A Prognostic Model Based on Immune-Related Long Non-Coding RNAs for Patients With Cervical Cancer.
A Prognostic Model Based on Immune-Related Long Non-Coding RNAs for Patients With Cervical Cancer.
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DOI：
10.3389/fphar.2020.585255
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发表时间：
2020
影响因子：
5.6
通讯作者：
Wang Y
中科院分区：
文献类型：
--
作者：
Chen P;Gao Y;Ouyang S;Wei L;Zhou M;You H;Wang Y
目的：本研究旨在分析免疫相关长链非编码RNA（lncRNA）与宫颈癌患者预后的关系。我们构建了一个预后模型，并探讨了不同风险组的免疫特征。研究方法：我们从The Cancer Genome Atlas数据库中下载了227例患者的基因表达谱和临床数据，并提取了免疫相关的lncRNA。采用考克斯回归分析筛选出预测性lncRNA。根据lncRNA表达水平和回归系数（β）计算每例患者的风险评分，并构建预后模型。采用Kaplan-Meier法分析比较不同风险组的总生存期（OS）。采用主成分分析和基因集富集分析方法分析免疫相关基因在各组中的分布。使用表达数据进行恶性肿瘤中的基质细胞和免疫细胞的估计以探索免疫微环境。结果：将患者分为训练集和验证集。选择五种免疫相关lncRNA（H1 FX-AS 1、AL441992.1、USP 30-AS 1、AP001527.2和AL031123.2）用于构建预后模型。训练集中的患者分为OS较短的高危组和OS较长的低危组（p = 0.004）;同时，验证集（p = 0.013）、组合集（p < 0.001）和不同肿瘤分期患者的结果相似。通过Q-PCR在56例宫颈癌组织中进一步证实了该模型。免疫相关基因在各组中的分布差异有统计学意义。此外，低危组的免疫评分和程序性死亡配体1表达较高。结论：基于免疫相关lncRNAs的预后模型可以预测宫颈癌患者的预后和免疫状态，有利于临床预后判断和个体化治疗。
Objectives: The study is performed to analyze the relationship between immune-related long non-coding RNAs (lncRNAs) and the prognosis of cervical cancer patients. We constructed a prognostic model and explored the immune characteristics of different risk groups. Methods: We downloaded the gene expression profiles and clinical data of 227 patients from The Cancer Genome Atlas database and extracted immune-related lncRNAs. Cox regression analysis was used to pick out the predictive lncRNAs. The risk score of each patient was calculated based on the expression level of lncRNAs and regression coefficient (β), and a prognostic model was constructed. The overall survival (OS) of different risk groups was analyzed and compared by the Kaplan–Meier method. To analyze the distribution of immune-related genes in each group, principal component analysis and Gene set enrichment analysis were carried out. Estimation of STromal and Immune cells in MAlignant Tumors using Expression data was performed to explore the immune microenvironment. Results: Patients were divided into training set and validation set. Five immune-related lncRNAs (H1FX-AS1, AL441992.1, USP30-AS1, AP001527.2, and AL031123.2) were selected for the construction of the prognostic model. Patients in the training set were divided into high-risk group with shorter OS and low-risk group with longer OS (p = 0.004); meanwhile, similar result were found in validation set (p = 0.013), combination set (p < 0.001) and patients with different tumor stages. This model was further confirmed in 56 cervical cancer tissues by Q-PCR. The distribution of immune-related genes was significantly different in each group. In addition, the immune score and the programmed death-ligand 1 expression of the low-risk group was higher. Conclusions: The prognostic model based on immune-related lncRNAs could predict the prognosis and immune status of cervical cancer patients which is conducive to clinical prognosis judgment and individual treatment.
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