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子宫内膜癌中氧化还原相关亚型的相互作用、预后模型的建立及免疫反应

Crosstalk of Redox-Related Subtypes, Establishment of a Prognostic Model and Immune Responses in Endometrial Carcinoma.

作者信息

Geng Rui, Song Jiahang, Zhong Zihang, Ni Senmiao, Liu Wen, He Zhiqiang, Gan Shilin, Huang Qinghao, Yu Hao, Bai Jianling, Liu Jinhui

机构信息

Department of Biostatistics, School of Public Heath, Nanjing Medical University, Nanjing 210029, China.

Department of Radiation Oncology, The First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, China.

出版信息

Cancers (Basel). 2022 Jul 12;14(14):3383. doi: 10.3390/cancers14143383.

Abstract

Redox plays a central part in the pathogeneses and development of tumors. We comprehensively determined the expression patterns of redox-related genes (RRGs) in endometrial carcinoma (EC) cohorts from public databases and identified four different RRG-related clusters. The prognosis and the characteristics of TME cell infiltration of RRGcluster C patients were worse than those of other RRG clusters. When it comes to the gene cluster, there were great differences in clinicopathology traits and immunocyte infiltration. The RRG score was calculated by Cox analyses, and an RRG-based signature was developed. The risk score performed well in the EC cohort. Samples were separated into two risk subgroups with the standard of the value of the median risk score. Low-risk patients had a better prognosis and higher immunogenicity. In addition, RRG score was closely associated with immunophenoscore, microsatellite instability, tumor mutation burden, tumor stem cell index, copy number variation and chemotherapy sensitivity. The nomogram accurately predicted the prognosis of patients, and our model showed better performance than other published models. In conclusion, we built a prognostic model of RRGs which can help to evaluate clinical outcomes and guide more effective treatment.

摘要

氧化还原在肿瘤的发病机制和发展中起着核心作用。我们从公共数据库全面确定了子宫内膜癌(EC)队列中氧化还原相关基因(RRGs)的表达模式,并鉴定出四个不同的RRG相关簇。RRG簇C患者的预后和肿瘤微环境(TME)细胞浸润特征比其他RRG簇患者更差。在基因簇方面,临床病理特征和免疫细胞浸润存在很大差异。通过Cox分析计算RRG评分,并开发了基于RRG的特征。风险评分在EC队列中表现良好。根据中位风险评分值的标准将样本分为两个风险亚组。低风险患者预后更好,免疫原性更高。此外,RRG评分与免疫表型评分、微卫星不稳定性、肿瘤突变负担、肿瘤干细胞指数、拷贝数变异和化疗敏感性密切相关。列线图准确预测了患者的预后,我们的模型表现优于其他已发表的模型。总之,我们构建了一个RRG预后模型,有助于评估临床结果并指导更有效的治疗。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3cd4/9319597/94ff19ab14ff/cancers-14-03383-g001.jpg

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