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Integrating molecular interactions and gene expression to identify biomarkers to predict response to tumor necrosis factor inhibitor therapies in rheumatoid arthritis patients1
Author(s) -
Minfan He,
Haihui Huang,
Yong Liang
Publication year - 2022
Publication title -
technology and health care
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.281
H-Index - 44
eISSN - 1878-7401
pISSN - 0928-7329
DOI - 10.3233/thc-thc228041
Subject(s) - rheumatoid arthritis , tumor necrosis factor alpha , medicine , biomarker , molecular biomarkers , arthritis , bioinformatics , oncology , immunology , biology , biochemistry
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.

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