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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

Cited 5 time in Web of Science Cited 5 time in Scopus
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

Issue Date
2022-03
Publisher
Frontiers Media S.A.
Citation
Frontiers in Oncology, Vol.12, p. 841398
Abstract
BackgroundThe 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. MethodsTo 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. ResultsFrom 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. ConclusionIn conclusion, we suggest PYGB as a promising immunohistochemical marker for IUP diagnosis in routine practice.
ISSN
2234-943X
URI
https://hdl.handle.net/10371/183821
DOI
https://doi.org/10.3389/fonc.2022.841398
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