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

Background: Tertiary 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. Methods: We 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. Results: The TRPS model exhibited robust prognostic performance, as validated across four independent cohorts. High-TRPS scores were associated with diminished infiltration of B cells and T cells. Moreover, the high-TRPS group displayed an elevated level of tumor mutation burden and enrichment of tumor-promoting pathways. QPRT facilitated BC growth and metastasis. Mechanistically, QPRT-mediated NAD Conclusions: This study establishes a novel TLS-related prognostic model, and highlights the potential of QPRT as a promising therapeutic target in BC.