卵巢癌（OC）是目前发病率较高的癌症之一，严重威胁女性健康。本研究的重点是根据多学科研究，识别恶性上皮细胞 OC 之间的异质性，并构建有效的预后特征来预测预后和免疫治疗。InterCNV 算法用于基于 scRNA-seq 和bulk RNA-识别 OC 的异质性。序列数据。六种算法选择了EMTscore。使用 COX 和最小绝对收缩和选择算子 (LASSO) 回归算法进行有效的预后特征。短信数据集用于评估预后特征的准确性。我们评估了其他风险组之间的不同免疫特征和免疫治疗反应差异。建立了包括 14 个基因的预后特征。高危组患者的生存结果较差。我们还发现低危组患者的免疫细胞浸润、免疫检查点富集和免疫治疗反应较高，提示低危组患者可能对免疫治疗更敏感。最后，实验室测试结果表明，KREMEN2被确定为OC患者的新型生物标志物和治疗靶点。我们的研究基于scRNA-seq和bulk RNA-seq数据建立了由16个基因组成的GRG特征，这提供了新的视角关于 OC 的预后预测和治疗策略。版权所有 © 2024 Elsevier B.V. 保留所有权利。

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.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.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.Our study established a GRG signature consisting of 16 genes based on the scRNA-seq and bulk RNA-seq data, which provides a new perspective on the prediction of prognosis and treatment strategy for OC.Copyright © 2024 Elsevier B.V. All rights reserved.