AUTHOR=Weng Chengyin , Wang Lina , Liu Guolong , Guan Mingmei , Lu Lin 

TITLE=Identification of a N6-Methyladenosine (m6A)-Related lncRNA Signature for Predicting the Prognosis and Immune Landscape of Lung Squamous Cell Carcinoma

JOURNAL=Frontiers in Oncology

VOLUME=Volume 11 - 2021

YEAR=2021

URL=https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2021.763027

DOI=10.3389/fonc.2021.763027

ISSN=2234-943X

ABSTRACT=Background:
m6A-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. 
Material and methods:
RNA-sequencing and clinical data of LUSC patients were downloaded from the Cancer Genome Atlas (TCGA) database. m6A-related lncRNAs were identified by using Pearson correlation assay. Univariate the multivariate cox regression analyses were utilized to construct a risk model. The performance of the risk model was validated using Kaplan Meier survival analysis, receiver operating curve (ROC). Immune estimation of LUSC were 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.
Results:
A total of 351 m6A-related lncRNAs were obtained from TCGA. Seven lncRNAs demonstrated prognostic values. Further multivariate cox regression assay constructed a risk model consisted of 2 lncRNAs (AL122125.1 and HORMAD2-AS1). Kaplan-Meier analysis and area under the curve (AUC) indicated that this risk model could be used to predict the prognosis of LUSC patients. The m6A-retaed 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 upregulated expression of AL122125.1 was tumor-related.
Conclusion:
In this study, we constructed a m6A-related lncRNA risk model to predict the survival of LUSC patients. This study could provide novel insight to the prognosis and treatment of LUSC patients.