Development of a Novel Diagnostic Biomarker Set for Rheumatoid Arthritis Using a Proteomics Approach

Abstract

Background: Rheumatoid arthritis (RA) is an autoimmune disease that starts with inflammation of the synovial membrane. Studies have been conducted to develop methods for efficient diagnosis of RA and to identify the mechanisms underlying RA development. Blood samples can be useful for detecting disturbance of homeostasis in patients with RA. Nanoliquid chromatography-tandem mass spectrometry (LC-MS/MS) is an efficient proteomics approach to analyze blood sample and quantify serum proteins.

Methods: Serum samples of 18 healthy controls and 18 patients with RA were analyzed by LC-MS/MS. Selected candidate biomarkers were validated by enzyme-linked immunosorbent assay (ELISA) using sera from 43 healthy controls and 44 patients with RA.

Results: Thirty-eight proteins were significantly differentially expressed by more than 2-fold in healthy controls and patients with RA. Based on a literature survey, we selected six candidate RA biomarkers. ELISA was used to evaluate whether these proteins effectively allow distinguishing patients with RA from healthy controls and monitoring drug efficacy. SAA4, gelsolin, and vitamin D-binding protein were validated as potential biomarkers of RA for screening and drug efficacy monitoring of RA.

Conclusions: We identified a panel of three biomarkers for RA which has potential for application in RA diagnosis and drug efficacy monitoring. Further, our findings will aid in understanding the pathogenesis of RA.

Figure 1

Identification of proteins by LC-MS/MS in sera of healthy controls and patients with RA. (a) Total numbers of peptides and proteins (at least 2 peptides) identified in the two groups. (b) Venn diagram of proteins identified in the two groups. Protein functions of identified proteins in (c) healthy controls and (d) patients with RA using GeneGo MetaCore software.

Figure 2

DEPs with statistical significance and cluster analysis of the proteins. (a) Volcano plot of >2-fold DEPs that are filtered by p value (p < 0.05). (b) Heat map visualizing the >2-fold DEPs.

Figure 3

Pathway maps, process networks, and GO processes of >2-fold DEPs in patients with RA compared to healthy controls. (a) Significant pathway maps associated with >2-fold up-regulated proteins in patients with RA. (b) Significant process networks associated with >2-fold up-regulated proteins in patients with RA. (c) Significant GO processes associated with >2-fold up-regulated proteins in patients with RA. (d) Significant pathway maps associated with >2-fold down-regulated proteins in patients with RA. (e) Significant process networks associated with >2-fold down-regulated proteins in patients with RA. (f) Significant GO processes associated with >2-fold down-regulated proteins in patients with RA.

Figure 4

Validation of 6 selected biomarker candidates by ELISA and ROC curves for multibiomarkers. (a–f) The 6 selected biomarkers candidates were measured in sera from healthy controls and RA patients. (g) ROC curve analysis for 3-biomarker set, including SAA4, gelsolin, and VDBP to evaluate the ability of the biomarkers to distinguish patients with RA from healthy controls. (h) AUCs. Plots indicate individual protein abundances in healthy controls and RA patients. Data are shown as mean ± SEM. ∗p < 0.05 (independent t-test). ROC curves for selected 6 biomarker candidates were generated to evaluate the ability of the biomarkers to distinguish patients with RA from healthy control.

Figure 5

Differentiation of disease activity during drug treatment. (a–f) The 6 selected candidate biomarkers were measured in patients in remission and in patients in nonremission. (g-i) ROC curve analysis for rheumatoid factor, anti-CCP, and 3-biomarker set, including SAA4, gelsolin, and VDBP to evaluate the ability of the biomarkers to distinguish patients with RA from healthy controls. Plots indicate individual protein abundances in patients. Data are shown as mean ± SEM. ∗p < 0.05 (independent t-test).