lncRNAs classifier to accurately predict the recurrence of thymic epithelial tumors.
Adolescent
Adult
Aged
Aged, 80 and over
Area Under Curve
China
/ epidemiology
Female
Follow-Up Studies
Gene Expression Profiling
Humans
Male
Middle Aged
Neoplasm Recurrence, Local
/ diagnosis
Neoplasms, Glandular and Epithelial
/ pathology
Nomograms
Prognosis
RNA, Long Noncoding
/ genetics
Thymus Neoplasms
/ pathology
Young Adult
lncRNAs classifier
prediction
recurrence-free survival
thymic epithelial tumors
Journal
Thoracic cancer
ISSN: 1759-7714
Titre abrégé: Thorac Cancer
Pays: Singapore
ID NLM: 101531441
Informations de publication
Date de publication:
07 2020
07 2020
Historique:
received:
30
01
2020
revised:
28
03
2020
accepted:
30
03
2020
pubmed:
7
5
2020
medline:
12
3
2021
entrez:
7
5
2020
Statut:
ppublish
Résumé
Long non-coding RNAs (lncRNAs), which have little or no ability to encode proteins, have attracted special attention due to their potential role in cancer disease. In this study we aimed to establish a lncRNAs classifier to improve the accuracy of recurrence prediction for thymic epithelial tumors (TETs). TETs RNA sequencing (RNA-seq) data set and the matched clinicopathologic information were downloaded from the Cancer Genome Atlas. Using univariate Cox regression and least absolute shrinkage and selection operator (LASSO) analysis, we developed a lncRNAs classifier related to recurrence. Functional analysis was conducted to investigate the potential biological processes of the lncRNAs target genes. The independent prognostic factors were identified by Cox regression model. Additionally, predictive ability and clinical application of the lncRNAs classifier were assessed, and compared with the Masaoka staging by receiver operating characteristic (ROC) analysis and decision curve analysis (DCA). Four recurrence-free survival (RFS)-related lncRNAs were identified, and the classifier consisting of the identified four lncRNAs was able to effectively divide the patients into high and low risk subgroups, with an area under curve (AUC) of 0.796 (three-year RFS) and 0.788 (five-year RFS), respectively. Multivariate analysis indicated that the lncRNAs classifier was an independent recurrence risk factor. The AUC of the lncRNAs classifier in predicting RFS was significantly higher than the Masaoka staging system. Decision curve analysis further demonstrated that the lncRNAs classifier had a larger net benefit than the Masaoka staging system. A lncRNAs classifier for patients with TETs was an independent risk factor for RFS despite other clinicopathologic variables. It generated more accurate estimations of the recurrence probability when compared to the Masaoka staging system, but additional data is required before it can be used in clinical practice.
Sections du résumé
BACKGROUND
Long non-coding RNAs (lncRNAs), which have little or no ability to encode proteins, have attracted special attention due to their potential role in cancer disease. In this study we aimed to establish a lncRNAs classifier to improve the accuracy of recurrence prediction for thymic epithelial tumors (TETs).
METHODS
TETs RNA sequencing (RNA-seq) data set and the matched clinicopathologic information were downloaded from the Cancer Genome Atlas. Using univariate Cox regression and least absolute shrinkage and selection operator (LASSO) analysis, we developed a lncRNAs classifier related to recurrence. Functional analysis was conducted to investigate the potential biological processes of the lncRNAs target genes. The independent prognostic factors were identified by Cox regression model. Additionally, predictive ability and clinical application of the lncRNAs classifier were assessed, and compared with the Masaoka staging by receiver operating characteristic (ROC) analysis and decision curve analysis (DCA).
RESULTS
Four recurrence-free survival (RFS)-related lncRNAs were identified, and the classifier consisting of the identified four lncRNAs was able to effectively divide the patients into high and low risk subgroups, with an area under curve (AUC) of 0.796 (three-year RFS) and 0.788 (five-year RFS), respectively. Multivariate analysis indicated that the lncRNAs classifier was an independent recurrence risk factor. The AUC of the lncRNAs classifier in predicting RFS was significantly higher than the Masaoka staging system. Decision curve analysis further demonstrated that the lncRNAs classifier had a larger net benefit than the Masaoka staging system.
CONCLUSIONS
A lncRNAs classifier for patients with TETs was an independent risk factor for RFS despite other clinicopathologic variables. It generated more accurate estimations of the recurrence probability when compared to the Masaoka staging system, but additional data is required before it can be used in clinical practice.
Identifiants
pubmed: 32374079
doi: 10.1111/1759-7714.13439
pmc: PMC7327696
doi:
Substances chimiques
RNA, Long Noncoding
0
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
1773-1783Informations de copyright
© 2020 The Authors. Thoracic Cancer published by China Lung Oncology Group and John Wiley & Sons Australia, Ltd.
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