The Immunological Contribution of a Novel Metabolism-Related Signature to the Prognosis and Anti-Tumor Immunity in Cervical Cancer.

Abstract

Simple SummaryCervical cancer is the most commonly diagnosed gynecological malignant carcinoma worldwide. It is crucial to develop reliable prognostic models to predict clinical outcomes and identify patients who will benefit from different treatment strategies. In current study, we identified a reliable metabolism-related signature composed of ALOX12B, CA9, FAR2, F5 and TDO2 for the prognosis and anti-tumor immunity in cervical cancer. Patients with high-risk score underwent apparently worse prognosis and displayed lower infiltration of tumor infiltrating lymphocytes. Additionally, the metabolism-based risk score could also predict the prognosis of patients with cervical cancer based on the expression of immune checkpoints. Since this risk score signature achieves a good performance in predicting clinical outcome, we genuinely expect that our study could provide an effective prognostic tool for guidance of personalized treatment of cervical cancer patients.Cervical cancer is the most frequently diagnosed malignancy in the female reproductive system. Conventional stratification of patients based on clinicopathological characters has gradually been outpaced by a molecular profiling strategy. Our study aimed to identify a reliable metabolism-related predictive signature for the prognosis and anti-tumor immunity in cervical cancer. In this study, we extracted five metabolism-related hub genes, including ALOX12B, CA9, FAR2, F5 and TDO2, for the establishment of the risk score model. The Kaplan-Meier curve suggested that patients with a high-risk score apparently had a worse prognosis in the cervical cancer training cohort (TCGA, n = 304, p < 0.0001), validation cohort (GSE44001, n = 300, p = 0.0059) and pan-cancer cohorts (including nine TCGA tumors). Using a gene set enrichment analysis (GSEA), we observed that the model was correlated with various immune-regulation-related pathways. Furthermore, pan-cancer cohorts and immunohistochemical analysis showed that the infiltration of tumor infiltrating lymphocytes (TILs) was lower in the high-score group. Additionally, the model could also predict the prognosis of patients with cervical cancer based on the expression of immune checkpoints (ICPs) in both the discovery and validation cohorts. Our study established and validated a metabolism-related prognostic model, which might improve the accuracy of predicting the clinical outcome of patients with cervical cancer and provide guidance for personalized treatment.