Development of a KRAS-Associated Metabolic Risk Model for Prognostic Prediction in Pancreatic Cancer

Abstract

Background: KRAS was reported to affect some metabolic genes and promote metabolic reprogramming in solid tumors. However, there is no comprehensive analysis to explore KRAS associated metabolic signature or risk model for Pancreatic cancer (PC).Methods: In current study, multiple bioinformatics analyses were used to identify differentially expressed metabolic genes based on KRAS mutation status in PC. Then we developed and validated a prognostic risk model based on the selected KRAS-associated metabolic genes. Besides, we explored the association of the risk model and the metabolic characteristics as well as Gemcitabine associated chemoresistance in PC.Results: 6 KRAS-associated metabolic genes (i.e. CYP2S1, GPX3, FTCD, ENPP2, UGT1A10, and XDH) were selected and were enrolled to establish a prognostic risk model. The prognostic model had a high C-index of 0.733 for overall survival (OS) in the TCGA pancreatic cancer database. The area under the curve (AUC) values of 1- and 3-year survival were both greater than 0.70. Then the risk model was validated in two GEO datasets and also presented a satisfactory discrimination and calibration performance. Further, we found that the expression of some KRAS-driven glycolysis associated genes (PKM, GLUT1, HK2, and LDHA) and Gemcitabine associated chemoresistance genes (i.e. CDA and RMM2) were significantly up-regulated in high-risk PC patients evaluated by the risk model.Conclusions: We constructed a risk model based on 6 KRAS associated metabolic genes, which predicts patients' survival with high accuracy and reflects tumor metabolic characteristics and Gemcitabine associated chemoresistance in PC.