Construction of N6-methyladenosine Related LncRNA Pairing Model for Renal Cell Carcinoma Based on Bioinformatics Analysis of TCGA Database and Its Prognostic Value Research
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维普
万方数据
目的 探究基于癌症基因组图谱(the cancer genome atlas,TCGA)数据库生物信息学分析构建肾癌N6-甲基腺苷相关长链非编码RNA(long non-coding RNA,LncRNA)配对模型及其预后预测价值.方法 从TCGA数据库中下载肾癌的RNA-sep转录组数据及相关临床信息,后通过Perl软件对转录组数据进行数据整理、分离LncRNA和信使RNA(messenger RNA,mRNA).总共得到 564 例肾癌患者的肾癌组织和 72 例正常组织,最终纳入 540 例肾癌患者.使用caret将 540 例肾癌患者采用随机数据表法分为训练集组(n=275)和验证集组(n=265).根据单因素和多因素COX回归分析建立N6-甲基腺苷相关LncRNA配对模型.以LASSO回归算法获取风险评估方程.根据该方程分别计算出风险评分,并以中位风险值最佳临界点将所有患者分为高风险组及低风险组.采用Kaplan-Meier生存分析对总体样本中高、低风险组患者的生存差异作出生存曲线图.利用Cluster Profiler软件包中对基因本体论(gene ontology,GO)和京都基因与基因组百库全书(Kyoto encyclopedia of genes and genomes,KEGG)进行通路富集分析.运用R软件分析N6-甲基腺苷相关LncRNA配对模型与免疫细胞浸润的关系.结果 根据Kaplan-Meier生存分析显示,在训练组中,低风险组患者总生存期明显高于高风险组患者(P＜0.05).与高风险组相比,低风险组G1～2,G3～4,Ⅰ～Ⅱ期、Ⅲ～Ⅳ期、年龄≤65 岁、＞65 岁患者总生存期较高(P＜0.05).对高、低风险组获取差异基因富集分析:主要富集含有肌收缩、横纹肌细胞分化、肌原纤维、受体激活活性、血管平滑肌收缩等.高风险组和低风险组最高的驱动基因进行展示变异频率及变异信息,其风险评分与T细胞、浆细胞的浸润程度呈正相关(r=0.638,P=0.001).结论 基于生物信息学分析N6-甲基腺苷相关LncRNA配对模型有助于预测肾癌患者的预后.为肾癌预后评估和最佳治疗策略提供了新思路,有助于未来进一步分析胃癌发生及发展的分子机制.
Objective To construct N6-methyladenosine related long non-coding RNA(LncRNA)pairing model for renal cell carcinoma based on bioinformatics analysis of the cancer ganome atlas(TCGA)database and to explore its prognosis value.Methods Transcriptome data of RNA-sep for renal cell carcinoma and its related clinical information were downloaded from the TCGA database.Perl software was used to organize and separate LncRNA and messenger RNA(mRNA)from the transcriptome data.A total of 564 tissues from renal cell carcinoma cases and 72 normal tissues were obtained,and thus 540 renal cancer patients were eventually included.Random data table method was used to divide 540 patients with renal cancer into a training group(n=275)and a validation group(n=265)by caret.M6A related LncRNA pairing models were established based on the single factor and multivariate COX regression analysis.The risk assessment equation was obtained using the LASSO regression algorithm.The risk scores were calculated based on this equation,and the optimal critical point of the median risk value was applied to divide all patients into high-risk and low-risk groups.Kaplan-Meier survival analysis was used to make a survival curve for the differences between high and low risk groups in the overall sample.The gene ontology(GO)and Kyoto encyclopedia of genes and genomes(KEGG)pathway enrichment analyses were conducted using the Cluster Profiler software package.The relationship between N6-methyladenosine related LncRNA pairing model and immune cell infiltration was analyzed by R software.Results Kaplan-Meier survival analysis showed the total survival time of patients in the low-risk group was significantly higher than that of patients in the high-risk group of the training group(P<0.05).Compared with high risk group,the overall survival time of patients(G1～2,G3～4,Ⅰ～Ⅱ,or Ⅲ～Ⅳ,age≤65 years,or patients>65 years old)in low risk group was higher(P<0.05).Differential gene enrichment analysis was obtained for high and low risk groups,which mainly enriched with many differential genes such as muscle contraction,rhabdomytic cell differentiation,myofibril,receptor activation activity,and vascular smooth muscle contraction.The highest driver genes in high risk group and low risk group exhibited mutation frequency and mutation information,and their risk score was positively correlated with the degree of T cell and plasma cell infiltration(r=0.638,P=0.001).Conclusion Bioinformatics-based analysis of the N6-methyladenosine related LncRNA pairing models can be helpful to predict the prognosis of patients with renal cancer.It provides new ideas for the prognosis evaluation and optimal treatment strategy of renal cancer,and contributes to further analyzing the molecular mechanism of the occurrence and development of gastric cancer in the future.
维普
万方数据
