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Using bioinformatics approaches to identify survival-related oncomiRs as potential targets of miRNA-based treatments for lung adenocarcinoma

Authors :
Chia-Hsin Liu
Shu-Hsuan Liu
Yo-Liang Lai
Yi-Chun Cho
Fang-Hsin Chen
Li-Jie Lin
Pei-Hua Peng
Chia-Yang Li
Shu-Chi Wang
Ji-Lin Chen
Heng-Hsiung Wu
Min-Zu Wu
Yuh-Pyng Sher
Wei-Chung Cheng
Kai-Wen Hsu
Source :
Computational and Structural Biotechnology Journal, Vol 20, Iss , Pp 4626-4635 (2022)
Publication Year :
2022
Publisher :
Elsevier, 2022.

Abstract

Lung cancer is a major cause of cancer-associated deaths worldwide, and lung adenocarcinoma (LUAD) is the most common lung cancer subtype. Micro RNAs (miRNAs) regulate the pattern of gene expression in multiple cancer types and have been explored as potential drug development targets. To develop an oncomiR-based panel, we identified miRNA candidates that show differential expression patterns and are relevant to the worse 5-year overall survival outcomes in LUAD patient samples. We further evaluated various combinations of miRNA candidates for association with 5-year overall survival and identified a four-miRNA panel: miR-9-5p, miR-1246, miR-31-3p, and miR-3136-5p. The combination of these four miRNAs outperformed any single miRNA for predicting 5-year overall survival (hazard ratio [HR]: 3.47, log-rank p-value = 0.000271). Experiments were performed on lung cancer cell lines and animal models to validate the effects of these miRNAs. The results showed that singly transfected antagomiRs largely inhibited cell growth, migration, and invasion, and the combination of all four antagomiRs considerably reduced cell numbers, which is twice as effective as any single miRNA-targeted transfected. The in vivo studies revealed that antagomiR-mediated knockdown of all four miRNAs significantly reduced tumor growth and metastatic ability of lung cancer cells compared to the negative control group. The success of these in vivo and in vitro experiments suggested that these four identified oncomiRs may have therapeutic potential.

Details

Language :
English
ISSN :
20010370
Volume :
20
Issue :
4626-4635
Database :
Directory of Open Access Journals
Journal :
Computational and Structural Biotechnology Journal
Publication Type :
Academic Journal
Accession number :
edsdoj.0142da904d0e48d8bb1bb3790a3773d0
Document Type :
article
Full Text :
https://doi.org/10.1016/j.csbj.2022.08.042