Exosome molecular screening and modeling of its relationship with gene probes based on big data analysis

As an important mediator of intercellular communication, this exosome plays a key role in tumor development.The purpose of this study is to screen exosome-related molecules in triple-negative breast cancer by bioinformatics methods, to explore their relationship with gene probes, and to construct a prognostic model.Methodologically, the gene expression profiles of 100 triple-negative breast cancer samples and 100 normal tissue samples were obtained from the TCGA database, combined with exosome signature-related genes from the ExoBCD database to screen for differentially expressed genes, and constructed a risk prediction model by LASSO regression and Cox regression analysis.The results showed that the prognostic model had high accuracy in the training set, with areas under the ROC curve of 0.8 at one year, 0.72 at three years, and 0.76 at five years.Univariate and multivariate Cox regression analyses demonstrated that the risk scores and the N stage could be used as independent prognostic indicators (P<0.001).In the external validation set, there was a significant difference in the overall survival of patients in the high and low risk groups.In this study, we successfully constructed a prognostic model for triple-negative breast cancer based on exosomal molecules, which provides new ideas for clinical risk assessment and individualized treatment.