Identifying a Novel Eight-NK Cell-related Gene Signature for Ovarian Cancer Prognosis Prediction

Background: Ovarian cancer (OVC) is the most common and costly tumor in the world with unfavorable overall survival and prognosis. This study is aimed to explore the prognostic value of natural killer cells related genes for OVC treatment. Methods: RNA-seq and clinical information were acquired from the TCGA-OVC dataset (training dataset) and the GSE51800 dataset (validation dataset). Genes linked to NK cells were obtained from the immPort dataset. Moreover, ConsensusClusterPlus facilitated the screening of molecular subtypes. Following this, the risk model was established by LASSO analysis, and immune infiltration and immunotherapy were then detected by CIBERSORT, ssGSEA, ESTIMATE, and TIDE algorithms. Results: Based on 23 NK cell-related genes with prognosis, TCGA-OVC samples were classified into two clusters, namely C1 and C2. Of these, C1 had better survival outcomes as well as enhanced immune infiltration and tumor stem cells. Additionally, it was more suitable for immunotherapy and was also sensitive to traditional chemotherapy drugs. The eight-gene prognosis model was constructed and verified via the GSE51800 dataset. Additionally, a high infiltration level of immune cells was observed in low-risk patients. Low-risk samples also benefited from immunotherapy and chemotherapy drugs. Finally, a nomogram and ROC curves were applied to validate model accuracy. Conclusion: The present study identified a RiskScore signature, which could stratify patients with different infiltration levels, immunotherapy, and chemotherapy drugs. Our study provided a basis for precisely evaluating OVC therapy and prognosis