Version 1
: Received: 26 September 2023 / Approved: 27 September 2023 / Online: 28 September 2023 (04:40:08 CEST)
How to cite:
Zhong, J.; Chen, J.; Zhang, Z.; Shao, L. Cell Glycolysis Related Gene Signature Predicts Survival of Patients with Colorectal Cancer. Preprints2023, 2023091887. https://doi.org/10.20944/preprints202309.1887.v1
Zhong, J.; Chen, J.; Zhang, Z.; Shao, L. Cell Glycolysis Related Gene Signature Predicts Survival of Patients with Colorectal Cancer. Preprints 2023, 2023091887. https://doi.org/10.20944/preprints202309.1887.v1
Zhong, J.; Chen, J.; Zhang, Z.; Shao, L. Cell Glycolysis Related Gene Signature Predicts Survival of Patients with Colorectal Cancer. Preprints2023, 2023091887. https://doi.org/10.20944/preprints202309.1887.v1
APA Style
Zhong, J., Chen, J., Zhang, Z., & Shao, L. (2023). Cell Glycolysis Related Gene Signature Predicts Survival of Patients with Colorectal Cancer. Preprints. https://doi.org/10.20944/preprints202309.1887.v1
Chicago/Turabian Style
Zhong, J., Zizhen Zhang and Liming Shao. 2023 "Cell Glycolysis Related Gene Signature Predicts Survival of Patients with Colorectal Cancer" Preprints. https://doi.org/10.20944/preprints202309.1887.v1
Abstract
Background: Colorectal cancer (CRC) is one of the major causes of cancer-related death worldwide. Aerobic glycolysis precedes obtainment of oncogenic mutations and loss of tumor suppressors, promoting the progress of CRC. Although numerous biomarkers have been identified to be associated with prognosis and survival, a glycolysis-related gene signature in CRC has not been explored. Methods: mRNA expression profiling data in a group of CRC patients (n = 540) was extracted from the Cancer Genome Atlas (TCGA). Gene set enrichment analysis (GESA) was performed to identify gene sets that were significantly different between CRC tissues and normal tissues. Cox proportional hazards regression models were used to identify genes significantly associated with overall survival. Multivariate Cox regression analysis was used to establish a prognostic risk parameter formula. Kaplan–Meier survival estimates and the log-rank test were used to validate the significance of risk parameters for prognosis prediction. Results: Five glycolysis-related genes (ENO3, GPC1, P4HA1, IDUA, ANKZF1) were identified to be significantly associated with overall survival (AUC=0.754). Based on the five‑gene signature, patients with CRC were divided into high and low‑risk subgroups. CRC patients with a low-risk score had better survival benefits than those with a high one (P < 0.001). Conclusions: A five-gene signature associated with glycolysis for predicting the outcome of CRC patients was generated, serving as a valuable prognosis model with high efficiency and potential targeted therapy of CRC patients.
Medicine and Pharmacology, Medicine and Pharmacology
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
