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http://hdl.handle.net/10125/69023IDENTIFICATION OF LNCRNA BIOMARKERS FOR LUNG CANCR THROUGH INTEGRATIVE CROSS-PLATFORM DATA ANALYSIS
| File | Size | Format | ||
|---|---|---|---|---|
| Zhao hawii 0085O 10586.pdf | 4.5 MB | Adobe PDF | View/Open |
Item Summary
| Title: | IDENTIFICATION OF LNCRNA BIOMARKERS FOR LUNG CANCR THROUGH INTEGRATIVE CROSS-PLATFORM DATA ANALYSIS |
| Authors: | Zhao, Tianying |
| Contributors: | Deng, Youping (advisor) Molecular Biosciences and Bioengineering (department) |
| Keywords: | Bioinformatics lncRNA lung cancer microarray RNA-Seq |
| Date Issued: | 2020 |
| Publisher: | University of Hawai'i at Manoa |
| Abstract: | This study was designed to identify lncRNA biomarker candidates using lung cancer data from RNA-Seq and microarray platforms separately. Lung cancer datasets were obtained from the Gene Expression Omnibus (GEO, n = 287) and The Cancer Genome Atlas (TCGA, n = 216) repositories, only common lncRNAs were used. Differentially expressed (DE) lncRNAs in tumor with respect to normal were selected from the Affymetrix and TCGA datasets. A training model consisting of the top 20 DE Affymetrix lncRNAs was used for validation in the TCGA and Agilent datasets. A second similar training model was generated using the TCGA dataset. First, a model using the top 20 DE lncRNAs from Affymetrix for training and validated using TCGA and Agilent, achieved high prediction accuracy for both training (98.5% AUC for Affymetrix) and validation (99.2% AUC for TCGA and 92.8% AUC for Agilent). A similar model using the top 20 DE lncRNAs from TCGA for training and validated using Affymetrix and Agilent, also achieved high prediction accuracy for both training (97.7% AUC for TCGA) and validation (96.5% AUC for Affymetrix and 80.9% AUC for Agilent). Eight overlapped lncRNAs were found to be potentially related with lung cancer diagnosis but not with prognosis. |
| Pages/Duration: | 120 pages |
| URI: | http://hdl.handle.net/10125/69023 |
| Rights: | All UHM dissertations and theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission from the copyright owner. |
| Appears in Collections: |
M.S. - Molecular Biosciences and Bioengineering |
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