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During the congress, E-Posters will be accessible to all participants on the congress website 24/7, as well as in the E-poster stations in the congress center.
Preparing your E-Poster
Please review the E-Poster format requirements carefully when preparing your E-Poster. Should your E-Poster not meet the mentioned requirements, it may not be displayed as described above.
E-Poster Submission Deadline
Please prepare and upload your E-Poster no later than March 14, 2026 11.59PM CET. After this date, you will no longer be able to prepare and upload your E-poster and it will not be displayed and accessible on the congress website.
Please follow the instructions below to input your abstract title.
Abstract titles should be brief and reflect the content of the abstract.
Primary membranous nephropathy (PMN) is one of the leading causes of nephrotic syndrome in adults, yet its reliable prognostic assessment tools remain limited. Although traditional clinical indicators and serum anti-PLA2R antibody have been widely used, they still cannot fully reflect the individualized treatment response of PMN patients. Identifying multifactorial risk profiles facilitates more precise therapeutic guidance. This study aims to identify novel urine protein prognostic biomarkers in PMN patients and uncover relapse-associated molecular subtypes in PMN.
We collected baseline midstream morning urine samples from 86 patients with biopsy-confirmed PMN and performed quantitative proteomic profiling using the nanoflow high-performance liquid chromatography–tandem mass spectrometry (nanoHPLC-MS/MS) analysis. The primary endpoints were treatment response status (complete/partial remission versus no remission) and time to remission. We constructed 101 machine learning model combinations using the R package "Mime1" to identify optimal algorithms. A prognostic risk score model was developed using random survival forest and stepwise Cox regression, with internal validation via bootstrap resampling. The combined model was deployed as a web-based interactive Shiny application. Finally, molecular subtypes were identified through non-negative matrix factorization (NMF) based on differentially expressed proteins.
During a median follow-up of 8 months (IQR: 3.4-18.5), 76.1% of patients achieved remission, with a median time to remission of 11.2 months (95%CI: 6.2-18.0). The four-protein risk model (PON1, ACTBL2, RDX, TPP1) was established (Harrell’s C-index=0.729), effectively stratifying patients into high-risk and low-risk groups. High-risk patients showed significantly worse outcomes. Compared to a clinical model incorporating only three features (serum anti-PLA2R antibody, age, and eGFR; Harrell's C-index = 0.636), the combined model integrating four urinary proteins with three clinical features showed superior predictive performance for clinical remission (Harrell's C-index = 0.744). To enhance clinical utility, we developed a web-based interactive Shiny application for the combined model (https://rsf0427models.shinyapps.io/PMN_Clinical_Predictor/). Furthermore, we observed significant heterogeneity among remitters and identified three molecular subtypes (PMN1, PMN2, PMN3) associated with relapse risk using molecular profiling. PMN2 emerged as an independent predictor of relapse (OR = 10.26, 95% CI: 1.68-81.97; P < 0.05).
This study established a prognostic model for PMN patients based on nanoHPLC-MS/MS analysis, providing a novel non-invasive approach to enhance the predictive capacity for treatment response. The combined risk score incorporating urinary proteomic biomarkers and clinical characteristics significantly improved prediction of clinical remission compared to clinical features alone. Furthermore, we identified three molecular subtypes associated with relapse risk, offering potential molecular stratification for recurrence risk assessment in PMN patients. External validation in multicenter cohorts is warranted.
