Bioinformatic Identification and Experimental Validation of a Prognostic Transcriptional Signature Derived from...

Breast cancer (BRCA) possesses prominent molecular heterogeneity, where aberrant expression of genes annotated to asparagine metabolism networks drives malignant progression and therapeutic resistance. However, systematic construction of prognostic signatures from a holistic asparagine metabolic pathway perspective remains scarce, limiting the clinical translation of metabolic insights into prognostic tools. We integrated TCGA and GEO BRCA transcriptomic datasets to screen asparagine metabolism-related differentially expressed genes and build a prognostic model. Six biomarkers, SLC35A2, SRD5A2, NT5E, CEL, IFNG and CNR1, were selected via univariate Cox, LASSO and multivariate Cox regression. SRD5A2 and IFNG were enriched in low-risk patients, while the other four genes were upregulated in high-risk subgroups. This signature reliably stratifies patient prognosis, with risk scores correlating strongly with pathway activity, immune infiltration, immune checkpoints, mutation landscapes and drug responsiveness. Bioinformatic results were validated via TCGA cohort analysis, in vitro cellular assays and Western blot. Two in vivo models were established: 4T1 xenografts in 6-week-old BALB/c mice and DMBA/hormone-induced spontaneous breast tumors in 8-week-old SD rats. Tumors were generated by cell injection or DMBA gavage plus cyclic hormone treatment, and tissue sections were processed for immunohistochemistry. Consistent differential expression of the six core genes was validated across all in vitro and in vivo systems. In conclusion, this asparagine metabolism-associated signature offers candidate biomarkers for personalized prognosis and provides preclinical evidence for metabolism-targeted BRCA therapy.