Oncocytoma-Related Gene Signature to Differentiate Chromophobe Renal Cancer and Oncocytoma Using Machine Learning
Publicly available gene expression datasets were analyzed to develop a chromophobe and oncocytoma related gene signature (COGS) to distinguish chRCC from RO. The datasets GSE11151, GSE19982, GSE2109, GSE8271 and GSE11024 were combined into a discovery dataset. The transcriptomic differences were ide...
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MDPI AG
2022-01-01
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| Series: | Cells |
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| Online Access: | https://www.mdpi.com/2073-4409/11/2/287 |
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| author | Khaled Bin Satter Paul Minh Huy Tran Lynn Kim Hoang Tran Zach Ramsey Katheine Pinkerton Shan Bai Natasha M. Savage Sravan Kavuri Martha K. Terris Jin-Xiong She Sharad Purohit |
| author_facet | Khaled Bin Satter Paul Minh Huy Tran Lynn Kim Hoang Tran Zach Ramsey Katheine Pinkerton Shan Bai Natasha M. Savage Sravan Kavuri Martha K. Terris Jin-Xiong She Sharad Purohit |
| author_sort | Khaled Bin Satter |
| collection | DOAJ |
| description | Publicly available gene expression datasets were analyzed to develop a chromophobe and oncocytoma related gene signature (COGS) to distinguish chRCC from RO. The datasets GSE11151, GSE19982, GSE2109, GSE8271 and GSE11024 were combined into a discovery dataset. The transcriptomic differences were identified with unsupervised learning in the discovery dataset (97.8% accuracy) with density based UMAP (DBU). The top 30 genes were identified by univariate gene expression analysis and ROC analysis, to create a gene signature called COGS. COGS, combined with DBU, was able to differentiate chRCC from RO in the discovery dataset with an accuracy of 97.8%. The classification accuracy of COGS was validated in an independent meta-dataset consisting of TCGA-KICH and GSE12090, where COGS could differentiate chRCC from RO with 100% accuracy. The differentially expressed genes were involved in carbohydrate metabolism, transcriptomic regulation by TP53, beta-catenin-dependent Wnt signaling, and cytokine (IL-4 and IL-13) signaling highly active in cancer cells. Using multiple datasets and machine learning, we constructed and validated COGS as a tool that can differentiate chRCC from RO and complement histology in routine clinical practice to distinguish these two tumors. |
| first_indexed | 2024-03-10T01:43:20Z |
| format | Article |
| id | doaj.art-6f2abb6a8e8e4a61a59265a58fa352ef |
| institution | Directory Open Access Journal |
| issn | 2073-4409 |
| language | English |
| last_indexed | 2024-03-10T01:43:20Z |
| publishDate | 2022-01-01 |
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| record_format | Article |
| series | Cells |
| spelling | doaj.art-6f2abb6a8e8e4a61a59265a58fa352ef2023-11-23T13:19:07ZengMDPI AGCells2073-44092022-01-0111228710.3390/cells11020287Oncocytoma-Related Gene Signature to Differentiate Chromophobe Renal Cancer and Oncocytoma Using Machine LearningKhaled Bin Satter0Paul Minh Huy Tran1Lynn Kim Hoang Tran2Zach Ramsey3Katheine Pinkerton4Shan Bai5Natasha M. Savage6Sravan Kavuri7Martha K. Terris8Jin-Xiong She9Sharad Purohit10Center for Biotechnology and Genomic Medicine, Medical College of Georgia, Augusta University, 1120 15th Str., Augusta, GA 30912, USACenter for Biotechnology and Genomic Medicine, Medical College of Georgia, Augusta University, 1120 15th Str., Augusta, GA 30912, USACenter for Biotechnology and Genomic Medicine, Medical College of Georgia, Augusta University, 1120 15th Str., Augusta, GA 30912, USADepartment of Pathology, Medical College of Georgia, Augusta University, 1120 15th Str., Augusta, GA 30912, USACenter for Biotechnology and Genomic Medicine, Medical College of Georgia, Augusta University, 1120 15th Str., Augusta, GA 30912, USACenter for Biotechnology and Genomic Medicine, Medical College of Georgia, Augusta University, 1120 15th Str., Augusta, GA 30912, USADepartment of Pathology, Medical College of Georgia, Augusta University, 1120 15th Str., Augusta, GA 30912, USADepartment of Pathology, Medical College of Georgia, Augusta University, 1120 15th Str., Augusta, GA 30912, USADepartment of Urology, Medical College of Georgia, Augusta University, 1120 15th Str., Augusta, GA 30912, USACenter for Biotechnology and Genomic Medicine, Medical College of Georgia, Augusta University, 1120 15th Str., Augusta, GA 30912, USACenter for Biotechnology and Genomic Medicine, Medical College of Georgia, Augusta University, 1120 15th Str., Augusta, GA 30912, USAPublicly available gene expression datasets were analyzed to develop a chromophobe and oncocytoma related gene signature (COGS) to distinguish chRCC from RO. The datasets GSE11151, GSE19982, GSE2109, GSE8271 and GSE11024 were combined into a discovery dataset. The transcriptomic differences were identified with unsupervised learning in the discovery dataset (97.8% accuracy) with density based UMAP (DBU). The top 30 genes were identified by univariate gene expression analysis and ROC analysis, to create a gene signature called COGS. COGS, combined with DBU, was able to differentiate chRCC from RO in the discovery dataset with an accuracy of 97.8%. The classification accuracy of COGS was validated in an independent meta-dataset consisting of TCGA-KICH and GSE12090, where COGS could differentiate chRCC from RO with 100% accuracy. The differentially expressed genes were involved in carbohydrate metabolism, transcriptomic regulation by TP53, beta-catenin-dependent Wnt signaling, and cytokine (IL-4 and IL-13) signaling highly active in cancer cells. Using multiple datasets and machine learning, we constructed and validated COGS as a tool that can differentiate chRCC from RO and complement histology in routine clinical practice to distinguish these two tumors.https://www.mdpi.com/2073-4409/11/2/287chromophobeoncocytomaclassificationmachine learningtranscriptomicgene signature |
| spellingShingle | Khaled Bin Satter Paul Minh Huy Tran Lynn Kim Hoang Tran Zach Ramsey Katheine Pinkerton Shan Bai Natasha M. Savage Sravan Kavuri Martha K. Terris Jin-Xiong She Sharad Purohit Oncocytoma-Related Gene Signature to Differentiate Chromophobe Renal Cancer and Oncocytoma Using Machine Learning Cells chromophobe oncocytoma classification machine learning transcriptomic gene signature |
| title | Oncocytoma-Related Gene Signature to Differentiate Chromophobe Renal Cancer and Oncocytoma Using Machine Learning |
| title_full | Oncocytoma-Related Gene Signature to Differentiate Chromophobe Renal Cancer and Oncocytoma Using Machine Learning |
| title_fullStr | Oncocytoma-Related Gene Signature to Differentiate Chromophobe Renal Cancer and Oncocytoma Using Machine Learning |
| title_full_unstemmed | Oncocytoma-Related Gene Signature to Differentiate Chromophobe Renal Cancer and Oncocytoma Using Machine Learning |
| title_short | Oncocytoma-Related Gene Signature to Differentiate Chromophobe Renal Cancer and Oncocytoma Using Machine Learning |
| title_sort | oncocytoma related gene signature to differentiate chromophobe renal cancer and oncocytoma using machine learning |
| topic | chromophobe oncocytoma classification machine learning transcriptomic gene signature |
| url | https://www.mdpi.com/2073-4409/11/2/287 |
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