Xin Zhao1,2, Jiaxuan Zou3, Ziwei Wang4, Ge Li1,2, Yi Lei2,5,6. 1. Department of Urology, The Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan 646000, China. 2. Sichuan Clinical Research Center for Nephropathy, Luzhou, Sichuan 646000, China. 3. Fuzhou Medical College of Nanchang University, Fuzhou, Jiangxi Province 344100, China. 4. College of Life Sciences, University of Chinese Academy of Sciences, Beijing 100049, China. 5. Department of Endocrinology and Metabolism, The Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan 646000, China. 6. Cardiovascular and Metabolic Diseases Key Laboratory of Luzhou, Luzhou, Sichuan 646000, China.
Abstract
BACKGROUND: Gastric cancer (GC) is believed to be one of the most common digestive tract malignant tumors. The prognosis of GC remains poor due to its high malignancy, high incidence of metastasis and relapse, and lack of effective treatment. The constant progress in bioinformatics and molecular biology techniques has given rise to the discovery of biomarkers with clinical value to predict the GC patients' prognosis. However, the use of a single gene biomarker can hardly achieve the satisfactory specificity and sensitivity. Therefore, it is urgent to identify novel genetic markers to forecast the prognosis of patients with GC. MATERIALS AND METHODS: In our research, data mining was applied to perform expression profile analysis of mRNAs in the 443 GC patients from The Cancer Genome Atlas (TCGA) cohort. Genes associated with the overall survival (OS) of GC were identified using univariate analysis. The prognostic predictive value of the risk factors was determined using the Kaplan-Meier survival analysis and multivariate analysis. The risk scoring system was built in TCGA dataset and validated in an independent Gene Expression Omnibus (GEO) dataset comprising 300 GC patients. Based on the median of the risk score, GC patients were grouped into high-risk and low-risk groups. RESULTS: We identified four genes (GMPPA, GPC3, NUP50, and VCAN) that were significantly correlated with GC patients' OS. The high-risk group showed poor prognosis, indicating that the risk score was an effective predictor for the prognosis of GC patients. CONCLUSION: The signature consisting of four glycolysis-related genes could be used to forecast the GC patients' prognosis.
BACKGROUND: Gastric cancer (GC) is believed to be one of the most common digestive tract malignant tumors. The prognosis of GC remains poor due to its high malignancy, high incidence of metastasis and relapse, and lack of effective treatment. The constant progress in bioinformatics and molecular biology techniques has given rise to the discovery of biomarkers with clinical value to predict the GC patients' prognosis. However, the use of a single gene biomarker can hardly achieve the satisfactory specificity and sensitivity. Therefore, it is urgent to identify novel genetic markers to forecast the prognosis of patients with GC. MATERIALS AND METHODS: In our research, data mining was applied to perform expression profile analysis of mRNAs in the 443 GC patients from The Cancer Genome Atlas (TCGA) cohort. Genes associated with the overall survival (OS) of GC were identified using univariate analysis. The prognostic predictive value of the risk factors was determined using the Kaplan-Meier survival analysis and multivariate analysis. The risk scoring system was built in TCGA dataset and validated in an independent Gene Expression Omnibus (GEO) dataset comprising 300 GC patients. Based on the median of the risk score, GC patients were grouped into high-risk and low-risk groups. RESULTS: We identified four genes (GMPPA, GPC3, NUP50, and VCAN) that were significantly correlated with GC patients' OS. The high-risk group showed poor prognosis, indicating that the risk score was an effective predictor for the prognosis of GC patients. CONCLUSION: The signature consisting of four glycolysis-related genes could be used to forecast the GC patients' prognosis.
Authors: Aravind Subramanian; Pablo Tamayo; Vamsi K Mootha; Sayan Mukherjee; Benjamin L Ebert; Michael A Gillette; Amanda Paulovich; Scott L Pomeroy; Todd R Golub; Eric S Lander; Jill P Mesirov Journal: Proc Natl Acad Sci U S A Date: 2005-09-30 Impact factor: 11.205
Authors: Sherif El-Saadany; Taher El-Demerdash; Amal Helmy; Wael Wahid Mayah; Boshra El-Sayed Hussein; Mohammed Hassanien; Nehal Elmashad; Mahmoud Ali Fouad; Eman Ahmed Basha Journal: Asian Pac J Cancer Prev Date: 2018-03-27