基于RNA-seq数据识别宫颈癌嵌合体RNA及预后标志基因  

Identification of chimeric RNA and prognostic marker genes in cervical cancer based on RNA-seq data

作　　者：张瀚元 王静[2,3] 刘湘涛 ZHANG Hanyuan;WANG Jing;LIU Xiangtao(College of Medical Engineering and Technology,Xinjiang Medical University,Urumqi 830017,China;The First Affiliated Hospital of Xinjiang Medical University,Xinjiang Medical University,Urumqi 830054,China;State Key Laboratory of Pathogenesis,Prevention and Treatment of High Incidence Diseases in Central Asia,Xinjiang Medical University,Urumqi 830054,China)

机构地区：[1]新疆医科大学医学工程技术学院,乌鲁木齐830017 [2]新疆医科大学第一附属医院,乌鲁木齐830054 [3]新疆医科大学省部共建中亚高发病成因与防治国家级重点实验室,乌鲁木齐830054

出　　处：《新疆医科大学学报》2025年第1期29-36,共8页Journal of Xinjiang Medical University

基　　金：新疆维吾尔自治区科技厅青年自然基金项目(2019D01C212);天池英才领军人才项目(2022LJRC)。

摘　　要：目的通过设计识别嵌合体的RNA-seq数据分析的新方法,挖掘出宫颈癌的关键基因,并发现与临床预后相关的标志基因。方法本研究先综合不同方法提供的融合基因(转录本)信息,构建考虑嵌合体RNA的比对库,然后利用StringTie工具进行转录本isoform定量分析,并利用DESeq2分析宫颈癌Ⅱ期(Ⅱa)、子宫颈鳞状上皮内瘤变(CINII)、宫颈炎(Cervicitis)三个组别差异表达基因。通过WGCNA构建在不同条件下的共表达网络筛选出宫颈癌特异的关键基因,并利用TCGA数据库中宫颈癌基因表达及对应的临床数据进行Cox生存分析以筛选预后标志基因。结果该方法应用到高发女性宫颈癌的一个RNA-seq数据集的分析,9个测序样本的平均比对率由传统方法的67.03%提升至新方法的81.28%。共识别了119305个表达转录本,其中包括11357个嵌合体RNA。通过宫颈癌的关键基因在TCGA数据库中生存分析得到11个与宫颈癌预后相关标志基因并以此构建预测模型,其ROC曲线下面积AUC达到0.808。结论识别嵌合体RNA能够提升测序数据的利用效率,有助于发现宫颈癌预后标志基因。Objective Through identification of Chimeric RNA to design a novel RNA-seq data analysis method,the research recognized key genes for cervical cancer in order to discovery prognostic marker genes related clinical survival.Methods In this study,chimeric RNA information was integrated into a mixed alignment library.A StringTie workflow was adopted to transcript isoform quantification and differential expression analysis conducted by DESeq2 among conditions:cervical cancer(IIa),cervical intraepithelial neoplasia(CINII)and cervicitis.After that,all samples from different conditions were used to construct the co-expression network by WGCNA and filtered out the hub genes of cervical cancer.The re-search also used TCGA database for validation and performed Cox regression on these hub genes for sur-vival analysis.Results After analyzing an RNA-seq dataset of cervical cancer,the average sequence-map-ping rate was increased from 67.03%to 81.28%by the method.The research identified 119305 different expressed transcripts,including 11357 chimeric RNAs.The hub gene in the co-expression network were used to discovery 11 cancer tumor marker genes related to prognosis and survival in TCGA database.A predictive model built with Survival ROC based on 11 genes reached AUC as 0.808.Conclusion Identifica-tion of chimeric RNAs enhances utilization of sequence data,prompts discovery of prognostic biomarker genes in cervical cancer.

关 键 词：RNA-seq数据分析 嵌合体RNA 宫颈癌 预后标志基因 

分 类 号：TP399[自动化与计算机技术—计算机应用技术]