jütting; Giuseppina Maccarrone; Hakan Sarioglu; Marius Ueffing; Herbert Braselmann; Horst Zitzelsberger; Roland M Schmid; Heinz Höfler; Matthias P Ebert; Axel Walch Journal: Am J Pathol Date: 2011-10-18 Impact factor: 4.307