Identification and Validation of Novel CAF Markers in Breast Cancer

Breast cancer remains a major global health challenge with high incidence and mortality rates among women. Recent studies have highlighted the critical role of the tumor microenvironment, particularly cancer-associated fibroblasts (CAFs), in tumor progression. However, current understanding of CAFs heterogeneity and its implications for breast cancer diagnosis and treatment remains limited. This study aimed to identify novel CAFs marker genes and develop a diagnostic model to improve breast cancer diagnosis and therapeutic strategies. We employed various machine learning algorithms to identify feature genes associated with cancer-associated fibroblasts (CAFs). Based on these genes, we constructed a high-precision diagnostic model for breast cancer. Furthermore, through single-cell analysis, we delved into the heterogeneity of CAFs and predicted the sensitivity of different CAF subsets to specific drugs. To validate the expression of these characteristic genes, immunohistochemical experiments were also conducted. This study identified FXYD1, SULF1, and TNXB as novel biomarkers for cancer-associated fibroblasts (CAFs) in breast cancer based on machine learning. Among these evaluated algorithms, the Random Forest algorithm distinctly stood out as the best due to its robust classification accuracy and stability. Single-cell analysis provided insights into the heterogeneity of CAFs between Luminal and non-Luminal breast cancer, thereby enhancing our understanding of the tumor microenvironment. Drug sensitivity predictions indicated that distinct CAF subsets responded differently to specific drugs, laying a solid foundation for the development of personalized breast cancer treatment strategies. Through immunohistochemistry (IHC), the expression patterns of these three biomarkers were verified: FXYD1 was expressed in myoepithelial and fibroblasts in normal breast tissue but was significantly absent in breast cancer; SULF1 was upregulated in fibroblasts of breast cancer; while the expression of TNXB did not exhibit notable variations between normal and cancerous tissues. These findings not only highlight the crucial roles played by FXYD1, SULF1, and TNXB in the development of breast cancer, but also uncover the heterogeneity CAFs. Consequently, our research provides a fresh perspective and a solid theoretical basis for advancing both early and precise diagnostic methods, as well as tailored therapeutic strategies.