A novel ten-gene prognostic signature for cervical cancer based on CD79B-related immunomodulators

The identification of immune-related prognostic biomarkers opens up the possibility of developing new immunotherapy strategies against tumors. In this study, we investigated immune-related biomarkers in the tumor microenvironment to predict the prognosis of cervical cancer (CC) patients. ESTIMATE and CIBERSORT algorithms were used to calculate the abundance of tumor-infiltrating immune cells (TICs) and the amount of immune and stromal components in cervical samples (n = 309) from The Cancer Genome Atlas. Ten immune-related differentially expressed genes associated with CC survival were identified via intersection analyses of multivariate Cox regression and protein-protein interactions. CD79B was chosen for further study, and its prognostic value and role in anti-CC immune functions were analyzed. Differential expression analysis and qRT-PCR validation both revealed that CD79B expression was down-regulated in CC tissues. Survival analysis suggested that a high level of CD79B expression was associated with good prognosis. In the clinical correlation analysis, CD79B expression was found to be related to primary therapy outcome, race, histological type, degree of cell differentiation, disease-specific survival, and progression-free interval. GSEA showed that the function and pathway of CD79B were mainly related to immune activities. Meanwhile, CD79B expression was correlated with 10 types of TICs. Based on CD79B-associated immunomodulators, a novel immune prognostic signature consisting of 10 genes (CD96, LAG3, PDCD1, TIGIT, CD27, KLRK1, LTA, PVR, TNFRSF13C, and TNFRSF17) was established and validated as possessing good independent prognostic value for CC patients. Finally, a nomogram to predict personalized 3- and 5-year overall survival probabilities in CC patients was built and validated. In summary, our findings demonstrated that CD79B might be a potential prognostic biomarker for CC. The 10-gene prognostic signature independently predicted the overall survival of patients with CC, which could improve individualized treatment and aid clinical decision-making.