A riskscore-based nomogram for the prediction of overall survival in cervical squamous cell carcinoma

Abstract

Background:Cervical cancer is still the major cause of cancer-related death among women. However, the prognosis of cervical cancer varies even in the same stage. Thus, exploring prognostic biomarkers that could reflect its biological heterogeneity may contribute to identify patients with a poor prognosis.Methods:Based on the ESTIMATE algorithm, we acquired the immune/stromal scores of cervical squamous cell carcinoma (CSCC) patients collected from The Cancer Genome Atlas (TCGA) dataset. Subsequently, we analyzed the DEGs between high- and low immune score groups using R package edgeR and performed K-M analysis to illustrate the relationship between differentially expressed genes (DEGs) and the overall survival to select survival-related DEGs. Then the LASSO regression model was constructed with the package “glmnet” in R to evaluate the riskscore of each patient. Finally, we developed a nomogram composing riskscore and clinicopathological characteristics to predict the overall survival (OS) of CSCC patients. The R software v3.6.1 was used for statistical analyses. All statistical tests were two-tailed.Results:We established a riskscore model composed of two genes including FOXP3 and ZAP70. The receiver operating characteristic (ROC) curve demonstrated a good potency of the riskscore model. Ultimately, we constructed a nomogram composing riskscore, age and stage to predict the overall survival (OS) of CSCC patients. The area under the ROC curve (AUC) of the nomogram for OS was 0.805, 0.723 and 0.748 for the first, third, and fifth years, respectively. The concordance index (C-index) was 0.746. The calibration curves also showed optimal accuracy of the nomogram for survival prediction. Conclusion:The nomogram based on riskscore could predict overall survival in CSCC and may benefit those patients through individualized immunotherapy.