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子宫内膜癌中ceRNA网络与肿瘤微环境的分析

The Analysis of ceRNA Networks and Tumor Microenvironment in Endometrial Cancer.

作者信息

Huang Jian, Yang Xiaoyue, Xu Shen, Li Ping

机构信息

Clinical and Translational Research Center, Shanghai First Maternity and Infant Hospital, Tongji University School of Medicine, Shanghai, China.

The International Peace Maternity and Child Health Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200030, China.

出版信息

J Cancer. 2024 Feb 24;15(8):2147-2159. doi: 10.7150/jca.93364. eCollection 2024.

Abstract

Endometrial carcinoma is a life-threatening and aggressive tumor that affects women worldwide. ceRNAs and carcinoma-infiltrating immunocytes can be associated with tumor formation and progression. Therefore, investigating the unique mechanisms underlying endometrial carcinoma is crucial. Prognostic nomograms were constructed based on the differentially expressed genes between normal and tumor tissues. Twenty types of tumor immune infiltrating cells in uterine corpus endometrial carcinoma (UCEC) were examined using CIBERSORT. To identify the potential signaling pathways, the associations among essential ceRNA network genes and important immunocytes were investigated using the co-expression assay. Differential analysis identified 3636 mRNAs, 249 miRNAs, and 252 lncRNAs unique to UCEC. The ceRNA network was constructed using the interplays between 19 lncRNA-miRNA pairs and 434 miRNA-mRNA pairs. Furthermore, CIBERSORT and ceRNA integration analysis revealed that immune cells, including dendritic cells and natural killer cells, and associated ceRNAs such as LRP8, HDGF, PPARGC1B, and TEAD1 can appropriately predict prognosis. A receiver operating characteristic curve was constructed to predict patient outcomes. Using a nomogram, we predicted the outcomes of patients with UCEC Furthermore, we revealed its significance in improving clinical management.

摘要

子宫内膜癌是一种危及生命且具有侵袭性的肿瘤,影响着全球女性。竞争性内源RNA(ceRNAs)和肿瘤浸润免疫细胞可能与肿瘤的形成和进展有关。因此,研究子宫内膜癌潜在的独特机制至关重要。基于正常组织和肿瘤组织之间的差异表达基因构建了预后列线图。使用CIBERSORT检测了子宫体子宫内膜癌(UCEC)中的20种肿瘤免疫浸润细胞。为了识别潜在的信号通路,使用共表达分析研究了关键ceRNA网络基因与重要免疫细胞之间的关联。差异分析确定了UCEC特有的3636个信使核糖核酸(mRNAs)、249个微小核糖核酸(miRNAs)和252个长链非编码核糖核酸(lncRNAs)。利用19对lncRNA-miRNA和434对miRNA-mRNA之间的相互作用构建了ceRNA网络。此外,CIBERSORT和ceRNA整合分析显示,包括树突状细胞和自然杀伤细胞在内的免疫细胞,以及诸如低密度脂蛋白受体相关蛋白8(LRP8)、肝细胞生长因子(HDGF)、过氧化物酶体增殖物激活受体γ共激活因子1β(PPARGC1B)和TEAD1等相关ceRNAs能够合理地预测预后。构建了受试者工作特征曲线以预测患者的预后。我们使用列线图预测了UCEC患者的预后。此外,我们揭示了其在改善临床管理方面的意义。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/172f/10937290/a8298085dd61/jcav15p2147g001.jpg

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