食管癌组织中异质性细胞核核糖核蛋白A2B1的表达及临床意义
Expression of heterogeneous nuclear ribonucleoprotein A2B1 in esophageal cancer tissues and its clinical significance
葛俊伟 1徐斌 1陈俊俊 1沈琼 1刘颖婷 1李迪 2郑晓 1陈陆俊1
作者信息
- 1. 苏州大学附属第三医院肿瘤生物诊疗中心,常州 213003
- 2. 生物芯片上海国家工程研究中心,上海 200125
- 折叠
摘要
目的 探讨食管癌组织中异质性细胞核核糖核蛋白A2B1(HNRNPA2B1)的表达及临床意义.方法 通过基因表达综合(GEO)数据库GSE160269数据集下载食管癌单细胞数据,数据更新时间为2020年11月29日,分析HNRNPA2B1的表达情况.下载癌症基因组图谱(TCGA)数据库中食管癌转录组测序的每千个碱基的转录每百万映射读取的片段数(FPKM)定量数据(包含173例食管癌患者样本,其中162例为食管癌组织,11例为癌旁正常组织)以及生存数据.利用TCGA数据库中食管癌的FPKM定量数据进行分析,选取HNRNPA2B1相关度最高的前250个基因,通过R4.3.0 clusterProfiler包对选取的基因集进行基因本体(GO)及京都基因与基因组百科全书(KEGG)富集分析.将TCGA数据库中食管癌的FPKM定量数据导入CIBERSORTx网站,获得免疫浸润细胞丰度评分,分析HNRNPA2B1与免疫细胞浸润程度的相关性.收集人食管癌组织芯片(包括114例食管鳞状细胞癌组织和66例癌旁正常组织)患者的临床病理资料,患者手术时间2006年1月至2008年12月,随访时间截至2015年7月;采用多色免疫组织化学染色(mIHC)检测食管癌组织芯片中细胞角蛋白(CK)、HNRNPA2B1的表达情况,并进行多光谱组织成像.TCGA数据库中数据通过R4.3.0 survival包和survminer包计算HNRNPA2B1基因表达的最佳临界值(cut-off值),组织芯片中数据通过CK+HNRNPA2B1+细胞比例计算HNRNPA2B1表达的cut-off值,根据cut-off值将食管癌患者分为高表达组和低表达组,比较两组总生存(OS),采用Cox比例风险模型分析OS的影响因素.结果 GSE160269数据集食管癌单细胞数据中,HNRNPA2B1在肿瘤上皮细胞中的表达水平高于正常上皮细胞,在免疫细胞不同亚群中呈现高表达.HNRNPA2B1高表达水平与调节性T细胞、初始B细胞、记忆性CD4+T细胞呈正相关.GO富集分析显示HNRNPA2B1主要参与核分裂的生物学过程;细胞组分主要富集于染色体区域;分子功能主要富集于ATP水解活性.KEGG富集分析显示HNRNPA2B1主要参与细胞周期、剪接体、DNA复制等生物学过程.mIHC和多光谱组织成像分析结果显示,CK主要表达在肿瘤细胞及正常食管上皮细胞胞膜,HNRNPA2B1主要表达于肿瘤细胞及正常食管细胞胞核;食管癌组织中HNRNPA2B1表达水平高于癌旁正常组织(U=2 984.00,P＜0.05).基于组织芯片和TCGA数据库数据的生存分析结果显示,HNRNPA2B1低表达食管癌患者OS均优于HNRNPA2B1高表达患者(均P＜0.05).Cox多因素回归分析结果显示,年龄(HR=1.919,95％CI:1.158～3.182,P=0.011)、TNM 分期(HR=2.404,95％CI:1.374～4.207,P=0.002)、T分期(HR=2.349,95％CI:1.150～4.789,P=0.019)和肿瘤上皮细胞 HNRNPA2B1 的表达(HR=2.160,95％CI:1.280～3.647,P=0.004)是食管癌患者OS的独立影响因素.结论 食管癌组织中HNRNPA2B1蛋白高表达可能参与食管癌的发生、发展,可作为食管癌预后评估的重要生物学标志.
Abstract
Objective To investigate the expression of heterogeneous nuclear ribonucleoprotein A2B1(HNRNPA2B1)in human esophageal cancer tissues and its clinical significance.Methods Single-cell data for esophageal cancer were downloaded from the Gene Expression Omnibus(GEO)database(GSE160269 dataset,last updated on November 29,2020)to analyze the expression of HNRNPA2B1.Transcriptional sequencing data for esophageal cancer from The Cancer Genome Atlas(TCGA)database,including the fragments per kilobase of transcript per million mapped reads(FPKM)quantitative data(173 samples,consisting of 162 esophageal cancer tissues and 11 adjacent normal tissues),and survival data in the phenotype category were downloaded.Analysis of FPKM quantitative data from the TCGA database for esophageal cancer was performed.The top 250 genes most correlated with HNRNPA2B1 were selected and the R4.3.0 clusterProfiler package was used to conduct Gene Ontology(GO)and Kyoto Encyclopedia of Genes and Genomes(KEGG)enrichment analyses on the selected gene set.FPKM quantitative data from the TCGA database for esophageal cancer were imported into the CIBERSORTx website to obtain immune cell abundance scores,and the correlation between HNRNPA2B1 and the degree of immune cell infiltration was analyzed.The clinicopathological data of patients from esophageal cancer tissue microarrays including 114 cases of esophageal squamous cell carcinoma tissues and 66 cases of adjacent normal tissues were collected.The patients underwent surgery from January 2006 to December 2008,and the follow-up period extended until July 2015.Cytokeratin(CK)and HNRNPA2B1 expression in esophageal cancer tissue microarrays were detected by using multi-color immunohistochemical(mIHC)staining,and multispectral tissue imaging was conducted.The R4.3.0 survival package and survminer package in TCGA database were used to calculate the optimal cut-off value of HNRNPA2B1 expression and the proportion of CK+HNRNPA2B1+cells in tissue microarrays was used to calculate the cut-off value of HNRNPA2B1 expression based on which patients were categorized into high and low expression groups.The overall survival(OS)of both groups was compared and the factors influencing OS were analyzed by using the Cox proportional hazards model.Results In the GSE160269 dataset of single-cell data for esophageal cancer,the expression level of HNRNPA2B1 in tumor epithelial cells was higher than that in normal epithelial cells,and HNRNPA2B1 was highly expressed in various immune cell subtypes.The high expression level of HNRNPA2B1 was positively correlated with regulatory T cells,naive B cells and memory CD4+T cells.GO enrichment analysis revealed that HNRNPA2B1 was primarily involved in the biological process of nuclear division,cellular components were mainly enriched in chromosomal regions,and molecular functions were mainly enriched in ATP hydrolysis activity.KEGG enrichment analysis indicated that HNRNPA2B1 was primarily involved in biological processes such as the cell cycle,spliceosome,and DNA replication.Results from mIHC and multispectral tissue imaging demonstrated that CK was predominantly expressed in the cell membranes of tumor cells and normal esophageal epithelial cells,while HNRNPA2B1 was primarily expressed in the nuclei of tumor cells and normal esophageal cells.The expression level of HNRNPA2B1 in esophageal cancer tissues was higher than that in the normal paracancerous tissues(U=2 984.00,P＜0.05).Results of tissue microarrays and the survival analysis on the data in the TCGA database indicated that esophageal cancer patients with low HNRNPA2B1 expression had a better OS compared to those with high expression(both P＜0.05).Cox multivariate regression analysis revealed that age(HR=1.919,95％CI:1.158-3.182,P=0.011),TNM stage(HR=2.404,95％CI:1.374-4.207,P=0.002),T stage(HR=2.349,95％CI:1.150-4.789,P=0.019),and the expression of HNRNPA2B1 in tumor epithelial cells(HR=2.160,95％CI:1.280-3.647,P=0.004)were independent factors influencing OS in esophageal cancer patients.Conclusions The high expression of HNRNPA2B1 protein in esophageal cancer tissues may play a role in the developement and progression of esophageal cancer,serving as a crucial biological indicator for prognostic assessment of esophageal cancer.
关键词
食管肿瘤/异质性细胞核核糖核蛋白A2B1/预后Key words
Esophageal neoplasms/Heterogeneous nuclear ribonucleoprotein A2B1/Prognosis引用本文复制引用
基金项目
国家自然科学基金(82172689)
国家自然科学基金(81902386)
江苏省重点研发计划(BE2022721)
江苏省自然科学基金(BK20211065)
中国博士后科学基金面上项目(2021M700543)
中国博士后科学基金面上项目(2021M700547)
常州市国际科技合作项目(CZ20210035)
常州市应用基础研究项目(CJ20220229)
出版年
2024
NETL
NSTL
万方数据