ArticleFrontiers in oncology2022
Proteomic-Based Machine Learning Analysis Reveals PYGB as a Novel Immunohistochemical Biomarker to Distinguish Inverted Urothelial Papilloma From Low-Grade Papillary Urothelial Carcinoma With Inverted Growth.
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, 1 of them a synthesis that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
5 citing papers in PubMed, 1 synthesis or guideline pooled it, 8 citations in OpenAlex.
- Brain-Type Glycogen Phosphorylase (PYGB) in the Pathologies of Diseases: A Systematic Review.Cells · 2024Pooled it
- Inverted Urothelial Papilloma in a Young Woman: A Case Report and Review of Diagnostic Advances.Cureus · 2025Article
- Integrated Proteomics and Machine Learning Approach Reveals PYCR1 as a Novel Biomarker to Predict Prognosis of Sinonasal Squamous Cell Carcinoma.International journal of molecular sciences · 2024Article
- Quantitative proteomics and immunohistochemistry uncover NT5DC2 as a diagnostic biomarker for papillary urothelial carcinoma.Heliyon · 2024Article
- The proteomic landscape shows oncologic relevance in cystitis glandularis.Journal of pathology and translational medicine · 2023Article
Corrections and comments
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Authors and funding
12 authors at 6 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: The molecular biology of inverted urothelial papilloma (IUP) as a precursor disease of urothelial carcinoma is poorly understood. Furthermore, the overlapping histology between IUP and papillary urothelial carcinoma (PUC) with inverted growth is a diagnostic pitfall leading to frequent misdiagnoses. Methods: To identify the oncologic significance of IUP and discover a novel biomarker for its diagnosis, we employed mass spectrometry-based proteomic analysis of IUP, PUC, and normal urothelium (NU). Machine learning analysis shortlisted candidate proteins, while subsequent immunohistochemical validation was performed in an independent sample cohort. Results: From the overall proteomic landscape, we found divergent 'NU-like' (low-risk) and 'PUC-like' (high-risk) signatures in IUP. The latter were characterized by altered metabolism, biosynthesis, and cell-cell interaction functions, indicating oncologic significance. Further machine learning-based analysis revealed SERPINH1, PKP2, and PYGB as potential diagnostic biomarkers discriminating IUP from PUC. The immunohistochemical validation confirmed PYGB as a specific biomarker to distinguish between IUP and PUC with inverted growth. Conclusion: In conclusion, we suggest PYGB as a promising immunohistochemical marker for IUP diagnosis in routine practice.
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