CoMI: consensus mutual information for tissue-specific gene signatures.
BMC Bioinformatics
; 22(Suppl 10): 624, 2022 Apr 19.
Article
en En
| MEDLINE
| ID: mdl-35439942
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.Palabras clave
Texto completo:
1
Colección:
01-internacional
Banco de datos:
MEDLINE
Asunto principal:
Regulación Neoplásica de la Expresión Génica
/
Neoplasias
Tipo de estudio:
Guideline
/
Prognostic_studies
/
Screening_studies
Límite:
Humans
Idioma:
En
Revista:
BMC Bioinformatics
Asunto de la revista:
INFORMATICA MEDICA
Año:
2022
Tipo del documento:
Article
País de afiliación:
Taiwán