BackgroundIn the tumor microenvironment, tumor-associated macrophages (TAMs) interact with cancer cells and contribute to the progression of solid tumors. Nonetheless, the clinical signi cance of TAMs-related biomarkers in prostate cancer (PCa) is largely unexplored. The present study aimed to construct a macrophage-related signature (MRS) for predicting the prognosis of PCa patients based on macrophage marker genes and exploring its potential mechanisms.
MethodsSix cohorts containing 1056 PCa patients with RNA-Seq and follow-up data were enrolled in this study.Based on macrophage marker genes identi ed by single-cell RNA-sequencing (scRNA-seq) analysis, univariate analysis, least absolute shrinkage and selection operator (Lasso)-Cox regression, and machine learning procedure were performed to derive a consensus MRS. The receiver operating characteristic curve (ROC), concordance index, and decision curve analyses were used to con rm the predictive capacity.
ResultsThe predictive performance of MRS for recurrence-free survival (RFS) is stable and robust, and it outperforms traditional clinical variables. Furthermore, the high MRS patients presented abundant macrophage in ltration and high expression of immune checkpoint genes (CTLA4, HAVCR2, and CD86).The frequency of mutations was relatively high in high MRS group. However, the low MRS patients indicated a better response to immune checkpoint blockade (ICB) and leuprolide-based adjuvant chemotherapy. Notably, the abnormal ATF3 expression may be associated with docetaxel and cabazitaxel-resistant in the PCa cell lines.
ConclusionsIn this study, a novel MRS was rst developed and validated to accurately predict patients' RFS, assess immune characteristics, infer therapeutic bene ts, and provide an auxiliary tool for personalized therapies.