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Title: |
Study on Screening of Adverse Prognostic Indexes and Predictive Model of Rheumatoid Arthritis Based on Quantitative Proteomics | |||||||||||||||||||
| Authors: | Yong Peng, M.M., Yuhan Wu, M.M., Xianqian Huang, M.M., Keyue Zhang, M.M., Yao Chen, M.M., Yunge Zhang, M.M., and Yong Chen, M.M. | |||||||||||||||||||
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Objective: Screening and verifying biomarkers related to the prognosis of rheumatoid arthritis by establishing a prognostic model.
Study Design: Serum differential proteins in 21 rheumatoid arthritis (RA) patients and 21 healthy controls (HCs) were detected by tandem mass tag (TMT)-labeled quantitative proteomics, and 6 differential proteins were screened by bioinformatics analysis; 36 patients with RA were followed up for 6 months and divided into improved prognosis (IP) and poor prognosis (PP) groups based on disease activity and extrapulmonary manifestations after treatment, and into non-interstitial lung disease (NILD) and combined interstitial lung disease (CILD) groups based on high-resolution CT. In screening differential proteins related to interstitial lung disease, and establishment of a prognosis related model based on risk factors of poor prognosis in patients with rheumatoid arthritis, ROC curve was used to analyze the predicted value of the model, calibrate, and verify the model. Results: The expression levels of 6 differentially expressed proteins in the RA group were higher than those in the HC group before and after treatment. The expression levels of age, ESR, uric acid, white blood cell count, RAP1B, and actn1 protein in the IP group were lower than in the PP group. Age, DAS28, ESR, CRP, joint tenderness, and RAP1B expression levels of RA patients in the PP group after treatment were higher than those in the IP group. Disease activity and the expression level of RAP1B in the ILD group were higher than those in the NILD group. Multivariate logistic regression analysis showed that ESR and RAP1B were independent risk factors for the PP group. Conclusion: RAP1B protein may be associated with the prognosis of patients with RA and can be used as a potential biomarker to evaluate the prognosis of RA, which has guiding value for treatment. |
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| Keywords: | biomarkers, prognostic model, proteomics, rheumatic diseases, rheumatoid arthritis | |||||||||||||||||||
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