RNA 结合蛋白的作用及其在肾透明细胞癌中的预后价值的综合分析。

Integrated Analysis of the Roles of RNA Binding Proteins and Their Prognostic Value in Clear Cell Renal Cell Carcinoma.

机构信息

Department of Urology, The First Affiliated Hospital, Harbin Medical University, Harbin 150001, China.

Department of Hepatopancreatobiliary Surgery, Harbin Medical University Cancer Hospital, Harbin, China.

出版信息

J Healthc Eng. 2021 Jun 30;2021:5568411. doi: 10.1155/2021/5568411. eCollection 2021.

DOI:10.1155/2021/5568411

PMID:34306592

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8263288/

Abstract

METHODS

We downloaded the RNA sequencing data of ccRCC from the Cancer Genome Atlas (TCGA) database and identified differently expressed RBPs in different tissues. In this study, we used bioinformatics to analyze the expression and prognostic value of RBPs; then, we performed functional analysis and constructed a protein interaction network for them. We also screened out some RBPs related to the prognosis of ccRCC. Finally, based on the identified RBPs, we constructed a prognostic model that can predict patients' risk of illness and survival time. Also, the data in the HPA database were used for verification.

RESULTS

In our experiment, we obtained 539 ccRCC samples and 72 normal controls. In the subsequent analysis, 87 upregulated RBPs and 38 downregulated RBPs were obtained. In addition, 9 genes related to the prognosis of patients were selected, namely, RPL36A, THOC6, RNASE2, NOVA2, TLR3, PPARGC1A, DARS, LARS2, and U2AF1L4. We further constructed a prognostic model based on these genes and plotted the ROC curve. This ROC curve performed well in judgement and evaluation. A nomogram that can judge the patient's life span is also made.

CONCLUSION

In conclusion, we have identified differentially expressed RBPs in ccRCC and carried out a series of in-depth research studies, the results of which may provide ideas for the diagnosis of ccRCC and the research of new targeted drugs.

摘要

方法

我们从癌症基因组图谱（TCGA）数据库下载了 ccRCC 的 RNA 测序数据，并鉴定了不同组织中差异表达的 RBPs。在本研究中，我们使用生物信息学方法分析了 RBPs 的表达和预后价值；然后，我们对其进行了功能分析并构建了蛋白质相互作用网络。我们还筛选出了一些与 ccRCC 预后相关的 RBPs。最后，基于鉴定出的 RBPs，我们构建了一个可以预测患者疾病风险和生存时间的预后模型。此外，还使用 HPA 数据库中的数据进行了验证。

结果

在我们的实验中，获得了 539 例 ccRCC 样本和 72 例正常对照。在随后的分析中，获得了 87 个上调的 RBPs 和 38 个下调的 RBPs。此外，还选择了 9 个与患者预后相关的基因，即 RPL36A、THOC6、RNASE2、NOVA2、TLR3、PPARGC1A、DARS、LARS2 和 U2AF1L4。我们进一步基于这些基因构建了预后模型，并绘制了 ROC 曲线。该 ROC 曲线在判断和评估方面表现良好。还制作了一个可以判断患者寿命的列线图。