View : 724 Download: 0
Identification of Novel microRNA Prognostic Markers Using Cascaded Wx, a Neural Network-Based Framework, in Lung Adenocarcinoma Patients
- Title
- Identification of Novel microRNA Prognostic Markers Using Cascaded Wx, a Neural Network-Based Framework, in Lung Adenocarcinoma Patients
- Authors
- Kim, Jeong Seon; Chun, Sang Hoon; Park, Sungsoo; Lee, Sieun; Kim, Sae Eun; Hong, Ji Hyung; Kang, Keunsoo; Ko, Yoon Ho; Ahn, Young-Ho
- Ewha Authors
- 안영호
- SCOPUS Author ID
- 안영호
- Issue Date
- 2020
- Journal Title
- CANCERS
- ISSN
- 2072-6694
- Citation
- CANCERS vol. 12, no. 7
- Keywords
- microRNA; lung adenocarcinoma; prognosis; Cascaded Wx; machine learning
- Publisher
- MDPI
- Indexed
- SCIE; SCOPUS
- Document Type
- Article
- 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.
- DOI
- 10.3390/cancers12071890
- Appears in Collections:
- 의과대학 > 의학과 > Journal papers
- Files in This Item:
There are no files associated with this item.
- Export
- RIS (EndNote)
- XLS (Excel)
- XML