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

ABSTRACT: 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.

SUBMITTER: Tan Z

PROVIDER: S-EPMC7762467 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature

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

Tan Zhen Z Lei Yubin Y Xu Jin J Shi Si S Hua Jie J Zhang Bo B Meng Qingcai Q Liu Jiang J Zhang Yiyin Y Wei Miaoyan M Yu Xianjun X Liang Chen C

Aging 20201120 23

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. The ...[more]

PMID: 33226369

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Project description:BackgroundMetabolism reprogramming is a hallmark that associates tumor growth, metastasis, progressive, and poor prognosis. However, the metabolism-related molecular patterns and mechanism in clear cell renal cell carcinoma (ccRCC) remain unclear. Herein, the purpose of this study was to identify metabolism-related molecular pattern and to investigate the characteristics and prognostic values of the metabolism-related clustering.MethodsWe comprehensively analyzed the differentially expressed genes (DEGs), and metabolism-related genes (MAGs) in ccRCC based on the TCGA database. Consensus clustering was used to construct a metabolism-related molecular pattern. Then, the biological function, molecular characteristics, Estimate/immune/stomal scores, immune cell infiltration, response to immunotherapy, and chemotherapy were analyzed. We also identified the DEGs between subclusters and constructed a poor signature and risk model based on LASSO regression cox analysis and univariable and multivariable cox regression analyses. Then, a predictive nomogram was constructed and validated by calibration curves.ResultsA total of 1942 DEGs (1004 upregulated and 838 downregulated) between ccRCC tumor and normal samples were identified, and 254 MRGs were screened out from those DEGs. Then, 526 ccRCC patients were divided into two subclusters. The 7 metabolism-related pathways enriched in cluster 2. And cluster 2 with high Estimate/immune/stomal scores and poor survival. While, cluster 1 with higher immune cell infiltrating, expression of the immune checkpoint, IFN, HLA, immune activation-related genes, response to anti-CTLA4 treatment, and chemotherapy. Moreover, we identified 295 DEGs between two metabolism-related subclusters and constructed a 15-gene signature and 9 risk factors. Then, a risk score was calculated and the patients into high- and low-risk groups in TCGA-KIRC and E-MTAB-1980 datasets. And the prediction viability of the risk score was validated by ROC curves. Finally, the clinicopathological characteristics (age and stage), risk score, and molecular clustering, were identified as independent prognostic variables, and were used to construct a nomogram for 1-, 3-, 5-year overall survival predicting. The calibration curves were used to verify the performance of the predicted ability of the nomogram.ConclusionOur finding identified two metabolism-related molecular subclusters for ccRCC, which facilitates the estimation of response to immunotherapy and chemotherapy, and prognosis after treatment.

Dataset Information

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

Publications

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

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