A Novel Prognostic Risk Model for Cervical Cancer Based on Immune Checkpoint HLA-G-Driven Differentially Expressed Genes - Xu, Hui-Hui; Wang, Hui-Li; Xing, Tong-Jin; Wang, Xue-Quan (Frontiers in Immunology, 2022)
HLA-G is a novel immune checkpoint molecule that plays a key role in cervical carcinogenesis. The aim of this study was to construct and validate a prognostic risk model to predict the overall survival of patients with cervical cancer, providing a reference for individualized clinical treatment that may lead to better clinical outcomes. HLA-G-driven differentially expressed genes (DEGs) were obtained from two cervical carcinoma cell lines, with stable overexpression of HLA-G by RNA sequencing. The biological functions of these HLA-G-driven DEGs were analysed by GO and KEGG using the clusterProfiler R. The protein-protein interactions were assessed using the STRING database. Then, the prognostic relevance of each DEG was evaluated by univariate Cox regression using the TCGA-CESC dataset. A prognostic risk model was established by LASSO and stepwise multivariate Cox regression analysis. A total of 1108 candidate HLA-G-driven DEGs, including 391 upregulated and 717 downregulated genes, were obtained and were enriched mostly in the ErbB signalling pathway, steroid biosynthesis, and MAPK signalling pathways. Then, an HLA-G-driven DEG signature consisting of the eight most important prognostic genes CD46, LGALS9, PGM1, SPRY4, CACNB3, PLIN2, MSMO1, and DAGLB was identified as a key predictor of cervical cancer. Multivariate Cox regression analysis showed that this signature is an independent risk factor for the overall survival of CESC patients. Kaplan-Meier survival analysis showed that the 5-year overall survival rate is 23.0% and 84.6% for the high-risk and low-risk patients, respectively (P<0.001). The ROC curve of this prognostic model with an area under the curve was 0.896 for 5 years, which was better than that of other clinical traits. This prognostic risk model was also successfully validated in different subtypes of cervical cancer. A nomogram that integrated risk score, age, clinical stage, histological grade, and pathological type was then built to predict the overall survival of CESC patients and evaluated by calibration curves, AUC, concordance index (C-index) and decision curve analysis (DCA). To summarize, we developed and validated a novel prognostic risk model for cervical cancer based on HLA-G-driven DEGs, and the prognostic signature showed great ability in predicting the overall survival of patients with cervical cancer.