Objective: Tumor heterogeneity renders identification of suitable biomarkers of gastric cancer(GC)challenging. Here, we aimed to identify prognostic genes of GC using computational analysis.Methods: We first used micr...Objective: Tumor heterogeneity renders identification of suitable biomarkers of gastric cancer(GC)challenging. Here, we aimed to identify prognostic genes of GC using computational analysis.Methods: We first used microarray technology to profile gene expression of GC and paired nontumor tissues from 198 patients. Based on these profiles and patients’ clinical information, we next identified prognostic genes using novel computational approaches. Phosphoglucose isomerase, also known as glucose-6-phosphate isomerase(GPI), which ranked first among 27 candidate genes, was further investigated by a new analytical tool namely enviro-geno-pheno-state(E-GPS) analysis. Suitability of GPI as a prognostic marker, and its relationship with physiological processes such as metabolism, epithelial-mesenchymal transition(EMT), as well as drug sensitivity were evaluated using both our own and independent public datasets.Results: We found that higher expression of GPI in GC correlated with prolonged survival of patients.Particularly, a combination of CDH2 and GPI expression effectively stratified the outcomes of patients with TNM stage Ⅱ/Ⅲ. Down-regulation of GPI in tumor tissues correlated well with depressed glucose metabolism and fatty acid synthesis, as well as enhanced fatty acid oxidation and creatine metabolism, indicating that GPI represents a suitable marker for increased probability of EMT in GC cells.Conclusions: Our findings strongly suggest that GPI acts as a novel biomarker candidate for GC prognosis,allowing greatly enhanced clinical management of GC patients. The potential metabolic rewiring correlated with GPI also provides new insights into studying the relationship between cancer metabolism and patient survival.展开更多
基金supported by grants from the Ministry of Science and Technology of the People’s Republic of China (No. SS2014AA020603)Beijing Municipal Administration of Hospitals Clinical Medicine Development of Special Funding Support (No. ZYLX201701)+3 种基金Beijing Municipal Science and Technology Commission (No. D1311 00005313010)the National Natural Science Foundation of China (No. 31520103905)the National High Technology Research and Development Program (“863” Program) of China (No. 2015AA020108)the Zhi-Yuan chair professorship start-up grant WF220103010 from Shanghai Jiao Tong University
文摘Objective: Tumor heterogeneity renders identification of suitable biomarkers of gastric cancer(GC)challenging. Here, we aimed to identify prognostic genes of GC using computational analysis.Methods: We first used microarray technology to profile gene expression of GC and paired nontumor tissues from 198 patients. Based on these profiles and patients’ clinical information, we next identified prognostic genes using novel computational approaches. Phosphoglucose isomerase, also known as glucose-6-phosphate isomerase(GPI), which ranked first among 27 candidate genes, was further investigated by a new analytical tool namely enviro-geno-pheno-state(E-GPS) analysis. Suitability of GPI as a prognostic marker, and its relationship with physiological processes such as metabolism, epithelial-mesenchymal transition(EMT), as well as drug sensitivity were evaluated using both our own and independent public datasets.Results: We found that higher expression of GPI in GC correlated with prolonged survival of patients.Particularly, a combination of CDH2 and GPI expression effectively stratified the outcomes of patients with TNM stage Ⅱ/Ⅲ. Down-regulation of GPI in tumor tissues correlated well with depressed glucose metabolism and fatty acid synthesis, as well as enhanced fatty acid oxidation and creatine metabolism, indicating that GPI represents a suitable marker for increased probability of EMT in GC cells.Conclusions: Our findings strongly suggest that GPI acts as a novel biomarker candidate for GC prognosis,allowing greatly enhanced clinical management of GC patients. The potential metabolic rewiring correlated with GPI also provides new insights into studying the relationship between cancer metabolism and patient survival.
