| Kai Qin,Yi Cheng,Jing Zhang,Xianglin Yuan,Jianhua Wang,Jian Bai. Prognostic risk model construction and prognostic biomarkers identification in esophageal adenocarcinoma based on immune-related long noncoding RNA. Oncol Transl Med, 2020, 6: 109-115. |
| 基于TCGA数据库的食管腺癌免疫相关长链非编码RNA的预后风险模型建立及预后标志物分析 |
| Prognostic risk model construction and prognostic biomarkers identification in esophageal adenocarcinoma based on immune-related long noncoding RNA |
| Received:March 11, 2020 Revised:June 12, 2020 |
| DOI：10.1007/s10330-020-0408-8 |
| 中文关键词: 免疫相关长链非编码RNA；预后模型；预后生物标志物；食管腺癌；TCGA数据库 |
| 英文关键词: immune-related Cancer Genome Atlas (lncRNA); prognostic model; prognostic biomarker; esophageal adenocarcinoma (EAC); Cancer Genome Atlas (TCGA) database |
| 基金项目:湖北省卫生健康委员会2019-2020年度面上项目 (WJ2019M118) |
| Author Name | Affiliation | E-mail | | Kai Qin | Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030 | qinkaitj@126.com | | Yi Cheng | Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030 | | | Jing Zhang | Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030 | | | Xianglin Yuan | Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030 | | | Jianhua Wang | Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030 | | | Jian Bai* | Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030 | 26062793@qq.com |
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| 中文摘要: |
| 目的：构建基于免疫相关长链非编码RNA( long non-coding RNA，lncRNA) 的食管腺癌预后模型，并寻找指导预后的生物标志物。
方法：从癌症基因组图谱(The Cancer Genome Atlas，TCGA) 数据库下载食管腺癌的转录本数据及临床数据，草莓版Per软件和R软件用于数据处理和分析。获得食管腺癌的免疫相关基因及免疫相关lncRNA，筛选差异表达lncRNA，继而对筛选结果进行单因素Cox回归分析筛选与预后相关的免疫相关lncRNA，再进一步用Lasso回归分析筛选关键免疫相关lncRNA，进行多因素Cox回归分析构建预后模型。运用Kaplan-Meier(K-M)生存分析、受试者接受特征(receiver operating characteristic，ROC)曲线和临床性状的独立预后分析对模型进行评价。此外，运用多因素Cox回归分析计算预后模型中具有统计学意义的免疫相关lncRNA进行K-M 生存分析以确定预后生物标志物。
结果：单因素Cox回归分析显示，在1322个差异表达的免疫相关lncRNA中，28个可能与预后相关。此外，K-M 生存分析结果显示低风险组的总生存时间较高风险组明显延长（P=1.063e?10); 5年总生存率的ROC曲线下面积为0.90。对临床性状指标进行单因素及多因素独立预后分析显示：预后风险评分对食管腺癌具有独立预后风险作用。多因素Cox回归表明， 12个关键免疫相关lncRNA 中有8个lncRNA差异有统计学意义，进一步的K-M生存分析表明，其中有5个免疫相关lncRNA可能具有独立的预后价值，包括AL136115.1(P=0.006)、AC079684.1(P=0.008)、AC016394.1(P=0.0386) 、AC087620.1（P=0.041）和MIRLET7BHG(P=0.044)。
结论：通过TCGA数据库的食管腺癌样本分析，成功构建基于12个免疫相关lncRNA表达水平的食管腺癌预后模型，预测准确性中等，并确定了一个有利的预后生物标志物MIRLET7BHG，以及四个不良的预后生物标志物AL136115.1、AC079684.1、AC016394.1和AC087620.1。 |
| 英文摘要: |
| Objective The aim of this study was to construct a prognostic model of esophageal adenocarcinoma
(EAC) based on immune-related long noncoding RNAs (immune-related lncRNAs) and identify prognostic
biomarkers using the Cancer Genome Atlas (TCGA) database.
Methods Whole genomic mRNA expression and clinical data of esophageal adenocarcinoma were
obtained from the TCGA database. The software Strawberry Perl, R and R packets were used to identify the
immune-related genes and lncRNAs of esophageal adenocarcinoma, and for data processing and analysis.
The differentially expressed lncRNAs were detected while comparing esophageal adenocarcinoma and
normal tissue samples. The key immune-related lncRNAs were screened using lasso regression analysis
and univariate cox regression analysis, and used to construct the prognostic model using multivariate cox
regression analysis.
To evaluate the accuracy of the risk prognostic model, all esophageal adenocarcinomas were divided into
high-risk and low-risk groups according to the median risk score, after which Kaplan-Meier (K-M) survival
curves, operating characteristic (ROC) curve and independent prognostic analysis of clinical traits were
created. In addition, statistically significant immune-related lncRNAs and potential prognostic biomarkers
were identified using the prognostic model and multifactor cox regression analysis for k-m survival analysis.
Results A total of 1322 differentially expressed immune-related lncRNAs were identified, 28 of which
were associated with prognosis via univariate cox regression analysis. In addition, K-M survival analysis
showed that the total survival time of the higher risk group was significantly shorter than that of the lower
risk group (P = 1.063e?10). The area under the ROC curve of 5-year total survival rate was 0.90. The
risk score showed independent prognostic risk for esophageal adenocarcinoma via single factor and
multifactorial independent prognostic analyses. In addition, the HR and 95% CI of each key immune-related
lncRNA were calculated using multivariate Cox regression. Using k-m survival analysis, we found that 5 out
of 12 key significant immune-related lncRNAs had independent prognostic value [AL136115.1 (P = 0.006),
AC079684.1 (P = 0.008), AC07916394.1 (P = 0.0386), AC087620.1 (P = 0.041) and MIRLET7BHG (P =
0.044)].
Conclusion The present study successfully constructed a prognostic model of esophageal
adenocarcinoma based on the TCGA database, with moderate predictive accuracy. The model consisted
of the expression level of 12 immune-related lncRNAs. Furthermore, the study identified one favorable
prognostic biomarker, MIRLET7BHG, and four poor prognostic biomarkers (AL136115.1, AC079684.1,
AC016394.1, and AC087620.1). |
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