Necroptosis是最近发现的编程性细胞死亡之一，涉及肿瘤发生、癌症转移和免疫反应。我们利用公共数据库中的数据研究了卵巢癌（OV）组织中与Necroptosis相关的基因（NRGs），将其分为两个NRG簇。位于A簇的患者将具有严重的临床特征、差的预后和更差的肿瘤微环境渗透特征。通过Cox分析得出了NRG得分，并建立了预测模型。风险得分较低的人将有更好的预后，较低的氧化还原相关基因表达，更高的免疫原性和更好的免疫治疗效果。此外，NRG分数与癌干细胞指数、拷贝数变异、肿瘤突变负荷和化疗敏感性密切相关。我们建立了一个评估签名的诊断模型，以提高其临床应用价值。这些结果有助于我们了解Necroptosis在OV中的作用，并为评估临床结果和开发更有效的治疗方案提供新思路。版权所有 © 2023 Rui Geng等。

Necroptosis is one of programmed cell death discovered recently, which involves in tumorigenesis, cancer metastasis, and immune reaction. We studied the necroptosis-related genes (NRGs) in ovarian cancer (OV) tissues using data from public databases, which separated into two NRGclusters. Patients in cluster A would have severe clinical characteristics, poor prognosis, and worse tumor microenvironment infiltration characteristics. The NRG score was achieved through the Cox analysis, along with a construction of a prognostic model. People with lower risk score would have better prognosis, lower expression of redox related genes, higher immunogenicity, and better effect on immunotherapy. In addition, the NRG score was closely related to cancer stem cell index, copy number variations, tumor mutation load, and chemosensitivity. We built a nomogram to enhance clinical application of the signature. These outcomes can help use know the function of NRGs in OV and provide new ideas for evaluating clinical outcome and developing more effective treatment protocols.Copyright © 2023 Rui Geng et al.