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Article

Interpretable Machine Learning for Proteomics-Based Subtyping and Tumor Mutational Burden Prediction in Endometrial Cancer.

Proteomics. Clinical applications - 1 Nov 2025

Luong Thi-My-Trang, Bui Xuan Lam, Tzeng Chii-Ruey, Le Nguyen Quoc Khanh

Abstract excerpt

BACKGROUND: Endometrial carcinoma (EC) represents a significant clinical challenge due to its pronounced molecular heterogeneity, directly influencing prognosis and therapeutic responses. Accurate classification of molecular subtypes (CNV-high, CNV-low, MSI-H, POLE) and precise tumor mutational burden (TMB) assessment is crucial for guiding personalized therapeutic interventions. Integrating proteomics data with...

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Source

Identifiers

PMID: 40916623
DOI: 10.1002/prca.70024

Genes

MAST4
MLH1
MTHFD2
MX2
PMS2
POLE
RPL22L1
SEC16A
STAT1

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 1 Sept 2026.

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