BACKGROUND: Esophageal adenocarcinoma (EAC) is a growing problem with a rapidly rising incidence and carries a poor prognosis. We aimed to develop a glycolysis-related gene signature to predict the prognostic outcome of patients with EAC. RESULTS: Five genes (CLDN9, GFPT1, HMMR, RARS and STMN1) were correlated with prognosis of EAC patients. Patients were classified into high-risk and low-risk groups calculated by Cox regression analysis, based on the five gene signature risk score. The five-gene signature was an independent biomarker for prognosis and patients with low risk scores showed better prognosis. Nomogram incorporating the gene signature and clinical prognostic factors was effective in predicting the overall survival. CONCLUSION: An innovative identified glycolysis-related gene signature and an effective nomogram reliably predicted the prognosis of EAC patients. METHODS: The Cancer Genome Atlas database was investigated for the gene expression profile of EAC patients. Glycolytic gene sets difference between EAC and normal tissues were identified via Gene set enrichment analysis (GSEA). Univariate and multivariate Cox analysis were utilized to construct a prognostic gene signature. The signature was evaluated by receiver operating characteristic curves and Kaplan-Meier curves. A prognosis model integrating clinical parameters with the gene signature was established with nomogram.
BACKGROUND:Esophageal adenocarcinoma (EAC) is a growing problem with a rapidly rising incidence and carries a poor prognosis. We aimed to develop a glycolysis-related gene signature to predict the prognostic outcome of patients with EAC. RESULTS: Five genes (CLDN9, GFPT1, HMMR, RARS and STMN1) were correlated with prognosis of EAC patients. Patients were classified into high-risk and low-risk groups calculated by Cox regression analysis, based on the five gene signature risk score. The five-gene signature was an independent biomarker for prognosis and patients with low risk scores showed better prognosis. Nomogram incorporating the gene signature and clinical prognostic factors was effective in predicting the overall survival. CONCLUSION: An innovative identified glycolysis-related gene signature and an effective nomogram reliably predicted the prognosis of EAC patients. METHODS: The Cancer Genome Atlas database was investigated for the gene expression profile of EAC patients. Glycolytic gene sets difference between EAC and normal tissues were identified via Gene set enrichment analysis (GSEA). Univariate and multivariate Cox analysis were utilized to construct a prognostic gene signature. The signature was evaluated by receiver operating characteristic curves and Kaplan-Meier curves. A prognosis model integrating clinical parameters with the gene signature was established with nomogram.
Authors: Mohammed Adil Butt; Hayley Pye; Rehan J Haidry; Dahmane Oukrif; Saif-U-Rehman Khan; Ignazio Puccio; Michael Gandy; Halla W Reinert; Ellie Bloom; Mohammed Rashid; Gokhan Yahioglu; Mahendra P Deonarain; Rifat Hamoudi; Manuel Rodriguez-Justo; Marco R Novelli; Laurence B Lovat Journal: Oncotarget Date: 2017-04-11
Authors: Xiaodun Li; Hayley E Francies; Maria Secrier; Juliane Perner; Ahmad Miremadi; Núria Galeano-Dalmau; William J Barendt; Laura Letchford; Genevieve M Leyden; Emma K Goffin; Andrew Barthorpe; Howard Lightfoot; Elisabeth Chen; James Gilbert; Ayesha Noorani; Ginny Devonshire; Lawrence Bower; Amber Grantham; Shona MacRae; Nicola Grehan; David C Wedge; Rebecca C Fitzgerald; Mathew J Garnett Journal: Nat Commun Date: 2018-07-30 Impact factor: 14.919