Integrating molecular interactions and gene expression to identify biomarkers to predict response to tumor necrosis factor inhibitor therapies in rheumatoid arthritis patients.
Technol Health Care
; 30(S1): 451-457, 2022.
Article
em En
| MEDLINE
| ID: mdl-35124619
ABSTRACT
BACKGROUND:
Targeted therapy using anti-TNF (tumor necrosis factor) is the first option for patients with rheumatoid arthritis (RA). Anti-TNF therapy, however, does not lead to meaningful clinical improvement in many RA patients. To predict which patients will not benefit from anti-TNF therapy, clinical tests should be performed prior to treatment beginning.OBJECTIVE:
Although various efforts have been made to identify biomarkers and pathways that may be helpful to predict the response to anti-TNF treatment, gaps remain in clinical use due to the low predictive power of the selected biomarkers.METHODS:
In this paper, we used a network-based computational method to identify the select the predictive biomarkers to guide the treatment of RA patients.RESULTS:
We select 69 genes from peripheral blood expression data from 46 subjects using a sparse network-based method. The result shows that the selected 69 genes might influence biological processes and molecular functions related to the treatment.CONCLUSIONS:
Our approach advances the predictive power of anti-TNF therapy response and provides new genetic markers and pathways that may influence the treatment.Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Artrite Reumatoide
/
Antirreumáticos
Tipo de estudo:
Prognostic_studies
/
Risk_factors_studies
Limite:
Humans
Idioma:
En
Revista:
Technol Health Care
Assunto da revista:
ENGENHARIA BIOMEDICA
/
SERVICOS DE SAUDE
Ano de publicação:
2022
Tipo de documento:
Article
País de afiliação:
China