基于生物信息学方法的喉鳞癌相分离相关基因调控机制分型分析与预后预测模型构建  

Regulation mechanism typing and prognosis prediction model construction of laryngeal squamous cell carcinoma using liquid-liquid phase separation-related genes based on bioinformatic analysis

作　　者：郑希望 薛绪亭 郭慧娜 张宇良 牛敏[1] 张春明[1,2] 吴勇延[1,3] 高伟 ZHENG Xiwang;XUE Xuting;GUO Huina;ZHANG Yulinag;NIU Min;ZHANG Chunming;WU Yongyan;GAO Wei(Shanxi Key Laboratory of Otorhinolaryngology Head and Neck Cancer,First Hospital of Shanxi Medical University,Taiyuan 030001,China;不详)

机构地区：[1]山西医科大学第一医院耳鼻咽喉头颈肿瘤山西省重点实验室,太原030001 [2]山西医科大学第一医院耳鼻咽喉头颈外科 [3]深圳市龙岗区耳鼻咽喉医院、深圳市耳鼻咽喉研究所

出　　处：《山东医药》2023年第2期6-10,共5页Shandong Medical Journal

基　　金：山西省卫生健康委员会科研课题项目(2019033);山西省基础研究计划自由探索类青年科学研究项目(20210302124088)。

摘　　要：目的根据喉鳞癌患者转录组数据和临床参数,分析喉鳞癌相分离相关基因调控机制分型,并建立用于预测喉鳞癌患者生存时间的预后预测模型。方法从TCGA数据库下载111例喉鳞癌、12例正常组织转录组测序数据作为TCGA队列,以本实验室自有的107例喉鳞癌及其对应癌旁组织转录组测序数据作为自测队列,进行生物信息学分析。使用R语言DESeq2分析癌组织中的差异表达的相分离相关基因。使用GSEA Preranked算法对每例患者个体进行调控机制评分,使用一致性聚类对患者进行调控机制分子分型;使用STRING在线数据库构建相分离相关基因互作网络,使用cluster Profiler对相分离相关基因进行富集分析;使用Cox回归法从喉鳞癌表达差异的相分离相关基因中筛选与喉鳞癌患者生存时间相关的基因,以此建立喉鳞癌预后预测模型,采用Kaplan-Meier显示相分离相关基因对喉鳞癌患者生存时间的影响;使用LASSO算法构建相分离相关基因喉鳞癌患者预后预测模型。基于预后预测模型和喉鳞癌患者的临床参数构建诺莫图,评估预后预测模型对喉鳞癌患者生存时间的预测能力。结果从两个独立患者队列中共筛选出105个在喉鳞癌组织中呈显著差异表达的相分离相关基因。基于GSEA Preranked算法进行调控机制评分,将喉鳞癌患者分成3个具有不同调控机制的亚型。经过生存分析,13个相分离相关基因(TFRC、SLC7A5、CPEB3、PIMREG、KIF2C、SERPINH1、POU4F1、PRKAA2、SLC3A2、LGALS7B、PIWIL2、SPANXC和TUBB3)与喉鳞癌患者总生存率相关。预后预测模型公式为:0.2067×Ex_(TUBB3)+0.0530×Ex_(SPANXC)+(-0.0129)×Ex_(PIWIL2)+(-0.0897)×Ex_(LGALS7B)+0.1047×Ex_(SERPINH1)+(-0.1032)×Ex_(KIF2C)+(-0.0626)×Ex_(PIMREG)+(-0.1744)×Ex_(CPEB3)+0.1279×Ex_(TFRC)。根据模型对患者进行危险因子评分,将患者分为高、低风险组。高风险组总生存率低于低风险组(HR=5.47,95%CI为2.68~11.15,P<0Objective To analyze the regulation mechanism typing of liquid-liquid phase separation related genes(LLPSGs)of laryngeal squamous cell carcinoma(LSCC)and to establish a LLPSGs-related model that can be used to predict the prognosis survival of LSCC patients based on their transcriptome data and clinical features.Methods The transcriptome data consisting of 111 LSCC and 12 normal tissues obtained from TCGA database and RNA-Seq data consisting of 107 paired LSCC and normal tissues from our laboratory were deeply analyzed using bioinformatics methods.DESeq2program in R was utilized to screen the differentially expressed LLPSGs.GSEAPreranked algorithm was employed to mark the scores of the regulated mechanism in individual levels.Regulatory mechanism molecular typing was performed using the consensus clustering.The protein-protein interaction networks among LLPSGs were constructed in STRING online database.The enrichment analysis was performed by clusterProfiler package in R.Cox regression was used to screen the genes related to the survival time of LSCC patients from the differentially expressed LLPSGs between LSCC and normal tissues,and then we built the prognosis prediction model of LSCC.Kaplan-Meier was also used to analyzed the effects of LLPSGs on the survival time of LSCC patients.LLPSGs-related prognosis prediction model for patients with LSCC was established using LASSO algorithm.The nomogram was finally constructed based on LLPSGs-related prognosis prediction model and clinical features,and was used to assess the predictive ability of prognosis prediction model on survival time.Results A total of 105 LLPSGs with significantly differential expression in LSCC tissues were screened out from two independent cohort.Based on enrichment scores of individuals given by GSEAPreranked algorithm,patients with LSCC were divided into three subtypes with different regulatory mechanisms.Thirteen LLPSGs were identified to tightly correlate with prognosis of LSCC patients,including TFRC,SLC7A5,CPEB3,PIMREG,KIF2C,SERPINH1,POU

关 键 词：相分离 预后预测模型 生物信息学 喉鳞癌 

分 类 号：R739.65[医药卫生—肿瘤]