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Primary research
Baohua Hou
Guangdong General Hospital
Corresponding Author
ORCiD: https://orcid.org/0000-0003-2786-3925
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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.
Keywords
Risk Model, Pancreatic Cancer, Metabolism; KRAS, Prognosis, Chemoresistance
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A new preprint design is coming!
We have improved readability, clarity, accessibility, and opportunities for engagement.
This is a preprint, a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Primary research
Baohua Hou
Guangdong General Hospital
Corresponding Author
ORCiD: https://orcid.org/0000-0003-2786-3925
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
History
CURRENT STATUS: Posted
Version 1
Posted 03 Nov, 2020
No community comments so far
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
Subject Areas
Cancer Biology
License:
This work is licensed under a CC BY 4.0 License. Read Full License
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.
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
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