A miRNA- and mRNA-seq-Based Feature Selection Approach for Kidney Cancer Biomakers
Microarray data sets have been used for predicting cancer biomarkers. Yet, replication of the prediction has not been fully satisfied. Recently, new data sets called deep sequencing data sets have been generated, with an advantage of less noise in computational analysis. In this study, we analyzed t...
| Main Author: | |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
SAGE Publishing
2020-02-01
|
| Series: | Cancer Informatics |
| Online Access: | https://doi.org/10.1177/1176935120908301 |
| _version_ | 1818517816102354944 |
|---|---|
| author | Shinuk Kim |
| author_facet | Shinuk Kim |
| author_sort | Shinuk Kim |
| collection | DOAJ |
| description | Microarray data sets have been used for predicting cancer biomarkers. Yet, replication of the prediction has not been fully satisfied. Recently, new data sets called deep sequencing data sets have been generated, with an advantage of less noise in computational analysis. In this study, we analyzed the kidney miRNA and mRNA sequence data sets for predicting cancer markers using 5 different statistical feature selection methods. In the results, we obtained 3 mRNA- and 27 miRNA-based cancer biomarkers to compare with the normal samples. In addition, we clustered the kidney cancer subtypes using a nonnegative matrix factorization method and obtained significant results of survival analysis from the 2 separate groups including miRNA-342 and its target eukaryotic translation initiation factor 5A ( EIF5A ). |
| first_indexed | 2024-12-11T01:01:24Z |
| format | Article |
| id | doaj.art-17e02c56e1c947dc8a80fe030223c0de |
| institution | Directory Open Access Journal |
| issn | 1176-9351 |
| language | English |
| last_indexed | 2024-12-11T01:01:24Z |
| publishDate | 2020-02-01 |
| publisher | SAGE Publishing |
| record_format | Article |
| series | Cancer Informatics |
| spelling | doaj.art-17e02c56e1c947dc8a80fe030223c0de2022-12-22T01:26:19ZengSAGE PublishingCancer Informatics1176-93512020-02-011910.1177/1176935120908301A miRNA- and mRNA-seq-Based Feature Selection Approach for Kidney Cancer BiomakersShinuk KimMicroarray data sets have been used for predicting cancer biomarkers. Yet, replication of the prediction has not been fully satisfied. Recently, new data sets called deep sequencing data sets have been generated, with an advantage of less noise in computational analysis. In this study, we analyzed the kidney miRNA and mRNA sequence data sets for predicting cancer markers using 5 different statistical feature selection methods. In the results, we obtained 3 mRNA- and 27 miRNA-based cancer biomarkers to compare with the normal samples. In addition, we clustered the kidney cancer subtypes using a nonnegative matrix factorization method and obtained significant results of survival analysis from the 2 separate groups including miRNA-342 and its target eukaryotic translation initiation factor 5A ( EIF5A ).https://doi.org/10.1177/1176935120908301 |
| spellingShingle | Shinuk Kim A miRNA- and mRNA-seq-Based Feature Selection Approach for Kidney Cancer Biomakers Cancer Informatics |
| title | A miRNA- and mRNA-seq-Based Feature Selection Approach for Kidney Cancer Biomakers |
| title_full | A miRNA- and mRNA-seq-Based Feature Selection Approach for Kidney Cancer Biomakers |
| title_fullStr | A miRNA- and mRNA-seq-Based Feature Selection Approach for Kidney Cancer Biomakers |
| title_full_unstemmed | A miRNA- and mRNA-seq-Based Feature Selection Approach for Kidney Cancer Biomakers |
| title_short | A miRNA- and mRNA-seq-Based Feature Selection Approach for Kidney Cancer Biomakers |
| title_sort | mirna and mrna seq based feature selection approach for kidney cancer biomakers |
| url | https://doi.org/10.1177/1176935120908301 |
| work_keys_str_mv | AT shinukkim amirnaandmrnaseqbasedfeatureselectionapproachforkidneycancerbiomakers AT shinukkim mirnaandmrnaseqbasedfeatureselectionapproachforkidneycancerbiomakers |