ArticleFrontiers in oncology2022
Construction of a Nomogram Discriminating Malignancy-Associated Membranous Nephropathy From Idiopathic Membranous Nephropathy: A Retrospective Study.
Article in Frontiers in oncology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Screening potential diagnostic biomarkers for PLA2R‑associated idiopathic membranous nephropathy by WGCNA analysis and LASSO algorithm.Renal failure · 2025Article
- Construction and validation of a predictive model for malignant tumors in patients with membranous nephropathy.BMC nephrology · 2025Article
- Genetic association between membranous nephropathy and malignancies: a two-sample Mendelian randomisation study.International urology and nephrology · 2024Article
- Combination of the gut microbiota and clinical indicators as a potential index for differentiating idiopathic membranous nephropathy and minimal change disease.Renal failure · 2023Article
- Serological Biomarker-Based Machine Learning Models for Predicting the Relapse of Ulcerative Colitis.Journal of inflammation research · 2023Article
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Authors and funding
6 authors.
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Abstract
Background: Based on the etiology, membranous nephropathy (MN) can be categorized into idiopathic membranous nephropathy (IMN) and secondary membranous nephropathy. Malignancy-associated membranous nephropathy (MMN) is a common type of secondary MN. Its incidence is only second to that of lupus nephritis. As the treatment and prognosis of MMN differ significantly from those of other MNs, the identification of MMN is crucial for clinical practice. The purpose of this study was to develop a model that could efficiently discriminate MMN, to guide more precise selection of therapeutic strategies. Methods: A total of 385 with IMN and 62 patients with MMN, who were hospitalized at the First Affiliated Hospital of Zhengzhou University between January 2017 and December 2020 were included in this study. We constructed a discriminant model based on demographic information and laboratory parameters for distinguishing MMN and IMN. To avoid an increased false positivity rate resulting from the large difference in sample numbers between the two groups, we matched MMN and IMN in a 1:3 ratio according to gender. Regression analysis was subsequently performed and a discriminant model was constructed. The calibration ability and clinical utility of the model were assessed Results: We constructed a discriminant model based on age, CD4 Conclusion: In clinical practice, patients demonstrating higher scores after screening with this model should be carefully monitored for the presence of tumors in order to improve their outcome.
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