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Abstract: SA-PO934

Urinary Proteomics Identifying Novel Biomarkers for Predicting the Activity of ANCA-Associated Nephritis

Session Information

Category: Glomerular Diseases

  • 1402 Glomerular Diseases: Clinical, Outcomes, and Trials

Authors

  • Zhang, Shuo, Peking Union Medical College Hospital, Beijing, Beijing, China
  • Chen, Xin, Peking Union Medical College Hospital, Beijing, Beijing, China
  • Tang, Xiaoyue, Peking Union Medical College Hospital, Beijing, Beijing, China
  • Xu, Jiatong, Peking Union Medical College Hospital, Beijing, Beijing, China
  • Hu, Yuting, Peking Union Medical College Hospital, Beijing, Beijing, China
  • Qin, Yan, Peking Union Medical College Hospital, Beijing, Beijing, China
  • Liu, Peng, Peking Union Medical College Hospital, Beijing, Beijing, China
  • Chen, Limeng, Peking Union Medical College Hospital, Beijing, Beijing, China
Background

Despite the pathogenic role of ANCA in ANCA-associated glomerular nephritis (AAGN), it’s still challenging to determine the active stage. Urinary proteomics, specifically data-independent acquisition (DIA) proteomics, might be the non-invasive and efficient method to find potential biomarkers to identify the active AAGN.

Methods

The urine samples used for proteomic analysis were from patients diagnosed as AAV at Peking Union Medical College Hospital from May 2022 to April 2023. We performed LC-MS/MS analysis with quality control and identified differentially expressed genes (DEGs). Weighted gene co-expression network analysis (WCGNA), Least Absolute Shrinkage and Selection Operator (LASSO), and Random Forest analysis were used to figure out the target molecule and validated in the validation cohort by ELISA.

Results

In this study, 40 patients performed the urine DIA analysis, and the other 50 patients were in the validation group, with 56.7% female, with an average age of 58±15 years old. In the 358 DEGs identified between AAV with and without renal involvement, the top pathways were the complement, coagulation cascade, and cholesteryl mechanism. After WGCNA, Lasso, and Random forest analysis, four urine proteins, marked complement factor D (CFD), coagulation factor II (F2), fibrinogen Alpha Chain (FGA), and plasminogen (PLG) were highly associated with AAGN in the active stage and confirmed by ELISA (Figure 1). The receiver operating characteristic curve (ROC) values of F2 combined CFD in AAV patients with active renal involvement was 0.922 (P<0.001) with a sensitivity of 75% and a specificity of 97.1%.

Conclusion

Several biomarkers from the complement and coagulation cascade might be potential urine biomarkers for AAGN in the active stage.

Figure 1. ELSIA analysis of F2, CFD, FBG and PLG.

Funding

  • NIDDK Support