Bioinformatics for differential proteomics data between triple-negative breast cancer and luminal A subtype breast cancer

Background The heterogeneity between different subtypes of breast cancer has been established. Triple-negative breast cancer (TNBC) is the type of breast cancer with the highest recurrence rate, metastasis rate, and worst prognosis, while luminal A breast cancer is the subtype with the best prognosis. To study the heterogeneity of the two subtypes of breast cancer is important. Methods We adopted a quantitative proteomics detection method based on data-independent acquisition and screened all differentially proteins between TNBC and luminal A breast cancer. Ingenuity Pathways Analysis was used for analysis. Results We found that 207 proteins were up-regulated and 326 proteins were down-regulated. Classical pathway analysis showed that Eukaryotic translation initiation factor 2 (EIF2) signaling was significantly activated, in which EIF2α and other molecules were significantly up-regulated. The upstream regulatory analysis predicted 18 strong activators and 63 strong inhibitors, among which v-myc avian myelocytomatosis viral oncogene neuroblastoma derived homolog (MYCN) was the strongest activator and tretinoin was the strongest inhibitor. Based on disease and function analysis, it was found that differentially proteins were enriched in 41 biological processes, with RNA damage and repair and protein synthesis the most significant biological processes. There were 14 diseases or functions that were significantly activated and 35 that were significantly suppressed. Among the diseases or functions, the differentially proteins had the most significant inhibitory effect on osteosarcoma cell death. According to an analysis of the interaction network, 25 molecular interaction networks were found and the network with the highest score mainly affected the diseases and functions of cancer, protein synthesis, and RNA damage and repair. Conclusion We identified many key proteins with TNBC heterogeneous characteristics and differentially expression, which will provide an abundance of new information for screening TNBC therapeutic specific targets and biomarkers.