ArticleTranslational cancer research2026
Development and validation of a DYX1C1- and GNAI1-based nomogram for predicting relapse in HER2-positive breast cancer treated with trastuzumab.
Article in Translational cancer research, 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: Resistance to trastuzumab and subsequent relapse remain critical challenges in human epidermal growth factor receptor 2 (HER2)-positive breast cancer management. This study aimed to develop and validate a multivariable prediction model for relapse in HER2-positive breast cancer patients treated with trastuzumab and to explore the underlying biological mechanisms of the identified biomarkers. Methods: This retrospective study utilized public gene expression profiles from the Gene Expression Omnibus (GEO) database and a clinical cohort from the First Affiliated Hospital of Xi'an Jiaotong University. The analysis included a training cohort (GSE55348, n=51), an internal validation cohort (GSE58984, n=94), and an external validation cohort (I-SPY2/GSE181574, n=127). Relapse-associated hub genes were identified via differential expression analysis and weighted gene co-expression network analysis (WGCNA). Independent predictors were determined through multivariate Cox regression to construct a nomogram for predicting 3-year relapse-free survival (RFS). Model performance was evaluated using time-dependent receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA). Biological mechanisms were investigated using gene set enrichment analysis (GSEA), immune infiltration analysis, and single-cell RNA sequencing (scRNA-seq). Clinical relevance was further supported by immunohistochemistry (IHC) in a pilot cohort of 12 patients. Results: Conclusions: The
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