Identification of a novel snoRNA expression signature associated with overall survival in patients with lung adenocarcinoma: A comprehensive analysis based on RNA sequencing dataset

Author:

Zhang Linbo, ,Xin Mei,Wang Peng

Abstract

<abstract> <p>Since multiple studies have reported that small nucleolar RNAs (snoRNAs) can be serve as prognostic biomarkers for cancers, however, the prognostic values of snoRNAs in lung adenocarcinoma (LUAD) remain unclear. Therefore, the main work of this study is to identify the prognostic snoRNAs of LUAD and conduct a comprehensive analysis. The Cancer Genome Atlas LUAD cohort whole-genome RNA-sequencing dataset is included in this study, prognostic analysis and multiple bioinformatics approaches are used for comprehensive analysis and identification of prognostic snoRNAs. There were seven LUAD prognostic snoRNAs were screened in current study. We also constructed a novel expression signature containing five LUAD prognostic snoRNAs (snoU109, SNORA5A, SNORA70, SNORD104 and U3). Survival analysis of this expression signature reveals that LUAD patients with high risk score was significantly related to an unfavourable overall survival (adjusted P = 0.01, adjusted hazard ratio = 1.476, 95% confidence interval = 1.096-1.987). Functional analysis indicated that LUAD patients with different risk score phenotypes had significant differences in cell cycle, apoptosis, integrin, transforming growth factor beta, ErbB, nuclear factor kappa B, mitogen-activated protein kinase, phosphatidylinositol-3-kinase and toll like receptor signaling pathway. Immune microenvironment analysis also indicated that there were significant differences in immune microenvironment scores among LUAD patients with different risk score. In conclusion, this study identified an novel expression signature containing five LUAD prognostic snoRNAs, which may be serve as an independent prognostic indicator for LUAD patients.</p> </abstract>

Publisher

American Institute of Mathematical Sciences (AIMS)

Subject

Applied Mathematics,Computational Mathematics,General Agricultural and Biological Sciences,Modeling and Simulation,General Medicine

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