Rong Xia1,2, Hua Tang3, Jiemiao Shen1,2, Shuyu Xu1,2, Yinyin Liang1,2, Yuxin Zhang4, Xing Gong1,2, Yue Min1,2, Di Zhang1,2, Chenzhe Tao1,2, Shoulin Wang1,2, Yi Zhang5, Jinyou Yang6, Chao Wang7,8. 1. Key Lab of Modern Toxicology of Ministry of Education, Center for Global Health, School of Public Health, Nanjing Medical University, 101 Longmian Avenue, Nanjing, 211166, People's Republic of China. 2. State Key Lab of Reproductive Medicine, Institute of Toxicology, Nanjing Medical University, 101 Longmian Avenue, Nanjing, 211166, People's Republic of China. 3. Department of General Surgery, Tongling People's Hospital, 468 Bijiashan Road, Tongling, Anhui Province, 244000, People's Republic of China. 4. The First Clinical Medical College of Nanjing Medical University, Nanjing, 211166, People's Republic of China. 5. Department of Colorectal Surgery, the First Affiliated Hospital of Nanjing Medical University, Nanjing, 210000, People's Republic of China. yizhang311@njmu.edu.cn. 6. Department of Clinical Medicine and Rehabilitation, Jiangsu College of Nursing, 9 Keji Road, Huai'an, 223005, People's Republic of China. yangjinyou4518@163.com. 7. Key Lab of Modern Toxicology of Ministry of Education, Center for Global Health, School of Public Health, Nanjing Medical University, 101 Longmian Avenue, Nanjing, 211166, People's Republic of China. wangchao@njmu.edu.cn. 8. State Key Lab of Reproductive Medicine, Institute of Toxicology, Nanjing Medical University, 101 Longmian Avenue, Nanjing, 211166, People's Republic of China. wangchao@njmu.edu.cn.
Abstract
BACKGROUND: Globally, gastrointestinal (GI) cancer is one of the most prevalent malignant tumors. However, studies have not established glycolysis-related gene signatures that can be used to construct accurate prognostic models for GI cancers in the Asian population. Herein, we aimed at establishing a novel glycolysis-related gene expression signature to predict the prognosis of GI cancers. METHODS: First, we evaluated the mRNA expression profiles and the corresponding clinical data of 296 Asian GI cancer patients in The Cancer Genome Atlas (TCGA) database (TCGA-LIHC, TCGA-STAD, TCGA-ESCA, TCGA-PAAD, TCGA-COAD, TCGA-CHOL and TCGA-READ). Differentially expressed mRNAs between GI tumors and normal tissues were investigated. Gene Set Enrichment Analysis (GSEA) was performed to identify glycolysis-related genes. Then, univariate, LASSO regression and multivariate Cox regression analyses were performed to establish a key prognostic glycolysis-related gene expression signature. The Kaplan-Meier and receiver operating characteristic (ROC) curves were used to evaluate the efficiency and accuracy of survival prediction. Finally, a risk score to predict the prognosis of GI cancers was calculated and validated using the TCGA data sets. Furthermore, this risk score was verified in two Gene Expression Omnibus (GEO) data sets (GSE116174 and GSE84433) and in 28 pairs of tissue samples. RESULTS: Prognosis-related genes (NUP85, HAX1, GNPDA1, HDLBP and GPD1) among the differentially expressed glycolysis-related genes were screened and identified. The five-gene expression signature was used to assign patients into high- and low-risk groups (p < 0.05) and it showed a satisfactory prognostic value for overall survival (OS, p = 6.383 × 10-6). The ROC curve analysis revealed that this model has a high sensitivity and specificity (0.757 at 5 years). Besides, stratification analysis showed that the prognostic value of the five-gene signature was independent of other clinical characteristics, and it could markedly discriminate between GI tumor tissues and normal tissues. Finally, the expression levels of the five prognosis-related genes in the clinical tissue samples were consistent with the results from the TCGA data sets. CONCLUSIONS: Based on the five glycolysis-related genes (NUP85, HAX1, GNPDA1, HDLBP and GPD1), and in combination with clinical characteristics, this model can independently predict the OS of GI cancers in Asian patients.
BACKGROUND: Globally, gastrointestinal (GI) cancer is one of the most prevalent malignant tumors. However, studies have not established glycolysis-related gene signatures that can be used to construct accurate prognostic models for GI cancers in the Asian population. Herein, we aimed at establishing a novel glycolysis-related gene expression signature to predict the prognosis of GI cancers. METHODS: First, we evaluated the mRNA expression profiles and the corresponding clinical data of 296 Asian GI cancerpatients in The Cancer Genome Atlas (TCGA) database (TCGA-LIHC, TCGA-STAD, TCGA-ESCA, TCGA-PAAD, TCGA-COAD, TCGA-CHOL and TCGA-READ). Differentially expressed mRNAs between GI tumors and normal tissues were investigated. Gene Set Enrichment Analysis (GSEA) was performed to identify glycolysis-related genes. Then, univariate, LASSO regression and multivariate Cox regression analyses were performed to establish a key prognostic glycolysis-related gene expression signature. The Kaplan-Meier and receiver operating characteristic (ROC) curves were used to evaluate the efficiency and accuracy of survival prediction. Finally, a risk score to predict the prognosis of GI cancers was calculated and validated using the TCGA data sets. Furthermore, this risk score was verified in two Gene Expression Omnibus (GEO) data sets (GSE116174 and GSE84433) and in 28 pairs of tissue samples. RESULTS: Prognosis-related genes (NUP85, HAX1, GNPDA1, HDLBP and GPD1) among the differentially expressed glycolysis-related genes were screened and identified. The five-gene expression signature was used to assign patients into high- and low-risk groups (p < 0.05) and it showed a satisfactory prognostic value for overall survival (OS, p = 6.383 × 10-6). The ROC curve analysis revealed that this model has a high sensitivity and specificity (0.757 at 5 years). Besides, stratification analysis showed that the prognostic value of the five-gene signature was independent of other clinical characteristics, and it could markedly discriminate between GI tumor tissues and normal tissues. Finally, the expression levels of the five prognosis-related genes in the clinical tissue samples were consistent with the results from the TCGA data sets. CONCLUSIONS: Based on the five glycolysis-related genes (NUP85, HAX1, GNPDA1, HDLBP and GPD1), and in combination with clinical characteristics, this model can independently predict the OS of GI cancers in Asian patients.
Entities:
Keywords:
Asian patients; Gastrointestinal cancer; Glycolysis-related genes; Prognosis
Authors: Mugdha Joshi; Jacqueline Eagan; Nirav K Desai; Stephanie A Newton; Meghan C Towne; Nicholas S Marinakis; Kristyn M Esteves; Sarah De Ferranti; Michael J Bennett; Adam McIntyre; Alan H Beggs; Gerard T Berry; Pankaj B Agrawal Journal: Eur J Hum Genet Date: 2014-02-19 Impact factor: 4.246
Authors: Madhulika Rai; Sarah M Carter; Shefali A Shefali; Nader H Mahmoudzadeh; Robert Pepin; Jason M Tennessen Journal: G3 (Bethesda) Date: 2022-07-29 Impact factor: 3.542