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N1-甲基腺苷相关长非编码 RNA 是预测子宫体子宫内膜癌预后和免疫反应的潜在生物标志物。

N1-Methyladenosine-Related lncRNAs Are Potential Biomarkers for Predicting Prognosis and Immune Response in Uterine Corpus Endometrial Carcinoma.

机构信息

Department of Gynecology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, 210029 Jiangsu, China.

Department of Biostatistics, School of Public Heath, Nanjing Medical University, 101 Longmian Avenue, Jiangning District, Nanjing 211166, China.

出版信息

Oxid Med Cell Longev. 2022 Jul 31;2022:2754836. doi: 10.1155/2022/2754836. eCollection 2022.

Abstract

Uterine corpus endometrial carcinoma (UCEC) is a malignant disease that, at present, has no well-characterised prognostic biomarker. In this study, two clusters were identified based on 28 N1-methyladenosine- (m1A-) related long noncoding RNAs (lncRNAs), of which cluster 1 was related to immune pathways according to the results of an enrichment analysis. We further observed better prognosis in patients with higher levels of immune cell infiltration, tumor mutation burden, microsatellite instability, and immune checkpoint gene expression. In addition, through Cox regression analysis and least absolute shrinkage and selection operator regression analysis, 10 m1A-related lncRNAs (mRLs) were employed to build a prognosis model. We found that people in higher risk categories had a poorer survival probability than those in lower risk. Low-risk samples were enriched with immune-related pathways, while the high-risk group was similar to the definition of the "immune desert" phenotype, which was associated with decreased immune infiltration, T cell failure, and decreased tumor mutation burden, while also being insensitive to immunotherapy and chemotherapy. This mRL-based model has the ability to accurately predict the prognosis of UCEC patients, and the mRLs could become promising therapeutic targets in enhancing the response of immunotherapy.

摘要

子宫内膜癌(Uterine corpus endometrial carcinoma,UCEC)是一种恶性疾病,目前尚无特征明确的预后生物标志物。在本研究中,基于 28 个 N1-甲基腺苷(m1A)相关长非编码 RNA(lncRNA),鉴定出了两个聚类,其中聚类 1 根据富集分析的结果与免疫途径有关。我们进一步观察到,免疫细胞浸润、肿瘤突变负荷、微卫星不稳定性和免疫检查点基因表达水平较高的患者预后更好。此外,通过 Cox 回归分析和最小绝对收缩和选择算子回归分析,选择了 10 个 m1A 相关 lncRNA(mRL)构建预后模型。我们发现,风险较高类别的患者比风险较低类别的患者的生存概率更差。低风险样本富集了免疫相关途径,而高风险组类似于“免疫荒漠”表型的定义,其特征是免疫浸润减少、T 细胞衰竭和肿瘤突变负荷降低,同时对免疫治疗和化疗不敏感。该基于 mRL 的模型能够准确预测 UCEC 患者的预后,mRL 可能成为增强免疫治疗反应的有前途的治疗靶点。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/21b5/9372539/4eb03355b119/OMCL2022-2754836.001.jpg

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