Yu Gong1, Siyuan Wu2, Sen Dong3, Shuai Chen4, Gengdi Cai5, Kun Bao5, Haojun Yang1, Yuwen Jiao6. 1. Department of Gastrointestinal Surgery, The Affiliated Changzhou No.2 People's Hospital of Nanjing Medical University, 68 GeHu Road, Changzhou, 213000, Jiangsu, China. 2. Department of Hepato-Biliary-Pancreatic Surgery, The Affiliated Changzhou No.2 People's Hospital of Nanjing Medical University, Changzhou, 213000, China. 3. Bengbu Medical University, Benbu, 233000, China. 4. Nanjing Medical University, Jiangsu, 213000, China. 5. Dalian Medical University, Dalian, 116000, China. 6. Department of Gastrointestinal Surgery, The Affiliated Changzhou No.2 People's Hospital of Nanjing Medical University, 68 GeHu Road, Changzhou, 213000, Jiangsu, China. drjiaoyuwen@163.com.
Abstract
PURPOSE: The growth and aggressiveness of Stomach adenocarcinoma (STAD) is significantly affected by basic metabolic changes. This study aimed to identify metabolic gene prognostic signatures in STAD. METHODS: An integrative analysis of datasets from the Cancer Genome Atlas and Gene Expression Omnibus was performed. A metabolic gene prognostic signature was developed using univariable Cox regression and Kaplan-Meier survival analysis. A nomogram model was developed to predict the prognosis of STAD patients. Finally, Gene Set Enrichment Analysis (GESA) was used to explore the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways significantly associated with the risk grouping. RESULTS: A total of 327 metabolism-related differentially expressed genes were identified. Three subtypes of STAD were identified and nine immune cell types, including memory B cell, resting and activated CD4+ memory T cells, were significantly different among the three subgroups. A risk score model including nine survival-related genes which could separate high-risk patients from low-risk patients was developed. The prognosis of STAD patients likely benefited from lower expression levels of genes, including ABCG4, ABCA6, GPX8, KYNU, ST8SIA5, and CYP19A1. Age, radiation therapy, tumor recurrence, and risk score model status were found to be independent risk factors for STAD and were used for developing a nomogram. Nine KEGG pathways, including spliceosome, pentose phosphate pathway, and citrate TCA cycle were significantly enriched in GESA. CONCLUSION: We propose a metabolic gene signature and a nomogram for STAD which might be used for predicting the survival of STAD patients and exploring prognostic markers.
PURPOSE: The growth and aggressiveness of Stomach adenocarcinoma (STAD) is significantly affected by basic metabolic changes. This study aimed to identify metabolic gene prognostic signatures in STAD. METHODS: An integrative analysis of datasets from the Cancer Genome Atlas and Gene Expression Omnibus was performed. A metabolic gene prognostic signature was developed using univariable Cox regression and Kaplan-Meier survival analysis. A nomogram model was developed to predict the prognosis of STAD patients. Finally, Gene Set Enrichment Analysis (GESA) was used to explore the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways significantly associated with the risk grouping. RESULTS: A total of 327 metabolism-related differentially expressed genes were identified. Three subtypes of STAD were identified and nine immune cell types, including memory B cell, resting and activated CD4+ memory T cells, were significantly different among the three subgroups. A risk score model including nine survival-related genes which could separate high-risk patients from low-risk patients was developed. The prognosis of STAD patients likely benefited from lower expression levels of genes, including ABCG4, ABCA6, GPX8, KYNU, ST8SIA5, and CYP19A1. Age, radiation therapy, tumor recurrence, and risk score model status were found to be independent risk factors for STAD and were used for developing a nomogram. Nine KEGG pathways, including spliceosome, pentose phosphate pathway, and citrate TCA cycle were significantly enriched in GESA. CONCLUSION: We propose a metabolic gene signature and a nomogram for STAD which might be used for predicting the survival of STAD patients and exploring prognostic markers.