Ultrasound Genomics Reveals a Signature for Predicting Breast Cancer Prognosis and Therapy Response.

Abstract

Background: Breast cancer (BC) in women is the most common malignancy worldwide, but there is still a lack of validated tools to accurately assess patient prognosis and response to available chemotherapy treatment regimens. Method: We collected ultrasound images and transcriptome data of BC from our breast center and public database. Key ultrasound features were then identified by using the support vector machine (SVM) algorithm and correlated with prognostic genes. Long-term survival-related genes were identified through differential expression analysis, and a prognostic evaluation model was established by using Cox regression. In addition, VPS28 from the model was identified as a promising biomarker for BC. Results: Using univariate logistic regression and SVM algorithms, we identified 12 ultrasound features significantly associated with chemotherapy response. Subsequent correlation and differential expression analyses linked 401 genes to these features, from which five key signature genes were derived using Lasso and multivariate Cox regression models. This signature not only facilitates the stratification of patients into risk-specific treatment pathways but also predicts their chemotherapy response, thus supporting personalized medicine in clinical settings. Notably, VPS28, in the signature, emerged as a significant biomarker, strongly associated with poor prognosis, greater tumor invasiveness, and differing expression across demographic groups. Conclusion: In this study, we use ultrasound genomics to reveal a signature that can provide an effective tool for prognostic assessment and predicting chemotherapy response in patients with BC.