Moreira Thayse Batista, Silvestrini Marina Malheiros Araújo, Gomes Ana Luiza de Freitas Magalhães, Rangel Kerstin Kapp, Costa Álvaro Percínio, Gomes Matheus Souza, do Amaral Laurence Rodrigues, Martins-Filho Olindo Assis, Salles Paulo Guilherme de Oliveira, Braga Letícia Conceição, Teixeira-Carvalho Andréa
Grupo Integrado de Pesquisas em Biomarcadores, Instituto René Rachou-Fiocruz, Belo Horizonte 30190-002, Brazil.
Laboratório de Pesquisa Translacional em Oncologia, Instituto de Ensino, Pesquisa e Inovação, Instituto Mário Penna, Belo Horizonte 30380-420, Brazil.
Biomedicines. 2025 Feb 27;13(3):587. doi: 10.3390/biomedicines13030587.
Breast cancer (BC) is a disease that affects about 2.2 million people worldwide. The prognosis and treatment of these patients depend on clinical and histopathologic staging, in which more aggressive cancers need a less conservative therapeutic approach. Previous studies showed that patients with BC have an increased frequency of systemic microvesicles (MVs) that are associated with invasion, progression, and metastasis, which can be used in liquid biopsy to predict the therapeutic response in individualized treatment. This study proposes the development of a minimally invasive BC diagnostic panel and follow-up biomarkers as a complementary method to screen patients. The quantification of circulating MVs in 48 healthy women and 100 BC patients who attended the Mário Penna Institute between 2019 and 2022 was performed by flow cytometry. In addition, the MVs of BC patients were analyzed before treatment and 6, 12, and 24 months post-treatment. Machine learning approaches were employed to determine the performance of MVs to identify BC and to propose BC classifier algorithms. Patients with BC had more neutrophil- and endothelial cell-derived MVs than controls before treatment. After treatment, all MV populations were decreased compared to pre-treatment, but leukocyte- and erythrocyte-derived MVs were increased at 12 months after treatment, before decreasing again at 24 months. Performance analyses and machine learning approaches pointed out that MVs from neutrophils and endothelial cells are the best candidates for BC diagnostic biomarkers. Neutrophil- and endothelial cell-derived MVs are putative candidates for BC biomarkers to be employed as screening tests for BC diagnosis.
乳腺癌（BC）是一种影响全球约220万人的疾病。这些患者的预后和治疗取决于临床和组织病理学分期，其中侵袭性更强的癌症需要采用不太保守的治疗方法。先前的研究表明，乳腺癌患者体内系统性微泡（MVs）的频率增加，这些微泡与侵袭、进展和转移相关，可用于液体活检以预测个体化治疗中的治疗反应。本研究提出开发一种微创乳腺癌诊断面板和随访生物标志物，作为筛查患者的补充方法。通过流式细胞术对2019年至2022年期间在马里奥·彭纳研究所就诊的48名健康女性和100名乳腺癌患者的循环微泡进行了定量分析。此外，对乳腺癌患者治疗前以及治疗后6个月、12个月和24个月的微泡进行了分析。采用机器学习方法来确定微泡在识别乳腺癌方面的性能，并提出乳腺癌分类算法。治疗前，乳腺癌患者的中性粒细胞和内皮细胞衍生的微泡比对照组更多。治疗后，与治疗前相比，所有微泡群体均减少，但白细胞和红细胞衍生的微泡在治疗后12个月增加，之后在24个月再次减少。性能分析和机器学习方法指出，中性粒细胞和内皮细胞来源的微泡是乳腺癌诊断生物标志物的最佳候选者。中性粒细胞和内皮细胞衍生的微泡是有望用作乳腺癌诊断筛查测试的生物标志物候选者。
