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

Background: KRAS was reported to affect some metabolic genes and promote metabolic reprogramming in solid tumors. However, there was no comprehensive analysis to explore KRAS-associated metabolic signature or risk model for pancreatic cancer (PC).

Methods: In the 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 between 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 enrolled to establish a prognostic risk model. The prognostic model had a high C-index of 0.733 for overall survival (OS) in 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) was significantly upregulated in high-risk PC patients evaluated by the risk model.

Conclusions: We constructed a risk model based on 6 KRAS-associated metabolic genes, which predicted patients' survival with high accuracy and reflected tumor metabolic characteristics and gemcitabine-associated chemoresistance in PC.