Research Paper Volume 13, Issue 23 pp 25072—25088
Construction and validation of a metabolic gene-associated prognostic model for cervical carcinoma and the role on tumor microenvironment and immunity
- 1 Department of Obstetrics and Gynecology, Affiliated Hospital of Guangdong Medical University, Zhanjiang 524001, Guangdong, PR China
- 2 Key Laboratory of Research in Maternal and Child Medicine and Birth Defects, Guangdong Medical University, Foshan 528300, Guangdong, PR China
- 3 First College of Clinical Medicine, Guangdong Medical University, Zhanjiang 524001, Guangdong, PR China
- 4 Department of Ultrasound, Shunde Women and Children’s Hospital (Maternity and Child Healthcare Hospital of Shunde Foshan), Guangdong Medical University, Foshan, China
- 5 Department of Internal Medicine, Shunde Women and Children’s Hospital (Maternity and Child Healthcare Hospital of Shunde Foshan), Guangdong Medical University, Foshan, China
- 6 Department of Pharmacy, Affiliated Hospital of Guangdong Medical University, Zhanjiang 524001, Guangdong, PR China
Received: May 24, 2021 Accepted: October 25, 2021 Published: December 1, 2021
https://doi.org/10.18632/aging.203723How to Cite
Copyright: © 2021 Huang et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Abstract
Metabolic reprogramming is a common feature of tumor cells and is associated with tumorigenesis and progression. In this study, a metabolic gene-associated prognostic model (MGPM) was constructed using multiple bioinformatics analysis methods in cervical carcinoma (CC) tissues from The Cancer Genome Atlas (TCGA) database, which comprised fifteen differentially expressed metabolic genes (DEMGs). Patients were divided into a high-risk group with shorter overall survival (OS) and a low-risk group with better survival. Receiver operating characteristic (ROC) curve analysis showed that the MGPM precisely predicted the 1-, 3- and 5-year survival of CC patients. As expected, MGPM exhibited a favorable prognostic significance in the training and testing datasets of TCGA. And the clinicopathological parameters including stage, tumor (T) and metastasis (M) classifications had significant differences in low- and high-risk groups, which further demonstrated the MGPM had a favorite prognostic prediction ability. Additionally, patients with low-ESTMATEScore had a shorter OS and when those combined with high-risk scores presented a worse prognosis. Through “CIBERSORT” package and Wilcoxon rank-sum test, patients in the high-risk group with a poor prognosis showed lower levels of infiltration of T cell CD8 (P < 0.001), T cells memory activated (P = 0.010) and mast cells resting (P < 0.001), and higher levels of mast cells activated (P < 0.001), and we also found these patients had a worse response for immunosuppressive therapy. These findings demonstrate that MGPM accurately predicts survival outcomes in CC patients, which will be helpful for further optimizing immunotherapies for cancer by reprogramming its cell metabolism.