Suppr超能文献

基于RNA结合蛋白探索潜在的调控性麻醉药物并构建CESC预后模型:一项基于TCGA数据库的研究

Exploring Potential Regulatory Anesthetic Drugs Based on RNA Binding Protein and Constructing CESC Prognosis Model: A Study Based on TCGA Database.

作者信息

Zheng Ying, Meng Xiao Wen, Yang Jian Ping

机构信息

Department of Anesthesiology, First Affiliated Hospital of Soochow University, Suzhou, China.

出版信息

Front Surg. 2022 Apr 5;9:823566. doi: 10.3389/fsurg.2022.823566. eCollection 2022.

Abstract

OBJECTIVE

To investigate the differential expression of RBPs in cervical squamous cell carcinoma (CESC), analyze the regulatory effect of narcotic drugs on RBPs, and establish the prognostic risk model of CESC patients.

METHODS

RNA-SEQ data and clinical case data of cancer and normal samples from CESC patients were obtained from the Cancer Genome Atlas (TCGA) database and Genotype-Tissue Expression (GTEx) database. Differentially expressed RBPs were screened by R language and enriched. The CMAP database is used to predict the anesthetic drugs that regulate the differential expression of RBPs. The prognostic risk score model was constructed by COX regression analysis. Risk score of each CESC patient was calculated and divided into high-risk group and low-risk group according to the median risk score. The prediction efficiency of prognostic risk model was evaluated by Kaplan-Meier (KM) analysis and receiver operating characteristic (ROC) curve, and the correlation between prognostic risk model and clinical characteristics was analyzed. Immunohistochemistry was used to detect the expression of RNASEH2A and HENMT1 in tissues.

RESULTS

There were 65 differentially expressed RBPs in CESC. Five anesthetics, including benzocaine, procaine, pentoxyverine, and tetracaine were obtained to regulate RBPs. Survival analysis showed that seven genes were related to the prognosis of patients, and the CESC risk score model was constructed by COX regression. The risk score can be used as an independent prognostic factor. RNASEH2A and HENMT1 are up-regulated in tumors, which can effectively distinguish normal tissues from tumor tissues.

CONCLUSION

It is found that different anesthetic drugs have different regulatory effects on the differential expression of RBPs. Based on the differentially expressed RBPs, the prognostic risk score model of CESC patients was constructed. To provide ideas for the formulation of individualized precise anesthesia scheme and cancer pain analgesia scheme, which is helpful to improve the perioperative survival rate of cancer patients.

摘要

目的

探讨RNA结合蛋白(RBPs)在宫颈鳞状细胞癌(CESC)中的差异表达,分析麻醉药物对RBPs的调控作用,建立CESC患者的预后风险模型。

方法

从癌症基因组图谱(TCGA)数据库和基因型-组织表达(GTEx)数据库中获取CESC患者癌症及正常样本的RNA-SEQ数据和临床病例数据。用R语言筛选并富集差异表达的RBPs。利用CMAP数据库预测调控RBPs差异表达的麻醉药物。通过COX回归分析构建预后风险评分模型。计算每位CESC患者的风险评分,并根据中位风险评分分为高风险组和低风险组。采用Kaplan-Meier(KM)分析和受试者工作特征(ROC)曲线评估预后风险模型的预测效率,并分析预后风险模型与临床特征之间的相关性。采用免疫组织化学法检测组织中RNASEH2A和HENMT1的表达。

结果

CESC中有65个差异表达的RBPs。获得了包括苯佐卡因、普鲁卡因、喷托维林和丁卡因在内的5种麻醉药物来调控RBPs。生存分析表明,7个基因与患者预后相关,并通过COX回归构建了CESC风险评分模型。风险评分可作为独立的预后因素。RNASEH2A和HENMT1在肿瘤中上调,可有效区分正常组织和肿瘤组织。

结论

发现不同麻醉药物对RBPs的差异表达具有不同的调控作用。基于差异表达的RBPs构建了CESC患者的预后风险评分模型。为制定个体化精准麻醉方案和癌症疼痛镇痛方案提供思路,有助于提高癌症患者围手术期生存率。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bf41/9018109/aa5aff8e8f12/fsurg-09-823566-g0001.jpg

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验