ArticleFrontiers in genetics2026
Identification of two genes associated with recurrence in Paget's disease and construction of a predictive model.
Article in Frontiers in genetics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Background: Mammary Paget's disease (MPD) and extramammary Paget's disease (EMPD) exhibit distinct clinical behaviors, yet the underlying molecular drivers of prognosis remain poorly characterized. This study aimed to identify key prognostic genes and construct a predictive model for Paget's disease (PD). Methods: RNA sequencing was performed on MPD and EMPD tissues. Hub genes were screened using weighted gene co-expression network analysis (WGCNA). Their prognostic value was validated via time dependent receiver operating characteristic (ROC) curves, Kaplan-Meier survival analysis, and Decision curve analysis (DCA) in internal and external cohorts. A risk-score model was subsequently developed based on the identified genes. Results: RNA-seq analysis revealed distinct functional profiles between MPD and EMPD, with recurrence in MPD associated with developmental and differentiation pathways, while EMPD recurrence was linked to immune and inflammatory processes. WGCNA identified KLF13 and TIA1 as hub genes. In the internal cohort, both genes were significantly overexpressed in patients with recurrence (KLF13: 22.74 ± 3.41 vs. 15.36 ± 4.91, P < 0.001; TIA1: 11.69 ± 2.48 vs. 7.74 ± 1.62, P < 0.001). And the KLF13 and TIA1 were also validated by qPCR in the internal cohort. The genes also demonstrated prognostic validity in an independent Chinese PD cohort. A risk-score model incorporating KLF13 and TIA1 effectively stratified patients into high- and low-risk groups with distinct outcomes in both internal and external validation sets. Moreover, we knocked down TIA1 expression in MDA-MB-231 cells, and both Conclusion: KLF13 and TIA1 are robust prognostic biomarkers in PD. The developed risk-score model provides a valuable tool for predicting recurrence and personalizing patient management. In addition, both
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