Identification of a N6-Methyladenosine (m6A)-Related lncRNA Signature for Predicting the Prognosis and Immune Landscape of Lung Squamous Cell Carcinoma
Backgroundm6A-related lncRNAs emerged as potential targets for tumor diagnosis and treatment. This study aimed to identify m6A-regulated lncRNAs in lung squamous cell carcinoma (LUSC) patients.Materials and MethodsRNA sequencing and the clinical data of LUSC patients were downloaded from The Cancer...
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| Autores principales: | , , , , |
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| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
Frontiers Media S.A.
2021
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| Materias: | |
| Acceso en línea: | https://doaj.org/article/73093a1085f64b2499b656683408f2c3 |
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| Sumario: | Backgroundm6A-related lncRNAs emerged as potential targets for tumor diagnosis and treatment. This study aimed to identify m6A-regulated lncRNAs in lung squamous cell carcinoma (LUSC) patients.Materials and MethodsRNA sequencing and the clinical data of LUSC patients were downloaded from The Cancer Genome Atlas (TCGA) database. The m6A-related lncRNAs were identified by using Pearson correlation assay. Univariate and multivariate Cox regression analyses were utilized to construct a risk model. The performance of the risk model was validated using Kaplan–Meier survival analysis and receiver operating characteristics (ROC). Immune estimation of LUSC was downloaded from TIMER, and the correlations between the risk score and various immune cells infiltration were analyzed using various methods. Differences in immune functions and expression of immune checkpoint inhibitors and m6A regulators between high-risk and low-risk groups were further explored. Finally, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were utilized to explore the biological functions of AL122125.1.ResultsA total of 351 m6A-related lncRNAs were obtained from TCGA. Seven lncRNAs demonstrated prognostic values. A further multivariate Cox regression assay constructed a risk model consisting of two lncRNAs (AL122125.1 and HORMAD2-AS1). The Kaplan–Meier analysis and area under the curve indicated that this risk model could be used to predict the prognosis of LUSC patients. The m6A-related lncRNAs were immune-associated. There were significant correlations between risk score and immune cell infiltration, immune functions, and expression of immune checkpoint inhibitors. Meanwhile, there were significant differences in the expression of m6A regulators between the high- and low-risk groups. Moreover, GO and KEGG analyses revealed that the upregulated expression of AL122125.1 was tumor-related.ConclusionIn this study, we constructed an m6A-related lncRNA risk model to predict the survival of LUSC patients. This study could provide a novel insight to the prognosis and treatment of LUSC patients. |
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