CoMI: consensus mutual information for tissue-specific gene signatures
Abstract Background The gene signatures have been considered as a promising early diagnosis and prognostic analysis to identify disease subtypes and to determine subsequent treatments. Tissue-specific gene signatures of a specific disease are an emergency requirement for precision medicine to improv...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2022-04-01
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| Series: | BMC Bioinformatics |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12859-022-04682-2 |
| _version_ | 1817990774756737024 |
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| author | Sing-Han Huang Yu-Shu Lo Yong-Chun Luo Yi-Hsuan Chuang Jung-Yu Lee Jinn-Moon Yang |
| author_facet | Sing-Han Huang Yu-Shu Lo Yong-Chun Luo Yi-Hsuan Chuang Jung-Yu Lee Jinn-Moon Yang |
| author_sort | Sing-Han Huang |
| collection | DOAJ |
| description | Abstract Background The gene signatures have been considered as a promising early diagnosis and prognostic analysis to identify disease subtypes and to determine subsequent treatments. Tissue-specific gene signatures of a specific disease are an emergency requirement for precision medicine to improve the accuracy and reduce the side effects. Currently, many approaches have been proposed for identifying gene signatures for diagnosis and prognostic. However, they often lack of tissue-specific gene signatures. Results Here, we propose a new method, consensus mutual information (CoMI) for analyzing omics data and discovering gene signatures. CoMI can identify differentially expressed genes in multiple cancer omics data for reflecting both cancer-related and tissue-specific signatures, such as Cell growth and death in multiple cancers, Xenobiotics biodegradation and metabolism in LIHC, and Nervous system in GBM. Our method identified 50-gene signatures effectively distinguishing the GBM patients into high- and low-risk groups (log-rank p = 0.006) for diagnosis and prognosis. Conclusions Our results demonstrate that CoMI can identify significant and consistent gene signatures with tissue-specific properties and can predict clinical outcomes for interested diseases. We believe that CoMI is useful for analyzing omics data and discovering gene signatures of diseases. |
| first_indexed | 2024-04-14T01:04:25Z |
| format | Article |
| id | doaj.art-765db4176cea43ac8806f7b5d5a40d30 |
| institution | Directory Open Access Journal |
| issn | 1471-2105 |
| language | English |
| last_indexed | 2024-04-14T01:04:25Z |
| publishDate | 2022-04-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Bioinformatics |
| spelling | doaj.art-765db4176cea43ac8806f7b5d5a40d302022-12-22T02:21:18ZengBMCBMC Bioinformatics1471-21052022-04-0122S1011810.1186/s12859-022-04682-2CoMI: consensus mutual information for tissue-specific gene signaturesSing-Han Huang0Yu-Shu Lo1Yong-Chun Luo2Yi-Hsuan Chuang3Jung-Yu Lee4Jinn-Moon Yang5Institute of Bioinformatics and Systems Biology, National Yang Ming Chiao Tung UniversityInstitute of Bioinformatics and Systems Biology, National Yang Ming Chiao Tung UniversityInstitute of Bioinformatics and Systems Biology, National Yang Ming Chiao Tung UniversityInstitute of Bioinformatics and Systems Biology, National Yang Ming Chiao Tung UniversityInstitute of Bioinformatics and Systems Biology, National Yang Ming Chiao Tung UniversityInstitute of Bioinformatics and Systems Biology, National Yang Ming Chiao Tung UniversityAbstract Background The gene signatures have been considered as a promising early diagnosis and prognostic analysis to identify disease subtypes and to determine subsequent treatments. Tissue-specific gene signatures of a specific disease are an emergency requirement for precision medicine to improve the accuracy and reduce the side effects. Currently, many approaches have been proposed for identifying gene signatures for diagnosis and prognostic. However, they often lack of tissue-specific gene signatures. Results Here, we propose a new method, consensus mutual information (CoMI) for analyzing omics data and discovering gene signatures. CoMI can identify differentially expressed genes in multiple cancer omics data for reflecting both cancer-related and tissue-specific signatures, such as Cell growth and death in multiple cancers, Xenobiotics biodegradation and metabolism in LIHC, and Nervous system in GBM. Our method identified 50-gene signatures effectively distinguishing the GBM patients into high- and low-risk groups (log-rank p = 0.006) for diagnosis and prognosis. Conclusions Our results demonstrate that CoMI can identify significant and consistent gene signatures with tissue-specific properties and can predict clinical outcomes for interested diseases. We believe that CoMI is useful for analyzing omics data and discovering gene signatures of diseases.https://doi.org/10.1186/s12859-022-04682-2Tissue-specific gene signaturePrognostic gene signatureOmics data |
| spellingShingle | Sing-Han Huang Yu-Shu Lo Yong-Chun Luo Yi-Hsuan Chuang Jung-Yu Lee Jinn-Moon Yang CoMI: consensus mutual information for tissue-specific gene signatures BMC Bioinformatics Tissue-specific gene signature Prognostic gene signature Omics data |
| title | CoMI: consensus mutual information for tissue-specific gene signatures |
| title_full | CoMI: consensus mutual information for tissue-specific gene signatures |
| title_fullStr | CoMI: consensus mutual information for tissue-specific gene signatures |
| title_full_unstemmed | CoMI: consensus mutual information for tissue-specific gene signatures |
| title_short | CoMI: consensus mutual information for tissue-specific gene signatures |
| title_sort | comi consensus mutual information for tissue specific gene signatures |
| topic | Tissue-specific gene signature Prognostic gene signature Omics data |
| url | https://doi.org/10.1186/s12859-022-04682-2 |
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