[1]黄向东,张兴伟.食管癌预后相关lncRNA的特征构建[J].医学信息,2023,36(05):1-7.[doi:10.3969/j.issn.1006-1959.2023.05.001]
　HUANG Xiang-dong,ZHANG Xing-wei.Feature Construction of lncRNA Related to Prognosis of Esophageal Cancer[J].Journal of Medical Information,2023,36(05):1-7.[doi:10.3969/j.issn.1006-1959.2023.05.001]
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食管癌预后相关lncRNA的特征构建()
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医学信息[ISSN:1006-1959/CN:61-1278/R]

卷:
36卷
期数:
2023年05期
页码:
1-7
栏目:
生物信息学
出版日期:
2023-03-01

文章信息/Info

Title:
Feature Construction of lncRNA Related to Prognosis of Esophageal Cancer
文章编号:
1006-1959（2023）05-0001-07
作者:
黄向东; 张兴伟
（内蒙古赤峰市医院胸外科，内蒙古 赤峰 024000）
Author(s):
HUANG Xiang-dong; ZHANG Xing-wei
(Department of Thoracic Surgery,Chifeng Municipal Hospital,Chifeng 024000,Inner Mongolia,China)
关键词:
TCGA数据库; 食管癌; lncRNA; 预测特征; 生物标志物
Keywords:
TCGA database; Esophageal cancer; lncRNA; Prediction features; Biomarkers
分类号:
R735.1
DOI:
10.3969/j.issn.1006-1959.2023.05.001
文献标志码:
A
摘要:
目的 基于癌症基因组图谱（TCGA）数据库，探索食管癌预后相关lncRNA，构建患者预后预测特征，为食管癌患者的潜在治疗靶点提供新的证据支持。方法 获取TCGA数据库中食管癌患者表达谱及临床信息。使用R语言鉴定癌组织与正常组织之间差异表达的mRNAs（DEGs）和差异表达的lncRNAs（DELs）。基于Cox回归和LASSO回归分析构建预后预测特征，通过K-M生存分析评估预后模型中高风险和低风险人群体的生存差异。另外，通过Pearson相关分析来识别与预后模型中lncRNAs具有共表达关系的mRNAs，对lncRNA的功能进行分析。结果 基于TCGA数据库，共获得169个样本，鉴定出592个DELs和1219个DEGs。基于LASSO回归分析，构建了包含13个lncRNA的稳健预测特征。根据风险评分将患者分为高低风险组，K-M生存曲线结果显示，高危组的总生存时间（OS）短于低危组（P=3e-10），该风险特征对1、3和5年生存率预测的AUC分别为0.762、0.773和0.925，显示出极好的可预测性。AC002331.1、LINC01068和RP11-2N1.2的高表达与患者的不良预后密切相关。结论 本研究中构建的lncRNA特征可较好的预测食管癌患者的总生存时间，可能是食管癌患者的潜在治疗靶点。
Abstract:
Objective To explore the prognostic lncRNA of esophageal cancer and construct the prognostic prediction features of patients based on the Cancer Genome Atlas (TCGA) database, and to provide new evidence support for the potential therapeutic targets of esophageal cancer patients.Methods The expression profile and clinical information of esophageal cancer patients in TCGA database were obtained. R language was used to identify differentially expressed mRNAs (DEGs) and differentially expressed lncRNAs (DELs) between cancer tissues and normal tissues. Based on Cox regression and LASSO regression analysis, prognostic predictive features were constructed, and survival differences between high-risk and low-risk groups in the prognostic model were evaluated by K-M survival analysis. In addition, Pearson correlation analysis was used to identify mRNAs that were co-expressed with lncRNAs in the prognostic model, and the function of lncRNAs was analyzed.Results Based on the TCGA database, a total of 169 samples were obtained, and 592 DELs and 1219 DEGs were identified. Based on LASSO regression analysis, robust prediction features containing 13 lncRNAs were constructed. According to the risk score, the patients were divided into high-risk group and low-risk groups. The K-M survival curve showed that the overall survival time (OS) of the high-risk group was shorter than that of the low-risk group (P=3e-10). The AUC of the risk feature for predicting 1, 3 and 5-year survival rates was 0.762, 0.773 and 0.925, respectively, showing excellent predictability. The high expression of AC002331.1, LINC01068 and RP11-2N1.2 was closely related to the poor prognosis of patients.Conclusion The lncRNA features constructed in this study can better predict the overall survival time of patients with esophageal cancer and may be a potential therapeutic target for esophageal cancer patients.

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更新日期/Last Update: 1900-01-01