构建并验证宫颈癌代谢基因相关预后模型及其在肿瘤微环境和免疫中的作用

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.