Artificial intelligence-based diagnostic model for identifying PTPRZ1-MET fusion in a clinically defined secondary glioblastoma cohort.

Authors

Jin-Hao Zhang, Ji Shi, Han-Xiao Zhou, Wen-Lu Tan, Ying Zhang, Tao Jiang, Zheng Zhao

Published in

Molecular biomedicine. Volume 7. Issue 1. Aug 04, 2026. Epub Aug 04, 2026.

Abstract

Glioblastoma (GBM) is an aggressive and highly lethal brain tumor. Secondary glioblastoma (sGBM), which arises through malignant progression from lower-grade diffuse glioma, represents a clinically and biologically distinct subset of GBM. In this disease context, the protein tyrosine phosphatase receptor type Z1-mesenchymal-epithelial transition factor (PTPRZ1-MET; ZM) fusion has emerged as a recurrent oncogenic driver associated with adverse clinical outcomes. In this study, we analyzed 159 patients with sGBM, including 15 ZM-positive and 144 ZM-negative cases, and confirmed that ZM-positive tumors were associated with significantly shorter overall and progression-free survival. Transcriptomic profiling identified 359 genes upregulated in ZM-positive tumors, with enrichment in cell-cycle regulation and mitotic spindle-related pathways. To explore surrogate biomarkers associated with ZM fusion status, we benchmarked eight machine-learning classifiers and retained XGBoost as the primary feature-prioritization model. MET, PCDHGA3, and FAM3C emerged as the most informative biomarkers, and the fixed three-gene panel showed stable discriminative performance across cross-validation, nested evaluation, feature-pool sensitivity analyses, repeated random seeds, and class-weighted modeling. Protein-level validation in an independent formalin-fixed, paraffin-embedded (FFPE) cohort using multiplex and conventional chromogenic immunohistochemistry supported the pathology-compatible detection of elevated MET, PCDHGA3, and FAM3C expression in ZM-positive tumors. Collectively, these findings support a robust three-gene molecular signature that may facilitate the identification and stratification of ZM fusion-positive sGBM and provide biological insights with potential translational relevance for precision glioma diagnosis.

PMID:
42547688
Bibliographic data and abstract were imported from PubMed on 04 Aug 2026.

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