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卵巢癌肿瘤免疫微环境中m7G相关特征分析及临床预后调节因子鉴定

Analysis of m7G-related signatures in the tumor immune microenvironment and identification of clinical prognostic regulators in ovarian cancer.

作者信息

Wang Kunyu, Wu You, Ao Miao, Mao Wei, Luo Haixia, Song Yan, Li Bin

机构信息

Department of Gynecology Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.

Department of Pathology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.

出版信息

Front Immunol. 2025 Aug 14;16:1595618. doi: 10.3389/fimmu.2025.1595618. eCollection 2025.

Abstract

Ovarian cancer (OV) is the most lethal gynecological malignancy in the world. At present, the effect of m7G modification-related genes on the development of ovarian cancer remains unclear. We performed consensus clustering of ovarian cancer samples based on the expression of 24 m7G modification-related genes, and obtained 2 subtypes. There were some differences in immune cell infiltration between the two subtypes. Furthermore, enrichment analysis showed that differential genes were mainly enriched in several pathways and biological processes, including positive translation regulation and TRAPP complex. Multivariate cox regression analysis confirmed two genes (DCP2 and NUDT16) related to prognosis for the construction of risk score prediction models. The risk map of survival status showed that the high-risk samples had a shorter survival time (p<0.05). Risk score was an independent prognostic factor for OV and correlated with immunotherapy response. We also performed network analysis for DCP2 and NUDT16. We further explored the effects of the genes on cellular function and prognosis. In conclusion, this study provided a new perspective for the development mechanism of ovarian cancer.

摘要

卵巢癌(OV)是全球最致命的妇科恶性肿瘤。目前,m7G修饰相关基因对卵巢癌发生发展的影响尚不清楚。我们基于24个m7G修饰相关基因的表达对卵巢癌样本进行了一致性聚类,得到了2个亚型。两个亚型在免疫细胞浸润方面存在一些差异。此外,富集分析表明差异基因主要富集在几个通路和生物学过程中,包括正向翻译调控和TRAPP复合体。多变量cox回归分析确定了两个与预后相关的基因(DCP2和NUDT16)用于构建风险评分预测模型。生存状态风险图显示高风险样本的生存时间较短(p<0.05)。风险评分是OV的独立预后因素,且与免疫治疗反应相关。我们还对DCP2和NUDT16进行了网络分析。我们进一步探讨了这些基因对细胞功能和预后的影响。总之,本研究为卵巢癌的发生发展机制提供了新的视角。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7810/12390964/5a159784039a/fimmu-16-1595618-g001.jpg

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