Quantitative or qualitative transcriptional diagnostic signatures? A case study for colorectal cancer
Abstract Background Due to experimental batch effects, the application of a quantitative transcriptional signature for disease diagnoses commonly requires inter-sample data normalization, which would be hardly applicable under common clinical settings. Many cancers might have qualitative differences...
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| Format: | Article |
| Language: | English |
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BMC
2018-01-01
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| Series: | BMC Genomics |
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| Online Access: | http://link.springer.com/article/10.1186/s12864-018-4446-y |
| _version_ | 1828850374204719104 |
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| author | Qingzhou Guan Haidan Yan Yanhua Chen Baotong Zheng Hao Cai Jun He Kai Song You Guo Lu Ao Huaping Liu Wenyuan Zhao Xianlong Wang Zheng Guo |
| author_facet | Qingzhou Guan Haidan Yan Yanhua Chen Baotong Zheng Hao Cai Jun He Kai Song You Guo Lu Ao Huaping Liu Wenyuan Zhao Xianlong Wang Zheng Guo |
| author_sort | Qingzhou Guan |
| collection | DOAJ |
| description | Abstract Background Due to experimental batch effects, the application of a quantitative transcriptional signature for disease diagnoses commonly requires inter-sample data normalization, which would be hardly applicable under common clinical settings. Many cancers might have qualitative differences with the non-cancer states in the gene expression pattern. Therefore, it is reasonable to explore the power of qualitative diagnostic signatures which are robust against experimental batch effects and other random factors. Results Firstly, using data of technical replicate samples from the MicroArray Quality Control (MAQC) project, we demonstrated that the low-throughput PCR-based technologies also exist large measurement variations for gene expression even when the samples were measured in the same test site. Then, we demonstrated the critical limitation of low stability for classifiers based on quantitative transcriptional signatures in applications to individual samples through a case study using a support vector machine and a naïve Bayesian classifier to discriminate colorectal cancer tissues from normal tissues. To address this problem, we identified a signature consisting of three gene pairs for discriminating colorectal cancer tissues from non-cancer (normal and inflammatory bowel disease) tissues based on within-sample relative expression orderings (REOs) of these gene pairs. The signature was well verified using 22 independent datasets measured by different microarray and RNA_seq platforms, obviating the need of inter-sample data normalization. Conclusions Subtle quantitative information of gene expression measurements tends to be unstable under current technical conditions, which will introduce uncertainty to clinical applications of the quantitative transcriptional diagnostic signatures. For diagnosis of disease states with qualitative transcriptional characteristics, the qualitative REO-based signatures could be robustly applied to individual samples measured by different platforms. |
| first_indexed | 2024-12-12T23:10:43Z |
| format | Article |
| id | doaj.art-e4b0bcd36fa145f8b6c00a1ea6b17206 |
| institution | Directory Open Access Journal |
| issn | 1471-2164 |
| language | English |
| last_indexed | 2024-12-12T23:10:43Z |
| publishDate | 2018-01-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Genomics |
| spelling | doaj.art-e4b0bcd36fa145f8b6c00a1ea6b172062022-12-22T00:08:36ZengBMCBMC Genomics1471-21642018-01-0119111110.1186/s12864-018-4446-yQuantitative or qualitative transcriptional diagnostic signatures? A case study for colorectal cancerQingzhou Guan0Haidan Yan1Yanhua Chen2Baotong Zheng3Hao Cai4Jun He5Kai Song6You Guo7Lu Ao8Huaping Liu9Wenyuan Zhao10Xianlong Wang11Zheng Guo12Fujian Key Laboratory of Medical Bioinformatics, Key Laboratory of Ministry of Education for Gastrointestinal Cancer, Fujian Medical UniversityFujian Key Laboratory of Medical Bioinformatics, Key Laboratory of Ministry of Education for Gastrointestinal Cancer, Fujian Medical UniversityFujian Key Laboratory of Medical Bioinformatics, Key Laboratory of Ministry of Education for Gastrointestinal Cancer, Fujian Medical UniversityFujian Key Laboratory of Medical Bioinformatics, Key Laboratory of Ministry of Education for Gastrointestinal Cancer, Fujian Medical UniversityFujian Key Laboratory of Medical Bioinformatics, Key Laboratory of Ministry of Education for Gastrointestinal Cancer, Fujian Medical UniversityFujian Key Laboratory of Medical Bioinformatics, Key Laboratory of Ministry of Education for Gastrointestinal Cancer, Fujian Medical UniversityCollege of Bioinformatics Science and Technology, Harbin Medical UniversityFujian Key Laboratory of Medical Bioinformatics, Key Laboratory of Ministry of Education for Gastrointestinal Cancer, Fujian Medical UniversityFujian Key Laboratory of Medical Bioinformatics, Key Laboratory of Ministry of Education for Gastrointestinal Cancer, Fujian Medical UniversityFujian Key Laboratory of Medical Bioinformatics, Key Laboratory of Ministry of Education for Gastrointestinal Cancer, Fujian Medical UniversityCollege of Bioinformatics Science and Technology, Harbin Medical UniversityFujian Key Laboratory of Medical Bioinformatics, Key Laboratory of Ministry of Education for Gastrointestinal Cancer, Fujian Medical UniversityFujian Key Laboratory of Medical Bioinformatics, Key Laboratory of Ministry of Education for Gastrointestinal Cancer, Fujian Medical UniversityAbstract Background Due to experimental batch effects, the application of a quantitative transcriptional signature for disease diagnoses commonly requires inter-sample data normalization, which would be hardly applicable under common clinical settings. Many cancers might have qualitative differences with the non-cancer states in the gene expression pattern. Therefore, it is reasonable to explore the power of qualitative diagnostic signatures which are robust against experimental batch effects and other random factors. Results Firstly, using data of technical replicate samples from the MicroArray Quality Control (MAQC) project, we demonstrated that the low-throughput PCR-based technologies also exist large measurement variations for gene expression even when the samples were measured in the same test site. Then, we demonstrated the critical limitation of low stability for classifiers based on quantitative transcriptional signatures in applications to individual samples through a case study using a support vector machine and a naïve Bayesian classifier to discriminate colorectal cancer tissues from normal tissues. To address this problem, we identified a signature consisting of three gene pairs for discriminating colorectal cancer tissues from non-cancer (normal and inflammatory bowel disease) tissues based on within-sample relative expression orderings (REOs) of these gene pairs. The signature was well verified using 22 independent datasets measured by different microarray and RNA_seq platforms, obviating the need of inter-sample data normalization. Conclusions Subtle quantitative information of gene expression measurements tends to be unstable under current technical conditions, which will introduce uncertainty to clinical applications of the quantitative transcriptional diagnostic signatures. For diagnosis of disease states with qualitative transcriptional characteristics, the qualitative REO-based signatures could be robustly applied to individual samples measured by different platforms.http://link.springer.com/article/10.1186/s12864-018-4446-yClassifiersDiagnostic signatureRelative expression orderingsPlatformBatch effects |
| spellingShingle | Qingzhou Guan Haidan Yan Yanhua Chen Baotong Zheng Hao Cai Jun He Kai Song You Guo Lu Ao Huaping Liu Wenyuan Zhao Xianlong Wang Zheng Guo Quantitative or qualitative transcriptional diagnostic signatures? A case study for colorectal cancer BMC Genomics Classifiers Diagnostic signature Relative expression orderings Platform Batch effects |
| title | Quantitative or qualitative transcriptional diagnostic signatures? A case study for colorectal cancer |
| title_full | Quantitative or qualitative transcriptional diagnostic signatures? A case study for colorectal cancer |
| title_fullStr | Quantitative or qualitative transcriptional diagnostic signatures? A case study for colorectal cancer |
| title_full_unstemmed | Quantitative or qualitative transcriptional diagnostic signatures? A case study for colorectal cancer |
| title_short | Quantitative or qualitative transcriptional diagnostic signatures? A case study for colorectal cancer |
| title_sort | quantitative or qualitative transcriptional diagnostic signatures a case study for colorectal cancer |
| topic | Classifiers Diagnostic signature Relative expression orderings Platform Batch effects |
| url | http://link.springer.com/article/10.1186/s12864-018-4446-y |
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