| 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. |
| Construction and validation of an immune-related lncRNA prognostic model for rectal adenocarcinomas |
| Received:November 26, 2020 Revised:June 10, 2021 |
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| KeyWord:rectal adenocarcinoma; immune-related lncRNA; prognostic model; The Cancer Genome Atlas (TCGA) database |
| 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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| Abstract: |
| 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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