The value of a metabolic reprogramming-related gene signature for pancreatic adenocarcinoma prognosis prediction

Pancreatic ductal adenocarcinoma (PDAC) is one of the most fatal malignancies worldwide. Extensive enhancement of glycolysis and reprogramming of lipid metabolism are both associated with the development and progression of PDAC. Previous studies have suggested that various gene signatures could conv...

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Veröffentlicht in:Aging (Albany, NY.) NY.), 2020-11, Vol.12 (23), p.24228-24241
Hauptverfasser: Tan, Zhen, Lei, Yubin, Xu, Jin, Shi, Si, Hua, Jie, Zhang, Bo, Meng, Qingcai, Liu, Jiang, Zhang, Yiyin, Wei, Miaoyan, Yu, Xianjun, Liang, Chen
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container_issue 23
container_start_page 24228
container_title Aging (Albany, NY.)
container_volume 12
creator Tan, Zhen
Lei, Yubin
Xu, Jin
Shi, Si
Hua, Jie
Zhang, Bo
Meng, Qingcai
Liu, Jiang
Zhang, Yiyin
Wei, Miaoyan
Yu, Xianjun
Liang, Chen
description Pancreatic ductal adenocarcinoma (PDAC) is one of the most fatal malignancies worldwide. Extensive enhancement of glycolysis and reprogramming of lipid metabolism are both associated with the development and progression of PDAC. Previous studies have suggested that various gene signatures could convey prognostic information about PDAC. However, the use of these signatures has some limitations, perhaps because of a lack of knowledge regarding the genetic and energy supply backgrounds of PDAC. Therefore, we conducted multi-mRNA analysis based on metabolic reprogramming to identify novel signatures for accurate prognosis prediction in PDAC patients. In this study, a three-gene signature comprising MET, ENO3 and CD36 was established to predict the overall survival of PDAC patients. The three-gene signature could divide patients into high- and low-risk groups by disparities in overall survival verified by log-rank test in two independent validation cohorts and could differentiate tumors from normal tissues with excellent accuracy in four Gene Expression Omnibus (GEO) cohorts. We also found a positive correlation between the risk score of the gene signature and inherited germline mutations in PDAC predisposition genes. A glycolysis and lipid metabolism-based gene nomogram and corresponding calibration curves showed significant performance for survival prediction in the TCGA-PDAC dataset. The high-risk designation was closely connected with oncological signatures and multiple aggressiveness-related pathways, as determined by gene set enrichment analysis (GSEA). In summary, our study developed a three-gene signature and established a prognostic nomogram that objectively predicted overall survival in PDAC. The findings could provide a reference for the prediction of overall survival and could aid in individualized management for PDAC patients.
doi_str_mv 10.18632/aging.104134
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Extensive enhancement of glycolysis and reprogramming of lipid metabolism are both associated with the development and progression of PDAC. Previous studies have suggested that various gene signatures could convey prognostic information about PDAC. However, the use of these signatures has some limitations, perhaps because of a lack of knowledge regarding the genetic and energy supply backgrounds of PDAC. Therefore, we conducted multi-mRNA analysis based on metabolic reprogramming to identify novel signatures for accurate prognosis prediction in PDAC patients. In this study, a three-gene signature comprising MET, ENO3 and CD36 was established to predict the overall survival of PDAC patients. The three-gene signature could divide patients into high- and low-risk groups by disparities in overall survival verified by log-rank test in two independent validation cohorts and could differentiate tumors from normal tissues with excellent accuracy in four Gene Expression Omnibus (GEO) cohorts. We also found a positive correlation between the risk score of the gene signature and inherited germline mutations in PDAC predisposition genes. A glycolysis and lipid metabolism-based gene nomogram and corresponding calibration curves showed significant performance for survival prediction in the TCGA-PDAC dataset. The high-risk designation was closely connected with oncological signatures and multiple aggressiveness-related pathways, as determined by gene set enrichment analysis (GSEA). In summary, our study developed a three-gene signature and established a prognostic nomogram that objectively predicted overall survival in PDAC. 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Extensive enhancement of glycolysis and reprogramming of lipid metabolism are both associated with the development and progression of PDAC. Previous studies have suggested that various gene signatures could convey prognostic information about PDAC. However, the use of these signatures has some limitations, perhaps because of a lack of knowledge regarding the genetic and energy supply backgrounds of PDAC. Therefore, we conducted multi-mRNA analysis based on metabolic reprogramming to identify novel signatures for accurate prognosis prediction in PDAC patients. In this study, a three-gene signature comprising MET, ENO3 and CD36 was established to predict the overall survival of PDAC patients. The three-gene signature could divide patients into high- and low-risk groups by disparities in overall survival verified by log-rank test in two independent validation cohorts and could differentiate tumors from normal tissues with excellent accuracy in four Gene Expression Omnibus (GEO) cohorts. We also found a positive correlation between the risk score of the gene signature and inherited germline mutations in PDAC predisposition genes. A glycolysis and lipid metabolism-based gene nomogram and corresponding calibration curves showed significant performance for survival prediction in the TCGA-PDAC dataset. The high-risk designation was closely connected with oncological signatures and multiple aggressiveness-related pathways, as determined by gene set enrichment analysis (GSEA). In summary, our study developed a three-gene signature and established a prognostic nomogram that objectively predicted overall survival in PDAC. The findings could provide a reference for the prediction of overall survival and could aid in individualized management for PDAC patients.</abstract><cop>United States</cop><pub>Impact Journals</pub><pmid>33226369</pmid><doi>10.18632/aging.104134</doi><tpages>14</tpages><oa>free_for_read</oa></addata></record>
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subjects Biomarkers, Tumor - genetics
Carcinoma, Pancreatic Ductal - genetics
Carcinoma, Pancreatic Ductal - metabolism
Carcinoma, Pancreatic Ductal - mortality
Carcinoma, Pancreatic Ductal - therapy
Cellular Reprogramming
Databases, Genetic
Decision Support Techniques
Energy Metabolism - genetics
Female
Gene Expression Profiling
Gene Expression Regulation, Neoplastic
Germ-Line Mutation
Humans
Male
Nomograms
Pancreatic Neoplasms - genetics
Pancreatic Neoplasms - metabolism
Pancreatic Neoplasms - mortality
Pancreatic Neoplasms - therapy
Predictive Value of Tests
Prognosis
Reproducibility of Results
Research Paper
Risk Assessment
Risk Factors
Signal Transduction
Transcriptome
title The value of a metabolic reprogramming-related gene signature for pancreatic adenocarcinoma prognosis prediction
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