Reanalysis of Non-Small-Cell Lung Cancer Microarray Gene Expression Data
Cancer is one of the leading causes of death in many countries, and this continues to be the case because of the lack of sufficient treatment. One of the most common types is non-small-cell lung cancer (NSCLC). The increasingly large and diverse public datasets about NSCLC constitute a rich source o...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-03-01
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| Series: | Proceedings |
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| Online Access: | https://www.mdpi.com/2504-3900/74/1/22 |
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| author | Tcharé Adnaane Bawa Yalçın Özkan Çiğdem Selçukcan Erol |
| author_facet | Tcharé Adnaane Bawa Yalçın Özkan Çiğdem Selçukcan Erol |
| author_sort | Tcharé Adnaane Bawa |
| collection | DOAJ |
| description | Cancer is one of the leading causes of death in many countries, and this continues to be the case because of the lack of sufficient treatment. One of the most common types is non-small-cell lung cancer (NSCLC). The increasingly large and diverse public datasets about NSCLC constitute a rich source of data on which several analyses can be performed so as to find candidate oncogenic drivers or therapeutic targets. The aim of this study is to reanalyze an existing NSCLC NCBI GEO Dataset (accession = GSE19804) in order to see if novel involved genes can be found. For this, we used microarray technology for preprocessing and, based on random forest, support vector machine and C5.0 decision tree models, made a comparison of the 10 most important genes recorded. This study was realized with R-Studio 4.0.2 and Bioconductor 3.11. In conclusion, the EFNA4 gene and other genes, namely KANK3, GRK5, CLIC5, SH3GL3, ACACB, LIN7A, JCAD, and NEDD1, are thought to be potential genes that may play a role in NSCLC and it is recommended that researchers working in the wet laboratory should focus on these genes. |
| first_indexed | 2024-03-10T13:00:08Z |
| format | Article |
| id | doaj.art-9353a4c554f54e649f4cb712ecd0e820 |
| institution | Directory Open Access Journal |
| issn | 2504-3900 |
| language | English |
| last_indexed | 2024-03-10T13:00:08Z |
| publishDate | 2021-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Proceedings |
| spelling | doaj.art-9353a4c554f54e649f4cb712ecd0e8202023-11-21T11:35:27ZengMDPI AGProceedings2504-39002021-03-017412210.3390/proceedings2021074022Reanalysis of Non-Small-Cell Lung Cancer Microarray Gene Expression DataTcharé Adnaane Bawa0Yalçın Özkan1Çiğdem Selçukcan Erol2Informatics Department, İstanbul University, 34134 Istanbul, TurkeyRetired Faculty Member, 34134 Istanbul, TurkeyInformatics Department, İstanbul University, 34134 Istanbul, TurkeyCancer is one of the leading causes of death in many countries, and this continues to be the case because of the lack of sufficient treatment. One of the most common types is non-small-cell lung cancer (NSCLC). The increasingly large and diverse public datasets about NSCLC constitute a rich source of data on which several analyses can be performed so as to find candidate oncogenic drivers or therapeutic targets. The aim of this study is to reanalyze an existing NSCLC NCBI GEO Dataset (accession = GSE19804) in order to see if novel involved genes can be found. For this, we used microarray technology for preprocessing and, based on random forest, support vector machine and C5.0 decision tree models, made a comparison of the 10 most important genes recorded. This study was realized with R-Studio 4.0.2 and Bioconductor 3.11. In conclusion, the EFNA4 gene and other genes, namely KANK3, GRK5, CLIC5, SH3GL3, ACACB, LIN7A, JCAD, and NEDD1, are thought to be potential genes that may play a role in NSCLC and it is recommended that researchers working in the wet laboratory should focus on these genes.https://www.mdpi.com/2504-3900/74/1/22non-small-cell lung cancermicroarraydata reanalysismachine learning |
| spellingShingle | Tcharé Adnaane Bawa Yalçın Özkan Çiğdem Selçukcan Erol Reanalysis of Non-Small-Cell Lung Cancer Microarray Gene Expression Data Proceedings non-small-cell lung cancer microarray data reanalysis machine learning |
| title | Reanalysis of Non-Small-Cell Lung Cancer Microarray Gene Expression Data |
| title_full | Reanalysis of Non-Small-Cell Lung Cancer Microarray Gene Expression Data |
| title_fullStr | Reanalysis of Non-Small-Cell Lung Cancer Microarray Gene Expression Data |
| title_full_unstemmed | Reanalysis of Non-Small-Cell Lung Cancer Microarray Gene Expression Data |
| title_short | Reanalysis of Non-Small-Cell Lung Cancer Microarray Gene Expression Data |
| title_sort | reanalysis of non small cell lung cancer microarray gene expression data |
| topic | non-small-cell lung cancer microarray data reanalysis machine learning |
| url | https://www.mdpi.com/2504-3900/74/1/22 |
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