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. |
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 |
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KeyWord:immune-related Cancer Genome Atlas (lncRNA); prognostic model; prognostic biomarker; esophageal adenocarcinoma (EAC); Cancer Genome Atlas (TCGA) database |
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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Abstract: |
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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