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- Title
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.
- Authors
Jung, Minsun; Lee, Cheol; Han, Dohyun; Kim, Kwangsoo; Yang, Sunah; Nikas, Ilias P.; Moon, Kyung Chul; Kim, Hyeyoon; Song, Min Ji; Kim, Bohyun; Lee, Hyebin; Ryu, Han Suk
- 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.
- Subjects
TRANSITIONAL cell carcinoma; PAPILLOMA; PAPILLARY carcinoma; MACHINE learning; BIOMARKERS; MOLECULAR biology
- Publication
Frontiers in Oncology, 2022, Vol 12, p1
- ISSN
2234-943X
- Publication type
Article
- DOI
10.3389/fonc.2022.841398