CD8+ T cell activation in endometrial cancer: prognostic implications and potential for personalized therapy
Background
As an important component in preventing the progression of endometrial cancer, CD8 T cells play a crucial role in this process and are important targets for immunotherapy. However, the status of CD8+ T cells in endometrial cancer and the key genes influencing their activation still remain to be elucidated.
Methods
Genes associated with the activation of CD8+ T cells were identified through differential analysis and weighted gene co-expression network analysis (WGCNA). A risk score model was constructed using the least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression. The clinical characteristics and differences between the high-risk group and the low-risk group were explored, and the applicability of the model to chemotherapy, poly (ADP-ribose) polymerase (PARP) inhibitors, and immune checkpoint inhibitors was evaluated. The characteristics of the model at the single-cell level were studied, and the tumor-suppressive effect of ASB2 was verified through experiments on endometrial cancer cells.
Results
A risk model based on genes related to the activation of CD8+ T cells was constructed, and the prognostic differences were verified using the Kaplan-Meier curve. A nomogram was designed to predict the survival probability. Pathway analysis showed that it was related to metabolism and DNA repair. There were significant differences between the high-risk and low-risk groups in terms of tumor mutational burden (TMB), checkpoint molecules, and major histocompatibility complex (MHC) class I molecules, and they had different sensitivities to different therapies. The tumor-suppressive effect of ASB2 was confirmed in experiments on cell proliferation, invasion, and migration.
Conclusion
This study provides a predictive tool for endometrial cancer. The classification based on the status of CD8+ T cells can distinguish the prognosis and treatment response, highlighting the potential of this model in personalized treatment.