AUTHOR=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 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 JOURNAL=Frontiers in Oncology VOLUME=12 YEAR=2022 URL=https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2022.841398 DOI=10.3389/fonc.2022.841398 ISSN=2234-943X 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.