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Article

Identification of Novel microRNA Prognostic Markers Using Cascaded Wx, a Neural Network-Based Framework, in Lung Adenocarcinoma Patients

1
Department of Molecular Medicine, College of Medicine, Ewha Womans University, Seoul 07804, Korea
2
Inflammation-Cancer Microenvironment Research Center, College of Medicine, Ewha Womans University, Seoul 07804, Korea
3
Division of Oncology, Department of Internal Medicine, College of Medicine, The Catholic University of Korea, Seoul 06591, Korea
4
Deargen, Inc., Daejeon 34051, Korea
5
Department of Microbiology, College of Natural Sciences, Dankook University, Cheonan 31116, Korea
6
Cancer Research Institute, College of Medicine, The Catholic University of Korea, Seoul 06591, Korea
*
Authors to whom correspondence should be addressed.
These authors contributed equally to this manuscript.
Cancers 2020, 12(7), 1890; https://doi.org/10.3390/cancers12071890
Received: 24 June 2020 / Revised: 10 July 2020 / Accepted: 11 July 2020 / Published: 14 July 2020
(This article belongs to the Collection Cancer Biomarkers)
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. View Full-Text
Keywords: microRNA; lung adenocarcinoma; prognosis; Cascaded Wx; machine learning microRNA; lung adenocarcinoma; prognosis; Cascaded Wx; machine learning
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MDPI and ACS Style

Kim, J.S.; Chun, S.H.; Park, S.; Lee, S.; Kim, S.E.; Hong, J.H.; Kang, K.; Ko, Y.H.; Ahn, Y.-H. Identification of Novel microRNA Prognostic Markers Using Cascaded Wx, a Neural Network-Based Framework, in Lung Adenocarcinoma Patients. Cancers 2020, 12, 1890. https://doi.org/10.3390/cancers12071890

AMA Style

Kim JS, Chun SH, Park S, Lee S, Kim SE, Hong JH, Kang K, Ko YH, Ahn Y-H. Identification of Novel microRNA Prognostic Markers Using Cascaded Wx, a Neural Network-Based Framework, in Lung Adenocarcinoma Patients. Cancers. 2020; 12(7):1890. https://doi.org/10.3390/cancers12071890

Chicago/Turabian Style

Kim, Jeong S., Sang H. Chun, Sungsoo Park, Sieun Lee, Sae E. Kim, Ji H. Hong, Keunsoo Kang, Yoon H. Ko, and Young-Ho Ahn. 2020. "Identification of Novel microRNA Prognostic Markers Using Cascaded Wx, a Neural Network-Based Framework, in Lung Adenocarcinoma Patients" Cancers 12, no. 7: 1890. https://doi.org/10.3390/cancers12071890

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