Identification of a novel immune-related lncRNA signature to predict prognostic outcome and therapeutic efficacy of LGG.
Biomarker
Chemotherapy sensitivity
Immune cell infiltration
Low-grade gliomas
Prognosis
lncRNA
Journal
Journal of integrative neuroscience
ISSN: 0219-6352
Titre abrégé: J Integr Neurosci
Pays: Singapore
ID NLM: 101156357
Informations de publication
Date de publication:
22 Mar 2022
22 Mar 2022
Historique:
received:
01
12
2021
revised:
11
01
2022
accepted:
13
01
2022
entrez:
1
4
2022
pubmed:
2
4
2022
medline:
6
4
2022
Statut:
ppublish
Résumé
Recent studies have shown that the prognosis of low-grade glioma (LGG) patients is closely correlated with the immune infiltration and the expression of long-stranded non-coding RNAs (lncRNAs). It's meaningful to find the immune-related lncRNAs (irlncRNAs). The Cancer Genome Atlas (TCGA) data was employed in the study to identify irlncRNAs and Cox regression model was applied to construct the risk proportional model based on irlncRNAs. In the study, we retrieved transcriptomic data of LGG from TCGA and identified 10 lncRNA signatures consisting of irlncRNAs by co-expression analysis. Then we plotted 1-year receiver operating characteristic (ROC) curves and calculated the area under the curve (AUC). LGG patients were divided into high-risk and low-risk groups according to the risk model. We found there were differences in survival prognosis, clinical characteristics, degree of immune cell infiltration, expression of immune gene checkpoint genes, and sensitivity to the commonly used chemotherapeutic agents of high-risk and low-risk groups. IrlncRNA-based risk assessment model can be used as a prognostic tool to predict the survival outcome and clinical characteristics of LGG and to guide treatment options.
Sections du résumé
BACKGROUND
BACKGROUND
Recent studies have shown that the prognosis of low-grade glioma (LGG) patients is closely correlated with the immune infiltration and the expression of long-stranded non-coding RNAs (lncRNAs). It's meaningful to find the immune-related lncRNAs (irlncRNAs).
METHODS
METHODS
The Cancer Genome Atlas (TCGA) data was employed in the study to identify irlncRNAs and Cox regression model was applied to construct the risk proportional model based on irlncRNAs.
RESULTS
RESULTS
In the study, we retrieved transcriptomic data of LGG from TCGA and identified 10 lncRNA signatures consisting of irlncRNAs by co-expression analysis. Then we plotted 1-year receiver operating characteristic (ROC) curves and calculated the area under the curve (AUC). LGG patients were divided into high-risk and low-risk groups according to the risk model. We found there were differences in survival prognosis, clinical characteristics, degree of immune cell infiltration, expression of immune gene checkpoint genes, and sensitivity to the commonly used chemotherapeutic agents of high-risk and low-risk groups.
CONCLUSIONS
CONCLUSIONS
IrlncRNA-based risk assessment model can be used as a prognostic tool to predict the survival outcome and clinical characteristics of LGG and to guide treatment options.
Identifiants
pubmed: 35364643
pii: S0219-6352(22)00315-1
doi: 10.31083/j.jin2102055
doi:
Substances chimiques
Biomarkers, Tumor
0
RNA, Long Noncoding
0
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
55Informations de copyright
© 2022 The Author(s). Published by IMR Press.
Déclaration de conflit d'intérêts
The authors declare no conflict of interest.