Breast cancer, as one of the most common
malignancies in women, exhibits complex and heterogeneous pathological characteristics across different subtypes.
Triple-negative breast cancer (TNBC) and HER2-positive
breast cancer are two common and highly invasive subtypes within
breast cancer. The stability of the breast microbiota is closely intertwined with the immune environment, and
immunotherapy is a common approach for treating
breast cancer.Tertiary lymphoid structures (TLSs), recently discovered immune cell aggregates surrounding
breast cancer, resemble secondary lymphoid organs (SLOs) and are associated with the prognosis and survival of some
breast cancer patients, offering new avenues for
immunotherapy. Machine learning, as a form of artificial intelligence, has increasingly been used for detecting
biomarkers and constructing
tumor prognosis models. This article systematically reviews the latest research progress on TLSs in
breast cancer and the application of machine learning in the detection of TLSs and the study of
breast cancer prognosis. The insights provided contribute valuable perspectives for further exploring the biological differences among different subtypes of
breast cancer and formulating personalized treatment strategies.