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

Abstract

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.

Trends in metabolic signaling pathways of tumor drug resistance: A scientometric analysis

“…The common tumor signatures are closely associated with various tumor types. Tumor signatures identified through the gene set enrichment analysis based on gene expression profiling could help to establish and validate metabolism-related prognostic models, discovering drug resistance signaling pathways ( 40 ). Notably, the eighth article ( 41 ) provided another gene lists analysis protocol explaining how to use DAVID bioinformatics resources, which consisted of an integrated biological knowledge base and analytic tools aimed at systematically extracting biological meaning from large gene/protein lists.…”

Tryptophan metabolism enzymes are potential targets in ovarian clear cell carcinoma

“…Positive IDO1, IDO2 and TDO2 staining was diffuse in the cytoplasm, while positive IL4I1 staining was granular and scattered in the cytoplasm. 22 , 35 , 36 , 37 , 38 , 39 The IHC controls are listed in Table S1 .…”