Abstract
The evolution of next-generation sequencing technology has resulted in a generation of large amounts of cancer genomic data. Therefore, increasingly complex techniques are required to appropriately analyze this data in order to determine its clinical relevance. In this study, we applied a neural network-based technique to analyze data from The Cancer Genome Atlas and extract useful microRNA (miRNA) features for predicting the prognosis of patients with lung adenocarcinomas (LUAD). Using the Cascaded Wx platform, we identified and ranked miRNAs that affected LUAD patient survival and selected the two top-ranked miRNAs (miR-374a and miR-374b) for measurement of their expression levels in patient tumor tissues and in lung cancer cells exhibiting an altered epithelial-to-mesenchymal transition (EMT) status. Analysis of miRNA expression from tumor samples revealed that high miR-374a/b expression was associated with poor patient survival rates. In lung cancer cells, the EMT signal induced miR-374a/b expression, which, in turn, promoted EMT and invasiveness. These findings demonstrated that this approach enabled effective identification and validation of prognostic miRNA markers in LUAD, suggesting its potential efficacy for clinical use.
Original language | English |
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Article number | 1890 |
Pages (from-to) | 1-14 |
Number of pages | 14 |
Journal | Cancers |
Volume | 12 |
Issue number | 7 |
DOIs | |
State | Published - Jul 2020 |
Bibliographical note
Funding Information:Funding: This work was supported by a grant from the National R&D Program for Cancer Control, Ministry of Health & Welfare, Republic of Korea (1720100 to Y.H.K.), and by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (NRF-2019R1F1A1057968 and 2020R1A5A2019210 to Y.-H.A.). This study was also supported by the Health Fellowship Foundation (to J.S.K.).
Funding Information:
This work was supported by a grant from the National R&D Program for Cancer Control, Ministry of Health & Welfare, Republic of Korea (1720100 to Y.H.K.), and by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (NRF-2019R1F1A1057968 and 2020R1A5A2019210 to Y.-H.A.). This study was also supported by the Health Fellowship Foundation (to J.S.K.).
Publisher Copyright:
© 2020 by the authors. Licensee MDPI, Basel, Switzerland.
Keywords
- Cascaded Wx
- Lung adenocarcinoma
- Machine learning
- MicroRNA
- Prognosis