Development and validation of a TLS-associated signature for prognosis prediction in breast cancer: new insights into QPRT

BackgroundTertiary lymphoid structures (TLSs) are associated with superior prognosis in breast cancer (BC). TLSs serve as key niches of anti-tumor adaptive immune responses across various malignancies.

However, the tumor-intrinsic factors that are associated with TLSs have been largely overlooked in BC.MethodsWe integrated bulk and single-cell transcriptomic data to develop a TLS-related prognostic signature (TRPS) based on tumor-intrinsic TLS-related genes using machine learning algorithms. The predictive accuracy of the TRPS was validated through survival analysis, ROC curve evaluation, and the construction of a nomogram.

The relationship between TRPS and the tumor microenvironment was assessed using TCGA-BC and in-house single-cell RNA-seq data. Furthermore, the relationship between TRPS and genomic alterations, drug sensitivity, and functional enrichment were explored.

In vitro and in vivo functional assays were performed to investigate the role of QPRT, a key model gene, in the progression of BC. RNA sequencing, Western blotting, immunoprecipitation, immunofluorescence, immunohistochemistry, and flow cytometry were performed to elucidate the molecular mechanisms underlying the functions of QPRT.ResultsThe TRPS model exhibited robust prognostic performance, as validated across four independent cohorts.