CoMI: consensus mutual information for tissue-specific gene signatures.

Tytuł:: CoMI: consensus mutual information for tissue-specific gene signatures.
Autorzy:: Huang SH; Institute of Bioinformatics and Systems Biology, National Yang Ming Chiao Tung University, Hsinchu, 300193, Taiwan.; Graphen Inc., New York, NY, 10110, USA.
Lo YS; Institute of Bioinformatics and Systems Biology, National Yang Ming Chiao Tung University, Hsinchu, 300193, Taiwan.
Luo YC; Institute of Bioinformatics and Systems Biology, National Yang Ming Chiao Tung University, Hsinchu, 300193, Taiwan.
Chuang YH; Institute of Bioinformatics and Systems Biology, National Yang Ming Chiao Tung University, Hsinchu, 300193, Taiwan.
Lee JY; Institute of Bioinformatics and Systems Biology, National Yang Ming Chiao Tung University, Hsinchu, 300193, Taiwan.
Yang JM; Institute of Bioinformatics and Systems Biology, National Yang Ming Chiao Tung University, Hsinchu, 300193, Taiwan. .; Department of Biological Science and Technology, National Yang Ming Chiao Tung University, Hsinchu, 300193, Taiwan. .; Center for Intelligent Drug Systems and Smart Bio-Devices, National Yang Ming Chiao Tung University, Hsinchu, 300193, Taiwan. .
Źródło:: BMC bioinformatics [BMC Bioinformatics] 2022 Apr 19; Vol. 22 (Suppl 10), pp. 624. Date of Electronic Publication: 2022 Apr 19.
Typ publikacji:: Journal Article
Język:: English
Imprint Name(s):: Original Publication: [London] : BioMed Central, 2000-
MeSH Terms:: Gene Expression Regulation, Neoplastic*
Neoplasms*/genetics
Consensus ; Gene Expression Profiling ; Humans ; Precision Medicine
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Grant Information:: MOST 109-2634-F-009-021 Ministry of Science and Technology, Taiwan; MOST 109-2321-B-009-007 Ministry of Science and Technology, Taiwan; NHRI-EX109-10504PI National Health Research Institutes
Contributed Indexing:: Keywords: Omics data; Prognostic gene signature; Tissue-specific gene signature
Entry Date(s):: Date Created: 20220420 Date Completed: 20220421 Latest Revision: 20220716
Update Code:: 20240104
PubMed Central ID:: PMC9019939
DOI:: 10.1186/s12859-022-04682-2
PMID:: 35439942
: Czasopismo naukowe

Pełny tekst

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.
(© 2022. The Author(s).)

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