Abstract：
BACKGROUND: Vasculogenic mimicry (VM) refers to the direct formation of microcirculatory ducts by invasive malignant tumors via cellular phenotypic transformation. However, there is a lack of VM-based biomarkers for breast cancer.
METHODS: We obtained transcriptomic expression data, single cell sequencing data, and clinical data of patients from The Cancer Genome Atlas Program (TCGA) database and GEO database, performed single cell analysis to obtain specific type annotations of breast cancer cells and analyzed their spatial expression analysis. Kyoto Encyclopedia of Genes and Genomes (KEGG) and gene ontology (GO) analyses as well as Gene Set Enrichment Analysis (GSEA) analyses were performed to clarify the biological pathways and tumor functional enrichment relationships of the major expressed genes of VM in the breast cancer bulk data specimens. VM biomarkers were constructed. Meanwhile, the relationship between VM scores and tumor immune infiltration in breast cancer was analyzed using MCPcounter and ssGSEA methods. In addition, we assessed the specific relationship between NDRG1, a key VM gene in breast cancer, and tumor colonization, adhesion and invasion by biological experiments in breast cancer cell lines.
RESULTS: The main cell types of breast cancer (BRCA) samples were annotated by single cell transcriptome analysis. Most of the VM-high group was present in epithelial cells, whereas the VM-low group was present in immune and stromal cells. Multiple tumor pathways such as TGFβ p53 and MAPK were closely associated with VM-mediated breast cancer infiltration and invasion. A prognostic model of breast cancer based on VM key genes was constituted. Prognostic stratification of breast cancer was successfully achieved for the TCGA-BRCA and GSE58812 datasets. Through immune infiltration analysis, we found that differential expression of VM markers was associated with multiple immune cell regulation. In MDA-MB-231 and MDA-MB-453 cell lines, we found that the NDRG1 gene significantly promoted colony formation of breast cancer cells.
CONCLUSIONS: Our constructed VM-related gene-based model of breast cancer biology holds promise for prognostic prediction and patient stratification of breast cancer. This may provide a potentially clinically valuable aid in promoting a deeper understanding of the biological regulation of VM in breast cancer and exploring the specific mechanisms of tumor angiogenesis and breast cancer development.
METHODS: We obtained transcriptomic expression data, single cell sequencing data, and clinical data of patients from The Cancer Genome Atlas Program (TCGA) database and GEO database, performed single cell analysis to obtain specific type annotations of breast cancer cells and analyzed their spatial expression analysis. Kyoto Encyclopedia of Genes and Genomes (KEGG) and gene ontology (GO) analyses as well as Gene Set Enrichment Analysis (GSEA) analyses were performed to clarify the biological pathways and tumor functional enrichment relationships of the major expressed genes of VM in the breast cancer bulk data specimens. VM biomarkers were constructed. Meanwhile, the relationship between VM scores and tumor immune infiltration in breast cancer was analyzed using MCPcounter and ssGSEA methods. In addition, we assessed the specific relationship between NDRG1, a key VM gene in breast cancer, and tumor colonization, adhesion and invasion by biological experiments in breast cancer cell lines.
RESULTS: The main cell types of breast cancer (BRCA) samples were annotated by single cell transcriptome analysis. Most of the VM-high group was present in epithelial cells, whereas the VM-low group was present in immune and stromal cells. Multiple tumor pathways such as TGFβ p53 and MAPK were closely associated with VM-mediated breast cancer infiltration and invasion. A prognostic model of breast cancer based on VM key genes was constituted. Prognostic stratification of breast cancer was successfully achieved for the TCGA-BRCA and GSE58812 datasets. Through immune infiltration analysis, we found that differential expression of VM markers was associated with multiple immune cell regulation. In MDA-MB-231 and MDA-MB-453 cell lines, we found that the NDRG1 gene significantly promoted colony formation of breast cancer cells.
CONCLUSIONS: Our constructed VM-related gene-based model of breast cancer biology holds promise for prognostic prediction and patient stratification of breast cancer. This may provide a potentially clinically valuable aid in promoting a deeper understanding of the biological regulation of VM in breast cancer and exploring the specific mechanisms of tumor angiogenesis and breast cancer development.
摘要：
背景：血管生成拟态（VM）是指侵袭性恶性肿瘤通过细胞表型转化直接形成微循环导管。然而,乳腺癌缺乏基于VM的生物标志物.
方法：我们获得了转录组表达数据，单细胞测序数据，以及来自癌症基因组图谱计划（TCGA）数据库和GEO数据库的患者临床数据，进行单细胞分析，获得乳腺癌细胞的特定类型注释，并对其空间表达分析进行分析。进行了京都基因和基因组百科全书（KEGG）和基因本体论（GO）分析以及基因集富集分析（GSEA）分析，以阐明乳腺癌中VM主要表达基因的生物学途径和肿瘤功能富集关系。大量数据样本。构建VM生物标志物。同时,采用MCPcounter和ssGSEA方法分析VM评分与乳腺癌肿瘤免疫浸润的关系。此外,我们评估了NDRG1之间的特定关系，NDRG1是乳腺癌的关键VM基因，和肿瘤定植，通过生物学实验在乳腺癌细胞系中的粘附和侵袭。
结果：通过单细胞转录组分析注释了乳腺癌（BRCA）样品的主要细胞类型。大多数VM高组存在于上皮细胞中，而低VM组存在于免疫和基质细胞中。TGFβp53和MAPK等多种肿瘤通路与VM介导的乳腺癌浸润和侵袭密切相关。构建了基于VM关键基因的乳腺癌预后模型。TCGA-BRCA和GSE58812数据集成功实现了乳腺癌的预后分层。通过免疫浸润分析,我们发现VM标志物的差异表达与多种免疫细胞调节有关。在MDA-MB-231和MDA-MB-453细胞系中，我们发现NDRG1基因显著促进乳腺癌细胞集落形成。
结论：我们构建的基于VM相关基因的乳腺癌生物学模型为乳腺癌的预后预测和患者分层提供了前景。这可能为促进对乳腺癌中VM的生物学调控的更深入理解以及探索肿瘤血管生成和乳腺癌发展的特定机制提供潜在的临床价值帮助。
方法：我们获得了转录组表达数据，单细胞测序数据，以及来自癌症基因组图谱计划（TCGA）数据库和GEO数据库的患者临床数据，进行单细胞分析，获得乳腺癌细胞的特定类型注释，并对其空间表达分析进行分析。进行了京都基因和基因组百科全书（KEGG）和基因本体论（GO）分析以及基因集富集分析（GSEA）分析，以阐明乳腺癌中VM主要表达基因的生物学途径和肿瘤功能富集关系。大量数据样本。构建VM生物标志物。同时,采用MCPcounter和ssGSEA方法分析VM评分与乳腺癌肿瘤免疫浸润的关系。此外,我们评估了NDRG1之间的特定关系，NDRG1是乳腺癌的关键VM基因，和肿瘤定植，通过生物学实验在乳腺癌细胞系中的粘附和侵袭。
结果：通过单细胞转录组分析注释了乳腺癌（BRCA）样品的主要细胞类型。大多数VM高组存在于上皮细胞中，而低VM组存在于免疫和基质细胞中。TGFβp53和MAPK等多种肿瘤通路与VM介导的乳腺癌浸润和侵袭密切相关。构建了基于VM关键基因的乳腺癌预后模型。TCGA-BRCA和GSE58812数据集成功实现了乳腺癌的预后分层。通过免疫浸润分析,我们发现VM标志物的差异表达与多种免疫细胞调节有关。在MDA-MB-231和MDA-MB-453细胞系中，我们发现NDRG1基因显著促进乳腺癌细胞集落形成。
结论：我们构建的基于VM相关基因的乳腺癌生物学模型为乳腺癌的预后预测和患者分层提供了前景。这可能为促进对乳腺癌中VM的生物学调控的更深入理解以及探索肿瘤血管生成和乳腺癌发展的特定机制提供潜在的临床价值帮助。
