Development of a risk model based on autophagy-related genes to predict survival and immunotherapy response in ovarian cancer.

Abstract

Background: Autophagy is a highly conserved cellular proteolytic process that can interact with innate immune signaling pathways to affect the growth of tumor cells. However, the regulatory mechanism of autophagy in the tumor microenvironment, drug sensitivity, and immunotherapy is still unclear.

Methods: Based on the prognostic autophagy-related genes, we used the unsupervised clustering method to divide 866 ovarian cancer samples into two regulatory patterns. According to the phenotypic regulation pattern formed by the differential gene between the two regulation patterns, a risk model was constructed to quantify patients with ovarian cancer. Then, we systematically analyzed the relationship between the risk model and immune cell infiltration, immunotherapeutic response, and drug sensitivity.

Results: Based on autophagy-related genes, we found two autophagy regulation patterns, and confirmed that there were differences in prognosis and immune cell infiltration between them. Subsequently, we constructed a risk model, which was divided into a high-risk group and a low-risk group. We found that the high-risk group had a worse prognosis, and the main infiltrating immune cells were adaptive immune cells, such as Th2 cells, Tgd cells, eosinophils cells, and lymph vessels cells. The low-risk group had a better prognosis, and the most infiltrated immune cells were innate immune cells, such as aDC cells, NK CD56dim cells, and NK CD56bright cells. Furthermore, we found that the risk model could predict chemosensitivity and immunotherapy response, suggesting that the risk model may help to formulate personalized treatment plans for patients.

Conclusions: Our study comprehensively analyzed the prognostic potential of autophagy-related risk models in ovarian cancer and determined their clinical guiding role in targeted therapy and immunotherapy.