A novel signature based on CeRNA and immune status predicts prognostic risk and drug sensitivity in gastric cancer patients

Abstract

Background: At present, there is increasing evidence that both competitive endogenous RNAs (ceRNAs) and immune status in the tumor microenvironment (TME) can affect the progression of gastric cancer (GC), and are closely related to the prognosis of patients. However, few studies have linked the two to jointly determine the prognosis of patients with GC. This study aimed to develop a combined prognostic model based on ceRNAs and immune biomarkers.

Methods: First, the gene expression profiles and clinical information were downloaded from TCGA and GEO databases. Then two ceRNA networks were constructed on the basis of circRNA. Afterwards, the key genes were screened by univariate Cox regression analysis and Lasso regression analysis, and the ceRNA-related prognostic model was constructed by multivariate Cox regression analysis. Next, CIBERSORT and ESTIMATE algorithms were utilized to obtain the immune cell infiltration abundance and stromal/immune score in TME. Furthermore, the correlation between ceRNAs and immunity was found out through co-expression analysis, and another immune-related prognosis model was established. Finally, combining these two models, a comprehensive prognostic model was built and visualized with a nomogram.

Results: The (circRNA, lncRNA)-miRNA-mRNA regulatory network of GC was constructed. The predictive power of ceRNA-related and immune-related prognosis models was moderate. Co-expression analysis showed that the ceRNA network was correlated with immunity. The integrated model of combined ceRNAs and immunity in the TCGA training set, the AUC values of 1, 3, and 5-year survival rates were 0.78, 0.76, and 0.78, respectively; in the independent external validation set GSE62254, they were 0.81, 0.79, and 0.78 respectively; in GSE15459, they were 0.84, 0.88 and 0.89 respectively. Besides, the prognostic score of the comprehensive model can predict chemotherapeutic drug resistance. Moreover, we found that plasma variant translocation 1 (PVT1) and infiltrating immune cells (mast cells) are worthy of further investigation as independent prognostic factors.

Conclusions: Two ceRNA regulatory networks were constructed based on circRNA. At the same time, a comprehensive prognosis model was established, which has a high clinical significance for prognosis prediction and chemotherapy drug selection of GC patients.

Keywords: CircRNA; competing endogenous RNA network; gastric cancer; immunocyte infiltration; mast cell; plasma variant translocation 1; prognostic model.