Breast cancer is a leading cause of cancer-related deaths globally, underscoring the urgent need for improved diagnostic and therapeutic strategies. While advancements in targeted therapy and immunotherapy offer hope, response rates vary significantly. Identifying predictive biomarkers is crucial for personalized medicine. Chemokines, small secreted proteins, play a key role in cell migration, survival, and proliferation, influencing tumor growth and metastasis. CXCL12, a CXC chemokine initially identified as pre-B cell growth factor (PBGF) and later as stromal cell-derived factor-1 (SDF-1), is a homeostatic chemokine primarily involved in processes like embryogenesis and lymphopoiesis. However, it also exhibits inflammatory functions under certain conditions. CXCL12 interacts with its receptors, CXCR4 and ACKR1/3, and glycosaminoglycans (GAGs) to exert its effects. Preclinical studies suggest a link between high CXCL12 expression and improved survival in breast cancer, possibly due to reduced metastasis. However, the complex role of CXCL12 and its related biomarkers in breast cancer diagnosis and treatment requires further investigation. This study aimed to comprehensively analyze the role of CXCL12 in breast cancer, identify novel biomarkers, and develop a powerful prognostic model for improved patient management.

The literature review section comprehensively covers the existing knowledge on chemokines, particularly CXCL12, and their roles in various biological processes, including cancer. It highlights CXCL12's dual nature as both a homeostatic and inflammatory chemokine, its interaction with CXCR4 and other receptors, and its influence on cell migration, survival, and proliferation. Existing research on CXCL12's involvement in breast cancer, including its association with survival outcomes, is also discussed. The review sets the stage for the current study by emphasizing the need for a more in-depth investigation into the complex role of CXCL12 and its related biomarkers in breast cancer.

This study employed a multi-omics approach, integrating various datasets and techniques to comprehensively investigate the role of CXCL12 in breast cancer. Public datasets from TCGA and GEO databases were utilized for RNA sequencing data and clinical information on breast cancer samples. Somatic mutation data from GDC was used to calculate Tumor Mutation Burden (TMB). Immunohistochemistry (IHC) analysis was performed on a tissue microarray (TMA) containing 157 breast cancer specimens to assess CXCL12 protein expression levels. Weighted Gene Co-expression Network Analysis (WGCNA) identified genes most correlated with CXCL12 expression. Univariate Cox and LASSO regression analyses were used to select prognostic genes, and a CXCL12-related prognostic signature was constructed and validated using multiple independent datasets. Gene set enrichment analysis (GSEA), gene set variation analysis (GSVA), and immune cell infiltration analysis using TIMER2.0 and CIBERSORT were performed to compare high- and low-risk groups. Single-cell RNA sequencing (scRNA-seq) and paired bulk RNA-seq data (GSE176078) were used to study tumor immune microenvironment heterogeneity. Combined single-nucleus RNA-seq (snRNA-seq) and spatial transcriptomics analyses were conducted to determine CXCL12 expression distribution in the breast cancer microenvironment. Finally, drug sensitivity analysis using oncoPredict and immunotherapy response prediction were performed, followed by the construction of a nomogram to predict overall survival.

The study revealed that CXCL12 mRNA levels were significantly lower in breast cancer tissues compared to normal tissues. High CXCL12 expression was linked to improved overall survival (OS), recurrence-free survival (RFS), and disease-free survival (DFS) in multiple cohorts. WGCNA identified 402 genes significantly correlated with CXCL12, and LASSO regression selected 11 genes to create a prognostic signature. This signature effectively predicted patient outcomes in both training and validation cohorts, with high-risk patients exhibiting poorer prognosis. High-risk patients showed enrichment in myogenesis, TNFα signaling, and estrogen response pathways, while low-risk patients showed enrichment in E2F targets, G2M checkpoint, and MTORC1 signaling. High-risk patients also exhibited increased Tumor Mutation Burden (TMB) and a higher proportion of M2-like macrophages, which are immunosuppressive, compared to low-risk patients. Single-cell analysis corroborated these findings, showing increased M2-like macrophage populations in high-risk tumors. Drug sensitivity analysis revealed that high-risk patients were more sensitive to certain drugs (BMS-536924, foretinib, PRT062607), while low-risk patients were more sensitive to others (Leflunomide). A nomogram incorporating the risk score, age, tumor stage, and metastasis stage improved the prediction of 1-, 3-, and 5-year OS.

The findings of this study provide a comprehensive understanding of CXCL12's role in breast cancer, demonstrating its prognostic significance and immunological implications. The development and validation of a robust prognostic signature based on CXCL12-related genes offer a novel approach for risk stratification and personalized treatment strategies. The observed differences in immune cell infiltration (particularly M2-like macrophages) and drug sensitivity between high- and low-risk groups highlight the importance of considering the tumor microenvironment and individual patient characteristics when designing treatment plans. The study supports the potential use of CXCL12-related biomarkers in guiding the selection of appropriate therapeutic agents, including immunotherapy. The combined use of TMB and the risk score further improves predictive power, providing a more comprehensive approach to patient management.

This study provides a comprehensive analysis of CXCL12's role in breast cancer, establishing a novel prognostic signature that accurately predicts patient outcomes. The findings reveal significant differences in immune cell infiltration and drug sensitivities between high- and low-risk groups, emphasizing the need for a personalized approach to treatment. Future research should focus on validating these findings in larger prospective studies and investigating the underlying mechanisms driving the observed associations to optimize therapeutic strategies based on CXCL12-related biomarkers.

The study's retrospective nature and reliance on publicly available datasets introduce potential biases. The findings need validation in larger, prospective, multicenter studies with longer follow-up periods. The precise mechanisms by which CXCL12 influences breast cancer progression require further in vivo and in vitro investigations. The relatively limited number of patients in certain subgroups might also affect the interpretation of some results.