Xinan Wang ^1,2,3,4 Li Lin ⁵ Xue Zhang ^6* Minghui Zhang ^1,2,3,4* Zhuo Sun ^1,2,3,4* Yichen Yang ^1,2,3,4 Xiuna Zhang ^7* Yonghui Yuan ^8* Yong Zhang ^9* Hao Chen ^10* Ti Wen ^1,2,3,4*

• ¹ Department of Medical Oncology, The First Affiliated Hospital of China Medical University, Shenyang, Liaoning Province, China
• ² Key Laboratory of Anticancer Drugs and Biotherapy of Liaoning Province, First Hospital of China Medical University, Shenyang, Liaoning Province, China
• ³ Key Laboratory of Precision Diagnosis and Treatment of Gastrointestinal Tumors, Ministry of Education, The First Affiliated Hospital of China Medical University, Shenyang, Liaoning Province, China
• ⁴ Clinical Cancer Treatment and Research Center of Shenyang, The First Hospital of China Medical University, Shenyang 110001, Liaoning, China., Shenyang, China
• ⁵ Department of Pulmonary and Critical Care Medicine, The First Affiliated Hospital of China Medical University, Shenyang, Liaoning Province, China
• ⁶ Department of Gastroenterology, The First Hospital of China Medical University, Shenyang, Liaoning Province, China
• ⁷ Department of Medical Oncology, Second People's Hospital of Huludao, Huludao 125060, Liaoning, China, Huludao, China
• ⁸ Cancer Hospital of Dalian University of Technology (Liaoning Cancer Hospital & Institute), Shenyang 110042, Liaoning, China., Shenyang, China
• ⁹ Department of Pathology, Cancer Hospital of Dalian University of Technology (Liaoning Cancer Hospital & Institute), Shenyang, Liaoning, 110042, China，, Shenyang, China
• ¹⁰ Department of Breast Surgery, The First Affiliated Hospital of China Medical University, Shenyang, Liaoning Province, China

Abstract Triple-negative breast cancer (TNBC) is the most aggressive subtype of breast cancer, with the worst prognosis among all subtypes. The impact of distinct cell subpopulations within the tumor microenvironment (TME) on TNBC patient prognosis has yet to be clarified. In this study, utilizing single-cell RNA sequencing (scRNA-seq) integrated with bulk RNA sequencing (bulk RNA-seq), we applied Cox regression models to compute hazard ratios, combined with cross-validated prognostic scoring using a GLMNET-based Cox model. Our findings revealed that the function of immune cells is more pivotal in prognosis, with tumor-associated macrophages (TAMs) showing the strongest correlation with TNBC patient outcomes, compared with other immune cells. Additionally, we identified CPVL and MSR1 as critical prognostic genes within TAMs, with CPVL expression positively correlated with favorable outcomes and MSR1 expression associated with poorer prognosis. Mechanistically, CPVL may contribute to favorable prognosis by inhibiting the SPP1-CD44 ligand-receptor and promoting CXCL9-CXCR3, C3-C3AR1 ligand-receptor, through which TAMs interact with other cells such as monocytes, neutrophils, and T cells. Moreover, cytokines including IL-18, IFNγR1, CCL20, and CCL2, along with complement-related gene like TREM2 and complement component CFD, may participate in the process of CPVL or MSR1 regulating macrophage polarization. These results suggest that CPVL and MSR1 could serve as potential biomarkers for macrophage-mediated regulation of TNBC prognosis. Furthermore, RT-PCR experiments confirmed that CPVL is positively associated with M1-like TAM polarization, while MSR1 is linked to M2-like TAM polarization. Finally, the prognostic significance of these two genes is also validated in HER2-positive breast cancer subtypes. Collectively, our results underscore the pivotal role of TAMs, particularly through CPVL and MSR1, in modulating TNBC prognosis.