Single-cell combined with bulk-RNA data reveal a pattern related to angiogenesis in breast cancer patients: Individualized medicine.

Abstract

Angiogenesis contributes to tumor progression, aggressive behavior, and metastasis. Although several endothelial dysfunction genes (angiogenesis-related genes [ARGs]) have been identified as diagnostic biomarkers of breast cancer in a few studies, the mixed effects of ARGs have not been thoroughly investigated. The RNA sequencing data and patient survival datasets of breast cancer were obtained for further analysis. MSigDB website includes angiogenesis-related mechanisms. The consensus clustering analysis identifies 1082 breast cancer patients as three clusters. differential expression genes (DEGs) were identified by limma package. GO combined with gene set enrichment analysis (GSEA) to identify cytogenetic functions between two predefined clusters. Then Serpin Family F Member 1 (SERPINF1), angiomotin (AMOT), promyelocytic leukemia (PML), and BTG anti-proliferation factor 1 (BTG) were selected to construct prediction models using random forest survival analysis. External validation was performed using the GSE58812 triple-negative breast cancer cohort as the validation set. The median scoring system was used to discern the high- and low-risk groups, and there was a significant difference in their diagnostic results. Immunological infiltration scores were calculated using single sample gene set enrichment analysis (ssGSEA) and xCell algorithms, and consciousness scores were calculated using the R package "oncoPredict" for drugs in the Genomics of Drug Sensitivity in Cancer (GDSC) database. In addition, the single-cell analysis of seven triple-negative breast cancers using scRNA-seq information from GSE118389 demonstrated the interpretation of SERPINF1, AMOT, PML, and BTG1. In conclusion, this investigation engineered ARG-centric disease paradigms that not only prognosticated prospective therapeutic compounds, but also projected their mechanistic trajectories, thereby facilitating the proposition of tailored treatments within diverse patient cohorts diagnosed with breast cancer.