为了对乳腺癌的微环境有更深入的了解，我们利用GeoMx数字空间分析（DSP）技术，分析了65例未经治疗的乳腺癌组织样本中的107个感兴趣区域的转录本。我们的研究揭示了肿瘤细胞富集区域、免疫细胞富集区域和正常上皮区域的标记基因表达的空间异质性。在肿瘤细胞富集区域评估了55个预后标记物，在免疫细胞富集区域评估了15个预后标记物，发现肿瘤细胞富集区域中滤泡性辅助T细胞、静息树突状细胞和浆细胞的水平高于免疫细胞富集区域，而静息CD4记忆T细胞和调节性（Treg）T细胞的水平较低。此外，我们还对这些区域的HLA基因家族、免疫检查点和代谢基因进行了异质性分析。通过单变量Cox分析，我们鉴定出了5个与预后相关的代谢基因。此外，我们还进行了包括EMILIN2、SURF4和LYPLA1的免疫染色实验证实了我们的发现。我们对乳腺癌肿瘤环境的空间异质性的研究发现了特异性的诊断和预后标记物。 © 2023. BioMed Central Ltd., part of Springer Nature.

To gain deeper insights into the microenvironment of breast cancer, we utilized GeoMx Digital Spatial Profiling (DSP) technology to analyze transcripts from 107 regions of interest in 65 untreated breast cancer tissue samples. Our study revealed spatial heterogeneity in the expression of marker genes in tumor cell enriched, immune cell enriched, and normal epithelial areas. We evaluated a total of 55 prognostic markers in tumor cell enriched regions and 15 in immune cell enriched regions, identifying that tumor cell enriched regions had higher levels of follicular helper T cells, resting dendritic cells, and plasma cells than immune cell enriched regions, while the levels of resting CD4 memory in T cells and regulatory (Treg) T cells were lower. Additionally, we analyzed the heterogeneity of HLA gene families, immunological checkpoints, and metabolic genes in these areas. Through univariate Cox analysis, we identified 5 prognosis-related metabolic genes. Furthermore, we conducted immunostaining experiments, including EMILIN2, SURF4, and LYPLA1, to verify our findings. Our investigation into the spatial heterogeneity of the breast cancer tumor environment has led to the discovery of specific diagnostic and prognostic markers in breast cancer.© 2023. BioMed Central Ltd., part of Springer Nature.