Prediction of Prognosis, Immunotherapy and Chemotherapy with an Immune-Related Risk Score Model for Endometrial Cancer

SIMPLE SUMMARY: Endometrial cancer (EC) is one of the most common gynecologic cancers. However, its clinical therapy remains unsatisfying due to the lack of effective treatment screening approaches. The primary treatment of EC is surgery, supplemented with radiotherapy and chemotherapy. In addition, immunotherapy as a promising therapeutic strategy has been gradually applied in clinical treatment. However, not all patients can benefit from such kind of clinical treatment, because EC is a heterogeneous disease and exhibits distinct patterns of molecular alterations, biological functions, as well as clinical outcomes. Thus, there is an urgent need to develop an effective model to help optimize treatment strategies and improve their therapeutic effects. Therefore, we aimed to construct a risk score model which could be used to predict the prognosis, immunotherapy response and chemotherapy sensitivity of EC. Our study provides insights into new personalized therapies and benefits EC treatment screening. ABSTRACT: Endometrial cancer (EC) is the most common gynecologic cancer. The overall survival remains unsatisfying due to the lack of effective treatment screening approaches. Immunotherapy as a promising therapy has been applied for EC treatment, but still fails in many cases. Therefore, there is a strong need to optimize the screening approach for clinical treatment. In this study, we employed co-expression network (GCN) analysis to mine immune-related GCN modules and key genes and further constructed an immune-related risk score model (IRSM). The IRSM was proved effective as an independent predictor of poor prognosis. The roles of IRSM-related genes in EC were confirmed by IHC. The molecular basis, tumor immune microenvironment and clinical characteristics of the IRSM were revealed. Moreover, the IRSM effectiveness was associated with immunotherapy and chemotherapy. Patients in the low-risk group were more sensitive to immunotherapy and chemotherapy than those in the high-risk group. Interestingly, the patients responding to immunotherapy were also more sensitive to chemotherapy. Overall, we developed an IRSM which could be used to predict the prognosis, immunotherapy response and chemotherapy sensitivity of EC patients. Our analysis not only improves the treatment of EC but also offers targets for personalized therapeutic interventions.