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Discovery of tumor immune infiltration-related snoRNAs for predicting tumor immune microenvironment status and prognosis in lung adenocarcinoma

Authors :
Qiong Chen
Bingrong Zhao
Lu Bai
Juan Jiang
Wang Ya
Yuanyuan Li
Chengping Hu
Changjing Cai
Rongjun Wan
Source :
Computational and Structural Biotechnology Journal, Vol 19, Iss, Pp 6386-6399 (2021), Computational and Structural Biotechnology Journal
Publication Year :
2021
Publisher :
Elsevier, 2021.

Abstract

Graphical abstract<br />Lung adenocarcinoma (LUAD) has a high mortality rate and is difficult to diagnose and treat in its early stage. Previous studies have demonstrated that small nucleolar RNAs (snoRNAs) play a critical role in tumor immune infiltration and the development of a variety of solid tumors. However, there have been no studies on the correlation between tumor-infiltrating immune-related snoRNAs (TIISRs) and LUAD. In this study, we filtered six immune-related snoRNAs based on the tissue specificity index (TSI) and expression profile of all snoRNAs between all LUAD cell lines from the Cancer Cell Line Encyclopedia and 21 types of immune cells from the Gene Expression Omnibus database. Further, we performed real-time quantitative polymerase chain reaction (RT-qPCR) to validate the expression status of these snoRNAs on peripheral blood mononuclear cells (PBMCs) and lung cancer cell lines. Next, we developed a TIISR signature based on the expression profiles of snoRNAs from 479 LUAD patients filtered by the random survival forest algorithm. We then analyzed the value of this TIISR signature (TIISR risk score) for assessing tumor immune infiltration, immune checkpoint inhibitor (ICI) treatment response, and the prognosis of LUAD between groups with high and low TIISR risk score. Further, we found that the TIISR risk score groups showed significant differences in biological characteristics and that the risk score could be used to assess the level of tumor immune cell infiltration, thereby predicting prognosis and responsiveness to immunotherapy in LUAD patients.

Details

Language :
English
ISSN :
20010370
Volume :
19
Database :
OpenAIRE
Journal :
Computational and Structural Biotechnology Journal
Accession number :
edsair.doi.dedup.....3e2d4043c8554ee6af7977744fd1f51b