Single-cell RNA sequencing reveals heterogeneity in ovarian cancer and constructs a prognostic signature for prognostic prediction and immunotherapy

Background: Ovarian cancer (OC) is one of the cancers with a high incidence at present, which poses a severe threat to women's health. This study focused on identifying the heterogeneity among malignant epithelial cell OC and constructing an effective prognostic signature to predict prognosis and immunotherapy according to a multidisciplinary study. Methods: The InterCNV algorithm was used to identify the heterogeneity of OC based on the scRNA-seq and bulk RNA-seq data. Six algorithms selected EMTscore. An effective prognostic signature was conducted using the COX and Least Absolute Shrinkage and Selection Operator (LASSO) regression algorithms. The texting datasets were used to assess the accuracy of the prognostic signature. We evaluated different immune characteristics and immunotherapy response differences among other risk groups. Results: A prognostic signature including 14 genes was established. The patients in the high-risk group have poor survival outcomes. We also found that the patients in the low-risk group have higher immune cell infiltration, enrichment of immune checkpoints, and immunotherapy response, suggesting that the patients in the low-risk group may be more sensitive to immunotherapy. Finally, the laboratory test results showed that KREMEN2 was identified as a novel biomarker and therapeutic target for OC patients. Conclusions: Our study established a GRG signature consisting of 16 genes based on the scRNA-seq and bulk RNAseq data, which provides a new perspective on the prediction of prognosis and treatment strategy for OC.