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Identification and validation of an autophagy-related long non-coding RNA signature as a prognostic biomarker for patients with lung adenocarcinoma

BACKGROUND: Lung adenocarcinoma (LUAD) is the most predominant pathological subtype of lung cancer, accounting for 40–70% of all lung cancer cases. Although significant improvements have been made in the screening, diagnosis, and precise management in recent years, the prognosis of LUAD remains blea...

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Detalles Bibliográficos
Autores principales: Jiang, Aimin, Liu, Na, Bai, Shuheng, Wang, Jingjing, Gao, Huan, Zheng, Xiaoqiang, Fu, Xiao, Ren, Mengdi, Zhang, Xiaoni, Tian, Tao, Ruan, Zhiping, Liang, Xuan, Yao, Yu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: AME Publishing Company 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7947511/
https://www.ncbi.nlm.nih.gov/pubmed/33717544
http://dx.doi.org/10.21037/jtd-20-2803
Descripción
Sumario:BACKGROUND: Lung adenocarcinoma (LUAD) is the most predominant pathological subtype of lung cancer, accounting for 40–70% of all lung cancer cases. Although significant improvements have been made in the screening, diagnosis, and precise management in recent years, the prognosis of LUAD remains bleak. This study aimed to investigate the prognostic significance of autophagy-related long non-coding RNAs (lncRNAs) and construct an autophagy-related lncRNA prognostic model in LUAD. METHODS: The gene expression data of LUAD patients were obtained from The Cancer Genome Atlas (TCGA) database. All autophagy-related genes were downloaded from the Human Autophagy Database (HADb). Spearman’s correlation test was exploited to identify potential autophagy-related lncRNAs. The multivariate Cox regression analysis was used to construct the prognostic signature, which divided LUAD patients into high-risk and low-risk groups. Subsequently, the receiver operating characteristic (ROC) curves were generated to assess the predictive ability of this prognostic model for overall survival (OS) in these individuals. Then, the Gene set enrichment analysis (GSEA) was conducted to execute pathway enrichment analysis. Finally, a multidimensional validation was exploited to verify our findings. RESULTS: A total of 1,144 autophagy-related lncRNAs were identified to construct the co-expression network via Spearman’s correlation test (|R(2)| >0.4 and P≤0.001). Ultimately, a 16 autophagy-related lncRNAs prognostic model was constructed, and the area under the ROC curve (AUC) was 0.775. The results of GSEA enrichment analysis showed that the genes in the high-risk group were mainly enriched in cell cycle and p53 signaling pathways. The results of the multidimensional database validation indicated that the expression level of BIRC5 was significantly correlated with the expression level of TMPO-AS1. Furthermore, both TMPO-AS1 and BIRC5 had a higher expression level in LUAD samples. LUAD patients with high expression levels of TMPO-AS1 and BIRC5 were correlated with advanced disease stage and poor OS. CONCLUSIONS: In summary, our results suggested that the prognostic signature of the 16 autophagy-related lncRNAs has significant prognostic value for LUAD patients. Furthermore, TMPO-AS1 and BIRC5 are potential predictors and therapeutic targets in these individuals.