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

基于蛋白质组学的机器学习分析表明 PYGB 是一种新型免疫组织化学生物标志物,可用于区分倒置性尿路上皮乳头状瘤与倒置生长的低级别乳头状尿路上皮癌

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作者:Minsun Jung, Cheol Lee, Dohyun Han, Kwangsoo Kim, Sunah Yang, Ilias P Nikas, Kyung Chul Moon, Hyeyoon Kim, Min Ji Song, Bohyun Kim, Hyebin Lee, Han Suk Ryu

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.

Conclusion

In conclusion, we suggest PYGB as a promising immunohistochemical marker for IUP diagnosis in routine practice.

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.

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