| Danni Jian,Yi Cheng,Jing Zhang,Kai Qin. Construction and validation of an immune-related lncRNA prognostic model for rectal adenocarcinomas. Oncol Transl Med, 2021, 7: 130-135. |
| 构建与验证基于免疫相关LncRNA的直肠腺癌预后模型 |
| Construction and validation of an immune-related lncRNA prognostic model for rectal adenocarcinomas |
| Received:November 26, 2020 Revised:June 10, 2021 |
| DOI：10.1007/s10330-020-0472-2 |
| 中文关键词: 直肠腺癌；免疫相关LncRNA；预后模型；TCGA数据库 |
| 英文关键词: rectal adenocarcinoma; immune-related lncRNA; prognostic model; The Cancer Genome Atlas (TCGA) database |
| 基金项目:湖北省卫生健康委科研项目( 编号: WJ2019M118) |
| Author Name | Affiliation | E-mail | | Danni Jian | Union Hospital, Tongji Medical College, Huazhong University of Science and Technology | jian1989913@163.com | | Yi Cheng | Tongji Hospital,Tongji Medical College,Huazhong University of Science and Technology | | | Jing Zhang | Tongji Hospital,Tongji Medical College,Huazhong University of Science and Technology | | | Kai Qin* | Tongji Hospital,Tongji Medical College,Huazhong University of Science and Technology | qinkaitj@126.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 的直肠腺癌预后模型，并验证其预测效能。
方法：从癌症基因组图谱(TCGA) 数据库下载直肠腺癌的转录本数据及临床数据。应用Perl软件和R语言分析获得及筛选差异表达的免疫相关基因及LncRNA，对筛选结果进行单因素/多因素Cox和Lasso回归分析构建预后相关免疫相关LncRNA的风险评分模型。运用Kaplan-Meier生存分析、受试者工作特征ROC曲线和临床性状的独立预后分析对模型的有效性进行评价。此外，应用Kaplan-Meier生存分析筛选预后生物标志物。
结果：我们从TCGA获得89例直肠腺癌和2例癌旁标本的基因表达谱矩阵，联合免疫相关基因集，通过软件分析获得847个直肠腺癌免疫相关LncRNA及331个蛋白编码的免疫相关基因。这些免疫相关LncRNA依次经过单变量Cox和Lasso回归分析，确定8个与直肠腺癌预后相关的重要免疫相关LncRNA。进一步多变量Cox回归分析，4个免疫相关LncRNA被确定为直肠腺癌的预后标志。构建模型：风险评分 = -4.084*Expression LINC01871 + 3.112*Expression AL158152.2 + 7.616*Expression PXN-AS1- 0.867*Expression HCP5。单变量/多变量Cox回归揭示预后模型对直肠腺癌风险具有独立预后作用，且P=0.035。风险评分的ROC曲线下面积为0.957。对这4个LncRNA单独进行K-M生存分析，发现LINC01871(P=0.006)、PXN-AS1 (P=0.008)、AL158152.2 (P=0.0386)与预后相关。
结论：通过分析TCGA数据库的直肠腺癌样本及免疫相关基因集，构建基于4个免疫相关LncRNA表达水平的直肠腺癌预后模型，预测准确性高。并确定了2个预后不良的生物标志物（PXN-AS1和AL158152.2）和一个预后良好的生物标志物（LINC01871）。 |
| 英文摘要: |
| Objective This study aimed to construct a prognostic model for rectal adenocarcinomas based on
immune-related long noncoding RNAs (lncRNAs) and verify its prediction efficiency.
Methods Transcript data and clinical data of rectal adenocarcinomas were downloaded from The Cancer
Genome Atlas (TCGA) database. Perl software (strawberry version) and R language (version 3.6.1) were
used to analyze the immune-related genes and immune-related lncRNAs of rectal adenocarcinomas, and
the differentially expressed immune-related lncRNAs were screened according to the criteria |log2FC|
> 1 and P < 0.05. The key immune-related lncRNAs were screened using single-factor Cox regression
analysis and lasso regression analysis. Multivariate Cox regression analysis was performed to construct
an immune-related lncRNA prognostic model using the risk scores. Next, we evaluated the effectiveness of
the model through Kaplan-Meier (K-M) survival analysis, ROC curve analysis, and independent prognostic
analysis of clinical features. In addition, prognostic biomarkers of immune-related lncRNAs in the model
were analyzed by K-M survival analysis.
Results In this study, we obtained gene expression profile matrices of 89 rectal adenocarcinomas and 2
paracancerous specimens from TCGA database and applied immunologic signatures to these transcripts.
Through R and Perl software analysis, we obtained 847 immune-related lncRNAs and 331 protein-encoded
immune-related genes in rectal adenocarcinomas. Eight important immune-related lncRNAs related to the
prognosis of rectal adenocarcinomas were identified using univariate Cox regression and lasso regression
analysis. Furthermore, four immune-related lncRNAs were identified as prognostic markers of rectal
adenocarcinomas via multivariate Cox regression analysis. The prognostic risk model was as follows: risk
score = (-4.084) * expression LINC01871 + (3.112) * expression AL158152.2 + (7.616) * expression PXNAS1 + (-0.867) * expression HCP5. The independent prognostic effect of the rectal adenocarcinoma risk
score model was revealed through K-M analysis, ROC curve analysis, and univariate, and multivariate
Cox regression analysis (P = 0.035). LINC01871 (P = 0.006), PXN-AS1 (P = 0.008), and AL158152.2 (P
= 0.0386) were closely correlated with the prognosis of rectal adenocarcinomas through the K-M survival
analysis.
Conclusion We constructed a prognostic model of rectal adenocarcinomas based on four immunerelated lncRNAs by analyzing the data based on TCGA database, with high prediction accuracy. We also
identified two biomarkers with poor prognosis (PXN-AS1 and AL158152.2) and one biomarker with good
prognosis (LINC01871). |
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